Patent Application
20200210056
Contained herein is material that is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction of the patent disclosure by any person as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all rights to the copyright whatsoever.
This disclosure is generally related to a client-server based visualization mapping techniques. More particularly, this disclosure is related to a web based graphical user interface to enable users to custom-design product configurations tailored to their unique application needs.
Client-server based graphical user interfaces can be configured to enable users to custom-design product configurations tailored to their unique application needs. A plot may be employed to define a design space for a variety of products to reduce development time and provide self-service formulation assistance.
A ternary plot, ternary graph, triangle plot, simplex plot, or Gibbs triangle is a barycentric plot on three variables which sum to a constant. It graphically depicts the ratios of the three variables as positions in an equilateral triangle. It is used in physical chemistry, petrology, mineralogy, metallurgy, and other physical sciences to show the compositions of systems composed of three species.
In a ternary plot, the proportions of the three variables a, b, and c must sum to some constant, K. Usually, this constant is represented as 1.0 or 100%. Because a+b+c=K for all substances being graphed, any one variable is not independent of the others, so only two variables must be known to find a sample's point on the graph: for instance, c must be equal to K−a−b. Because the three proportions cannot vary independently—there are only two degrees of freedom—it is possible to graph the combinations of all three variables in only two dimensions. Ternary plots can be used for materials with n>3 components. The ternary plot then represents the three components with each of the other n−3 components held at a fixed proportion.
Design of experiments techniques may be employed to design any task that aims to describe or explain the variation of information under conditions that are hypothesized to reflect the variation. In one form, an experiment aims at predicting the outcome by introducing a change of preconditions, which is reflected in a variable called the predictor (independent). The change in the predictor is generally hypothesized to result in a change in the second variable, hence called the outcome (dependent) variable. Experimental design involves not only the selection of suitable predictors and outcomes, but planning the delivery of the experiment under statistically optimal conditions, given the constraints of available resources.
In experimental design, the predictor may be chosen to reduce the risk of measurement error. The experimental design should achieve appropriate levels of statistical power and sensitivity.
In one aspect, the present disclosure provides a method of producing a graphical depiction of a predicted value of a property of a material. The method comprises generating, by a processing unit, a plot defining a geometric shape and comprising a plurality of points arranged in a matrix, each of the points defining a value for at least two variables and a predicted value of a property of the material; displaying, on an output device, a visual representation of the predicted value of the property of the material for at least some of the plurality of points in a range of indicia, wherein the range of indicia represents a range of predicted values of the property; and displaying, on the output device, a pointer on the visual representation.
In another aspect, the present disclosure provides a method of producing a graphical depiction of a predicted value of a property of a material. The method comprises generating, by a processing unit, a plot defining a triangle and comprising a plurality of points arranged in a matrix, each of the points defining a value for three variables and a predicted value of a property of the material; displaying, on an output device, a color heat map representation of the predicted value of the property of the material for at least some of the plurality of points in a range of colors, wherein the range of colors represents a range of predicted values of the property; and displaying, on the output device, a pointer on the heat map.
In another aspect, the present disclosure provides a method of producing a graphical depiction of a predicted value of a property of a material. The method comprises generating, by a processing unit, a plot defining a four-sided polygon and comprising a plurality of points arranged in a matrix, each of the points defining a value for at least two variables and a predicted value of the property of the material; displaying, on an output device, a color heat map representation of the predicted value of the property of the material for at least some of the plurality of points in a range of colors, wherein the range of colors represents a range of predicted values of the property; and displaying, on the output device, a pointer on the heat map.
In some aspects, a digital formulation service is provided for generating optimized material configurations, both in types of materials and cost. A computerized system may be configured to provide a digital formulation service module that allows a user to generate a custom material configuration based on a specified constraint, such as cost or performance. The digital formulation service may provide a recommended material configuration that satisfies the specified constraint. The digital formulation service module may be an augmented or supplemental service with the other user interfaces described herein.
In one aspect, the present disclosure is directed to a client-server based visualization mapping techniques that employs graphical user interfaces configured to enable users to custom-design product configurations tailored to their unique application needs. A plot may be employed to define a design space for a variety of products to reduce development time and provide self-service formulation assistance. The plot may be incorporated in a graphical user interface on a client that runs a web server in a cloud based system. Conventional techniques for determining a property of a material requires making the material based on known components and then determining the actual property of the material. If the measured property is not the desired property, a new material is formulated and the new resulting property is tested. This trial-and-error technique is time consuming, expensive, and may never lead to the desired material property due to the large number of combinations of components that can be combined to achieve a large number of material properties. It would be desirable to be able to precisely predict a material property for a large number of combinations of components and to provide immediate real-time feedback to a user of a predicted material property based on a particular combination of components. It also would be desirable to quickly update the ratios of components on a graphical user interface and provide immediate real-time feedback to the user of the new predicted material property. The disclosed client-server based visualization mapping techniques enable a user to design materials using known components, e.g., polymers, based on desired performance properties of the material that are of interest to the user. The disclosed client-server based visualization mapping techniques enable such designs by generating a plot defining a geometric shape and comprising a plurality of points arranged in a matrix, where each of the points define a value for at least two variables and a predicted value of a property of the material. The underlying plot is generated based on experimental data or data generated by computer models. A visual representation of the predicted value of the property of the material is displayed for at least some of the points in a range of indicia, where the range of indicia represents a range of predicted values of the property. A pointer positioned on the visual representation can be displayed on the output device to enable the user to visually perceive the material properties. The user may move or drag the pointer over the plot to dynamically update the material properties and dynamically updates the visual representation of the predicted value of the property.
Before describing various aspects of client-server based visualization mapping techniques, the disclosure turns briefly to a description of the design of experiment technique that may be used to build a database of data used to generate ternary maps to enable users to custom-design various products by manipulating the ratios of the three variables as positions in an equilateral triangle and providing a graphical depiction of the results on a screen or display of a computer, tablet, smartphone, or other web based client appliance. In one aspect, a statistical software application known under the trade name of Design-Expert from Stat-Ease Inc. may be employed to create and analyze a design of experiments to generate model equations that drive the ternary maps of a ternary map interface according to the present disclosure. Other statistical software applications for generating and analyzing a design of experiments include, for example, statistical software applications known under the trade name ECHIP, JMP, and Minitab.
It will be appreciated that there are many considerations when creating, executing, and analyzing a design of experiments. The methodology used to create the ternary map described herein provide an example of one way in which experimental data can be used to drive an interactive, graphical interface. In one aspect, computer generated data may be employed to drive the ternary map interface in accordance with the present disclosure. In other aspects, real measurement data may be employed to drive the ternary map interface. In yet another aspect, real measurement data may be employed to drive the ternary map interface and computer generated data may be employed to fill in any gaps in the real measurement data.
In one formulation generation example, a polyurethane coating, comprising an A and B side, is analyzed. The system is evaluated using a two-mixture design, with one mixture (Mixture 1) based on the relative amounts of three components and the other mixture (Mixture 2) based on the relative amounts of two components. A design of experiments formulation data set can be created using the DesignExpert software application. Upon specifying the design space and generating a set of formulations, the coatings are prepared and cured on appropriate test substrates. Each property is then measured and recorded in a Design-Expert data table. The formulation data set can be stored in a database.
Once the data has been accumulated, it can be analyzed to develop model equations. There are a variety of approaches to selecting the terms for the final model, for example, a threshold p-value can be chosen, an information criterion statistic can be minimized (such as the Corrected Aikake's Information Criterion or the Bayesian Information Criterion), or another statistic can be optimized, such as R-square adjusted or Mallow's Cp. Additionally, a validation set of points may be withheld from the model building process, with the final model chosen as the best fit (again, a variety of criteria can be used to determine best fit) of the validation set. These approaches can be performed in a stepwise approach with Forward selection, that is starting with a model with no terms and stepwise adding one at a time, Backward selection, starting with the full model and reducing terms one by one, or one that mixes Forward and Backward selection. The addition and reduction of terms is stopped when the chosen criteria is met. Commercially available statistical software packages support these, as well as other, approaches.
In one example, computer generated data may be employed as input to a model as an independent variable to generate dependent variables, e.g., responses. For each response, the significant model terms may be identified by starting with a full quadratic model and performing a backwards stepwise elimination with minimization of the Bayesian Information Criterion (BIC) as the stopping rule. Standard least squares regression can then be used to determine the coefficients of the significant model terms for the final model equation. The following process demonstrates at a high level the use of this approach for the first response, “Property 1,” in the Design-Expert software application.
Typical independent variables include amount of recipe components in weight or weight percent. Calculations derived from the recipe such as volume percent filler and total catalyst weight also are common. The derived quantities can be based on molar quantities as well such as moles of blowing agent gas per weight of reactive materials and the overall stoichiometric balance between reactive species. Other derived quantities can be based on chemical characteristics such as moles of benzene rings per weight of reactive material. Other calculated normalizations are valid as well, moles of tin (Sn) atoms per mole of reactive material in the recipe. These independent variables extend to processing variables, length of mixing time, cure time, cure temperature and reaction temperature to cite but a few. These independent variables can be controlled or uncontrolled. Barometric pressure and relative humidity are common uncontrolled variable examples. Any of these variables may be transformed, for example, a log or reciprocal transformation, before building and analyzing a designed set of experiments.
A “Property 1” response is selected under the analysis tree. An initial model is chosen and a response fit summary is selected. Model reduction may be done manually or using an automated method. If an auto-select model is selected, model selection criteria are entered into the automatic model selection window. Upon completion of the above process, the selected design of experiments model is accepted and the analysis of variance (ANOVA), a statistical method in which the variation in a set of observations is divided into distinct components, is selected. The application (such as the Design-Expert application) then performs an R-Squared analysis and provides the user an opportunity to review the R-Squared analysis, adjust the R-Squared, and predetermine the R-Squared values to ensure the values are within the range desired for the response being evaluated. The application (such as the Design-Expert application) calculates a variety of statistics to assess the fit of the selected model to the data, including, for example, R-Squared, Adjusted R-Squared, Predicted R-Squared, standard deviation, and PRESS (Predicted Residual Error Sum of Squares). In addition, the application provides a Diagnostics section, where the validity of the ANOVA assumptions can be evaluated, the data can be examined for outliers from the model and other such important model building concerns can be gauged. Finally, the model graphical depictions may be selected and the final equation in terms of real components may be evaluated. The final equation may be employed to populate a data table for the ternary map interface for all properties.
A model for generating predictive values of properties of materials includes, without limitation, design of experiments, regression analysis of a data set, an equation, machine learning, or artificial intelligence, and/or any combination thereof. In one aspect, the model used to generate the predicted values of the properties of a material for a ternary plot is generated from a design of experiment technique. In other aspects, models for generating predictive values of properties include a statistical analysis of unstructured data, such as that generated by a historian of a distributive control system of a chemical manufacturing plant. For example, models of the dependence of polydimethylsiloxane (PDMS) modified polyolefin (PMPO) viscosity on solids content and other variables that are reasonably accurate within small ranges may be generated from such unstructured data. In other aspects, artificial intelligence methods may be employed to mine a large number of experimental systems in a company's lab notebook system and research papers. In other aspects, an analytical model may be generated based on scientific first principles. For example, a graphical user interface (GUI) may be configured to display pressure at a given volume and temperature of mixtures of multiple gases, predicted by a non-ideal gas law, for example.
Various material properties are tabulated in Table 1 below. As described herein, graphical depictions of ternary and square maps, among others, can be used to design products having a particular material property, short or long, as described in Table 1. Properties include, without limitation, properties often associated with coatings, such as Soft Feel, 5 Finger Scratch Resistance, Diethyltoluamide (DEET) Solvent Resistance, Coefficient of Friction, and properties often associated with polyurethane foams, such as flexible polyurethane foams, such as Density, Indentation Force Deflection 25%, Indentation Force Deflection 40%, Indentation Force Deflection 65%, Tensile Strength, Elongation, Tear Strength, Maximum Temperature, Compression Strength 90%, Humid Age Compression Set 75%, Fatigue Loss, among others, for example.
Generally, in one aspect, the present disclosure provides a method of producing a graphical depiction of a predicted value of a property of a material. The method includes generating, by a processing unit, a plot defining a geometric shape and comprising a plurality of points arranged in a matrix, each of the points defining a value for at least two variables and a predicted value of a property of the material. The method includes displaying, on an output device, a visual representation of the predicted value of the property of the material for at least some of the plurality of points in a range of indicia, wherein the range of indicia represents a range of predicted values of the property. At least some of the plurality of points in a range of indicia means at least two of the plurality of points up to and including each of the plurality of points in a range of indicia, such as a majority of the plurality of points. The method further includes displaying, on the output device, a pointer on the visual representation. At least one of the at least two variables may be an independent variable. The visual representation may be a heat map, a color heat map, or a contour map. The material may be a foam, a coating, an adhesive, a sealant, an elastomer, a sheet, a film, a binder, or any organic polymer, for example.
In one aspect, the method includes displaying, on the output device, the value of the indicia and property of the material based on a position of a cursor on the visual representation. In one aspect, the method includes dynamically updating the location of the pointer and an element as the pointer is dragged over the visual representation. The element may include a numeric value or a descriptor of the property, for example. The element may include indicia within the range of indicia that represents the predicted value or the descriptor of the property in the visual representation, for example.
In one aspect, the geometric shape defines a closed shape in Euclidian space. The closed shape may define a polygon, for example. The polygon may be a triangle or a four-sided polygon, for example. In the case where the polygon is a triangle, each of the points may define a value for three variables, where each variable represents a value for an amount of a component in a composition, such as the relative amount of components in a composition to each other. The amounts may be expressed as a percentage and a sum of the amounts is 100%, for example. In the case where the polygon is a four-sided polygon, each of the points may define a value for two variables, where each variable is a value for an amount of a component in a composition, a value for a processing condition, or a value representing an amount of two components of the composition relative to each other. The closed shape may define an ellipse or a circle, for example. The closed shape may define either a two-dimensional space or a two-dimensional perspective projection of a three-dimensional shape, for example.
In another aspect, the method includes formulating, by the processing unit, a composition based on the visual representation of the predicted value of the property of the material for at least some of the plurality of points in the range of indicia. The composition may be formulated based on a plurality of properties for at least some of the plurality of points in the range of indicia, for example. The method may also include optimizing, by the processing unit, one or more than one property of the material within one or more than one defined range of indicia. A gridded region that represents one or more than one optimized region based on the one or more than one defined range of indicia may be displayed on the output device, for example.
In one aspect, the method includes updating, by the processing unit, a table with current values of the at least two variables and the predicted value of the property based on the location of the pointer on the visual representation. The method may also include generating, by the processing unit, a set of instructions for producing a product that exhibits the predicted value of the property of the material at one of the plurality of points in the range of indicia.
In one aspect, the method also includes generating, by the processing unit, a plurality of plots each defining a geometric shape and each including a plurality of points arranged in a matrix where each of the points defines a value for at least two variables and a predicted value of the property of the material for each of the plurality of plots. A visual representation of the predicted value of the property of the material for at least some of the plurality of points in a range of indicia may be displayed on the output device. The range of indicia may represent a range of predicted values of the property. A pointer may be displayed on each of the plurality of plots.
In one aspect, the method includes generating, by the processing unit, a plot based on a model. The model may be generated based on design of experiments, regression analysis of a data set, an equation, machine learning, or artificial intelligence, and/or any combination thereof.
In one aspect, the plot defines a triangle including a plurality of points arranged in a matrix where each of the points define a value for three variables and a predicted value of a property of the material. A color heat map representation of the predicted value of the property of the material for at least some of the plurality of points in a range of colors may be displayed on the output device. The range of colors may represent a range of predicted values of the property. A pointer may be displayed on the heat map.
In another aspect, the plot defines a four-sided polygon including a plurality of points arranged in a matrix where each of the points defines a value for at least two variables and a predicted value of the property of the material. A color heat map representation of the predicted value of the property of the material for at least some of the plurality of points in a range of colors may be displayed on the output device. The range of colors may represent a range of predicted values of the property. A pointer may be displayed on the heat map.
In one aspect, the present disclosure provides a web based ternary map graphical user interface (GUI) that runs in any HTML5 compliant browser. The web based ternary map GUI may be created using web visualization software. Accordingly, the web based ternary map GUI can be used on modern cell phones, tablets, and personal computers. The interface may be accessed published to the cloud and may be made available to users via a website.
The ternary map GUI is a user-friendly interface that may be made available for self-service 24 hours per day and 7 days per week. All calculations conducted by the ternary map GUI are performed “behind” the face of the engine to protect the data used to build the models and to prevent the user from accidentally causing damage to the functionality of the ternary map GUI, as would be the case with a spreadsheet solution. The ternary map GUI user interface allows users to interact with the data table created by design of experiments techniques through graphical icons and visual indicators such as secondary notation, instead of text-based user interfaces, typed command labels or text navigation.
The ternary map GUI provides a fast, low cost solution to assist users in better understanding available products. The ternary map GUI requires unique username and password access to use. The structure of the ternary map GUI is universal, in that it can be customized to a user's wants and needs. Its dynamic nature allows the modeling of any type of product on the market.
To understand the three axes of a ternary plot 100, each axis (A, B, and C) will be evaluated separately. As shown in
As shown in
As shown in
As shown in
As noted in Table 1, at any point located on the ternary plot 100, all three coordinates will total 100%. Additional information on ternary plots may be sourced from Reading a Ternary Diagram, Ternary plotting program, Power Point presentation from http://csmres.imu.edu/geollab/Fichter/SedRx/readternary.html, which is incorporated herein by reference.
In one aspect, a ternary map GUI may be accessed by way of a login page that serves as a gateway to accessing the ternary map GUI. Once a user has been granted access to utilize the ternary map GUI, he/she will enter the assigned username and password into the provided entry boxes. Once a user has signed in, the home screen provides a tab or other selectable item that the user may select to open a ternary map GUI. In one aspect, the ternary map GUI allows a user to design products using resins, or other products, based on properties of interest as discussed below.
Polyurethane dispersions useful in the present disclosure contain: (A) at least one diol and/or polyol component (B) at least one di- and/or polyisocyanate component (C) at least one component including at least one hydrophilizing group (D) optionally mono-, di- and/or triamine-functional and/or hydroxylamine-functional compounds, and (E) optionally other isocyanate-reactive compounds.
Suitable diol- and/or polyol components (A) are compounds having at least two hydrogen atoms which are reactive with isocyanates and have an average molecular weight of preferably 62 to 18000 and particularly preferably 62 to 4000 g/mol. Examples of suitable structural components include polyethers, polyesters, polycarbonates, polylactones and polyamides. Preferred polyols (A) preferably have 2 to 4, particularly preferably 2 to 3 hydroxyl groups, and most particularly preferably 2 hydroxyl groups. Mixtures of different such compounds are also possible.
Possible polyester polyols are in particular linear polyester diols or indeed weakly branched polyester polyols, as can be prepared from aliphatic, cycloaliphatic or aromatic di- or polycarboxylic acids, such as succinic, methylsuccinic, glutaric, adipic, pimelic, suberic, azelaic, sebacic, nonanedicarboxylic, decanedicarboxylic, terephthalic, isophthalic, o-phthalic, tetrahydrophthalic, hexahydrophthalic, cyclohexane dicarboxylic, maleic, fumaric, malonic or trimellitic acid and acid anhydrides, such as o-phthalic, trimellitic or succinic acid anhydride or mixtures thereof with polyhydric alcohols such as ethanediol, di-, tri-, tetraethylene glycol, 1,2-propanediol, di-, tri-, tetrapropylene glycol, 1,3-propanediol, butanediol-1,4, butanediol-1,3, butanediol-2,3, pentanediol-1,5, hexanediol-1,6, 2,2-dimethyl-1,3-propanediol, 1,4-dihydroxycyclohexane, 1,4-dimethylol cyclohexane, octanediol-1,8, decanediol-1,10, dodecanediol-1,12 or mixtures thereof, optionally with the use of higher-functional polyols, such as trimethylol propane, glycerine or pentaerythritol. Cycloaliphatic and/or aromatic di- and polyhydroxyl compounds are also possible as the polyhydric alcohols for preparing the polyester polyols. Instead of free polycarboxylic acid, it is also possible to use the corresponding polycarboxylic acid anhydrides or corresponding polycarboxylic acid esters of low alcohols or mixtures thereof for preparing the polyesters.
The polyester polyols may be homopolymers or mixed polymers of lactones which are preferably obtained by the addition of lactones or lactone mixtures, such as butyrolactone, ε-caprolactone and/or methyl-ε-caprolactone, to suitable di- and/or higher-functional starter molecules, such as the low-molecular-weight polyhydric alcohols mentioned above as structural components for polyester polyols. The corresponding polymers of ε-caprolactone are preferred.
Polycarbonates having hydroxyl groups are also possible as the polyhydroxyl components (A), e.g. those which can be prepared by reacting diols such as 1,4-butanediol and/or 1,6-hexanediol with diaryl carbonates, such as diphenyl carbonate, dialkyl carbonates, such as dimethyl carbonate, or phosgene. As a result of the at least partial use of polycarbonates having hydroxyl groups, the resistance of the polyurethane dispersion to hydrolysis can be improved.
Suitable polyether polyols are for example the polyaddition products of styrene oxides, ethylene oxide, propylene oxide, tetrahydrofuran, butylene oxide, epichlorohydrine, and mixed addition and grafting products thereof, and the polyether polyols obtained from condensation of polyhydric alcohols or mixtures thereof and from alkoxylation of polyhydric alcohols, amines and amino alcohols. Polyether polyols which are suitable as structural components A) are the homopolymers, mixed polymers and graft polymers of propylene oxide and ethylene oxide which are obtainable by the addition of the said epoxies to low-molecular-weight diols or triols, such as those mentioned above as structural components for polyester polyols, or to higher-functional low-molecular-weight polyols such as pentaerythritol or sugar, or to water.
Other suitable components (A) are low-molecular-weight diols, triols and/or tetraols such as ethanediol, di-, tri-, tetraethylene glycol, 1,2-propanediol, di-, tri-, tetrapropylene glycol, 1,3-propanediol, butanediol-1,4, butanediol-1,3, butanediol-2,3, pentanediol-1,5, hexanediol-1,6, 2,2-dimethyl-1,3-propanediol, 1,4-dihydroxycyclohexane, 1,4-dimethylol cyclohexane, octanediol-1,8, decanediol-1,10, dodecanediol-1,12, neopentyl glycol, 1,4-cyclohexane diol, 1,4-cyclohexane dimethanol, 1,4-, 1,3-, 1,2-dihydroxybenzene or 2,2-bis-(4-hydroxyphenyl)-propane (bisphenol A), TCD-diol, trimethylol propane, glycerine, pentaerythritol, dipentaerythritol or mixtures thereof, optionally also using further diols or triols which are not mentioned.
Suitable polyols are reaction products of the said polyols, in particular low-molecular-weight polyols, with ethylene and/or propylene oxide.
The low-molecular-weight components (A) preferably have a molecular weight of 62 to 400 g/mol and are preferably used in combination with the polyester polyols, polylactones, polyethers and/or polycarbonates mentioned above.
Preferably, the content of polyol component (A) in the polyurethane according to this disclosure is 20 to 95, particularly preferably 30 to 90, and most particularly preferably 65 to 90 wt. %.
Suitable as component (B) are any organic compounds which have at least two free isocyanate groups in each molecule. Preferably, diisocyanates Y(NCO)2 are used, wherein Y represents a divalent aliphatic hydrocarbon radical having 4 to 12 carbon atoms, a divalent cycloaliphatic hydrocarbon radical having 6 to 15 carbon atoms, a divalent aromatic carbon radical having 6 to 15 carbon atoms or a divalent araliphatic hydrocarbon radical having 7 to 15 carbon atoms. Examples of such diisocyanates which are preferably used are tetramethylene diisocyanate, methylpentamethylene diisocyanate, hexamethylene diisocyanate, dodecamethylene diisocyanate, 1,4-diisocyanato-cyclohexane, 1-isocyanato-3,3,5-trimethyl-5-isocyanatomethyl-cyclohexane (IPDI, isophorone diisocyanate), 4,4′-diisocyanato-dicyclohexyl-methane, 4,4′-diisocyanato-dicyclohexylpropane-(2,2), 1,4-diisocyanatobenzene, 2,4-diisocyanatotoluene, 2,6-diisocyanatotoluene, 4,4′-diisocyanato-diphenylmethane, 2,2′- and 2,4′-diisocyanato-diphenylmethane, tetramethyl xylylene diisocyanate, p-xylylene diisocyanate, p-isopropylidene diisocyanate and mixtures of these compounds.
In addition to these simple diisocyanates, also suitable are those polyisocyanates which contain hetero atoms in the radical linking the isocyanate groups and/or have a functionality of more than 2 isocyanate groups in each molecule. The first are for example polyisocyanates which are obtained by modifying simple aliphatic, cycloaliphatic, araliphatic and/or aromatic diisocyanates and which comprise at least two diisocyanates with a uretdione, isocyanurate, urethane, allophanate, biuret, carbodiimide, iminooxadiazinedione and/or oxadiazinetrione structure. As an example of a non-modified polyisocyanate having more than 2 isocyanate groups in each molecule there may, for example, be mentioned 4-isocyanatomethyl-1,8-octane diisocyanate (nonane triisocyanate).
Preferred diisocyanates (B) are hexamethylene diisocyanate (HDI), dodecamethylene diisocyanate, 1,4-diisocyanato-cyclohexane, 1-isocyanato-3,3,5-trimethyl-5-isocyanatomethyl-cyclohexane (IPDI), 4,4′-diisocyanato-dicyclohexyl-methane, 2,4-diisocyanatotoluene, 2,6-diisocyanatotoluene, 4,4′-diisocyanato-diphenylmethane, 2,2′- and 2,4′-diisocyanato-diphenylmethane and mixtures of these compounds.
The content of component (B) in the polyurethane according to this disclosure is from 5 to 60, preferably from 6 to 45, and particularly preferably from 7 to 25 wt. %.
Suitable polyisocyanates are available under the DESMODUR and BAYHYDUR names from Covestro.
Suitable components (C) are for example components containing sulfonate or carboxylate groups, such as diamine compounds or dihydroxyl compounds which additionally contain sulfonate and/or carboxylate groups, such as the sodium, lithium, potassium, t-amine salts of N-(2-aminoethyl)-2-aminoethane sulfonic acid, N-(3-aminopropyl)-2-aminoethane sulfonic acid, N-(3-aminopropyl)-3-aminopropane sulfonic acid, N-(2-aminoethyl)-3-aminopropane sulfonic acid, analogous carboxylic acids, dimethylol propionic acid, dimethylol butyric acid, the reaction products from a Michael addition of 1 mol of diamine such as 1,2-ethane diamine or isophorone diamine with 2 mol of acrylic acid or maleic acid.
The acids are frequently used directly in the form of their salt as a sulfonate or carboxylate. However, it is also possible to add the neutralizing agent needed for formation of the salt in portions or in its entirety only during or after the polyurethanes have been prepared.
For forming salts, particularly suitable and preferred tert. amines are for example triethylamine, dimethyl cyclohexylamine and ethyl diisopropylamine. It is also possible to use other amines for the salt formation, such as ammonia, diethanolamine, triethanolamine, dimethylethanolamine, methyldiethanolamine, aminomethyl propanol, and also mixtures of the said and indeed other amines. It is sensible to add these amines only after the prepolymer has been formed.
It is also possible to use other neutralizing agents, such as sodium, potassium, lithium or calcium hydroxide for neutralizing purposes.
Other suitable components (C) are mono- or difunctional polyethers which have a non-ionic hydophilising action and are based on ethylene oxide polymers or ethylene oxide/propylene oxide copolymers which are started on alcohols or amines, such as POLYETHER LB 25 (Covestro AG) or MPEG 750: methoxypolyethylene glycol, molecular weight 750 g/mol (e.g. PLURIOL 750, BASF AG).
Preferably, components (C) are N-(2-aminoethyl)-2-aminoethane sulfonate and the salts of or dimethylol propionic acid and dimethylol butyric acid.
Preferably, the content of component (C) in the polyurethane according to this disclosure is 0.1 to 15 wt. %, particularly preferably 0.5 to 10 wt. %, very particularly preferably 0.8 to 5 wt. % and even more particularly preferably 0.9 to 3.0 wt. %.
Suitable components (D) are mono-, di-, trifunctional amines and/or mono-, di-, trifunctional hydroxylamines, such as aliphatic and/or alicyclic primary and/or secondary monoamines such as ethylamine, diethylamine, isomeric propyl and butyl amines, higher linear aliphatic monoamines and cycloaliphatic monoamines such as cyclohexylamine. Further examples are amino alcohols, that is compounds which contain amino and hydroxyl groups in one molecule, such as ethanolamine, N-methyl ethanolamine, diethanolamine, diisopropanolamine, 1,3-diamino-2-propanol, N-(2-hydroxyethyl)-ethylene diamine, N,N-bis(2-hydroxyethyl)-ethylene diamine and 2-propanolamine. Further examples are diamines and triamines, such as 1,2-ethane diamine, 1,6-hexamethylene diamine, 1-amino-3,3,5-trimethyl-5-aminomethyl cyclohexane (isophorone diamine), piperazine, 1,4-diamino cyclohexane, bis-(4-aminocyclohexyl)-methane and diethylene triamine. Also possible are adipic acid dihydrazide, hydrazine and hydrazine hydrate. Mixtures of a plurality of the compounds (D), optionally also those with compounds that are not mentioned, may also be used.
Preferred components (D) are 1,2-ethane diamine, 1-amino-3,3,5-trimethyl-5-aminomethyl cyclohexane, diethylene triamine, diethanolamine, ethanolamine, N-(2-hydroxyethyl)-ethylene diamine and N,N-bis(2-hydroxyethyl)-ethylene diamine.
Compounds (D) preferably serve as chain extenders for creating higher molecular weights or as monofunctional compounds for limiting molecular weights and/or optionally additionally for incorporating further reactive groups, such as free hydroxyl groups as further crosslink points.
Preferably, the content of component (D) in the polyurethane according to this disclosure is from 0 to 10, particularly preferably from 0 to 5, and most particularly preferably from 0.2 to 3 wt. %.
Component (E) which may optionally also be used may for example be aliphatic, cycloaliphatic or aromatic monoalcohols having 2 to 22 C atoms, such as ethanol, butanol, hexanol, cyclohexanol, isobutanol, benzyl alcohol, stearyl alcohol, 2-ethyl ethanol, cyclohexanol; blocking agents which are conventional for isocyanate groups and may be split again at elevated temperature, such as butanone oxime, dimethylpyrazole, caprolactam, malonic esters, triazole, dimethyl triazole, t-butyl-benzyl amine, cyclopentanone carboxyethyl ester.
Preferably, the content of components (E) in the polyurethane according to this disclosure may be in quantities from 0 to 20, most preferably from 0 to 10 wt. %.
The polyurethane polymers used according to this disclosure may contain di- or higher-functional polyester polyols (A), based on linear dicarboxylic acids and/or derivatives thereof, such as anhydrides, esters or acid chlorides and aliphatic or cycloaliphatic, linear or branched polyols. These are used in quantities of at least 80 mol %, preferably from 85 to 100 mol %, particularly preferably from 90 to 100 mol %, in relation to the total quantity of all carboxylic acids.
Optionally, other aliphatic, cycloaliphatic or aromatic dicarboxylic acids may also be used. Examples of such dicarboxylic acids are glutaric acid, azelaic acid, 1,4-, 1,3- or 1,2-cyclohexane dicarboxylic acid, terephthalic acid or isophthalic acid. These are used in quantities of at most 20 mol %, preferably from 0 to 15 mol %, particularly preferably from 0 to 10 mol %, in relation to the total quantity of all carboxylic acids.
Preferred polyol components for the polyesters (A) are selected from the group comprising monoethylene glycol, propanediol-1,3, butanediol-1,4, pentanediol-1,5, hexanediol-1,6 and neopentyl glycol, and particularly preferred as the polyol component are butanediol-1,4 and hexanediol-1,6, and most particularly preferred is butanediol-1,4. These are preferably used in quantities of at least 80 mol %, particularly preferably from 90 to 100 mol %, in relation to the total quantity of all polyols.
Optionally, other aliphatic or cycloaliphatic, linear or branched polyols may also be used. Examples of polyols of this kind are diethylene glycol, hydroxypivalic acid neopentyl glycol, cyclohexane dimethanol, pentanediol-1,5, pentanediol-1,2, nonanediol-1,9, trimethylol propane, glycerine or pentaerythritol. These are used in quantities of preferably at most 20 mol %, particularly preferably from 0 to 10 mol %, in relation to the total quantity of all polyols.
Mixtures of two or more polyesters (A) of this kind are also possible.
The polyurethane dispersions according to this disclosure preferably have solids contents of preferably from 15 to 70 wt. %, particularly preferably from 25 to 60 wt. %, and most particularly preferably from 30 to 50 wt. %. The pH is preferably in the range from 4 to 11, particularly preferably from 6 to 10.
The waterborne polyurethane dispersions useful in this disclosure may be prepared such that the components (A), (B) optionally (C) and optionally (E) are reacted in a single-stage or multi-stage reaction to give an isocyanate-functional prepolymer which is then, optionally with component (C) and optionally (D), reacted in a single-stage or two-stage reaction and then dispersed in or using water, wherein solvent used therein may optionally be removed, partially or entirely, by distillation during or after the dispersion.
The waterborne polyurethane or polyurethane urea dispersions according to this disclosure may be prepared in one or more stages in a homogeneous or, in the case of a multi-stage reaction, partly in a disperse phase. After the polyaddition has been partially or entirely performed, a step of dispersion, emulsification or solution is carried out. Then a further polyaddition or modification in a disperse phase is optionally carried out. For the preparation, any methods known from the prior art may be used, such as the emulsifier/shear force method, acetone method, prepolymer mixing method, melting/emulsifying method, ketimine method and spontaneous dispersion of solids method, or derivatives thereof. A summary of these methods can be found in Methoden der organischen Chemie (Houben-Weyl, supplemental volumes to the 4th edition, Volume E20, H. Bartl and J. Falbe, Stuttgart, N.Y., Thieme 1987, pp. 1671-1682). The melting/emulsifying method, prepolymer mixing method and acetone method are preferred. The acetone method is particularly preferred.
In principle, it is possible to measure out all the components—all the hydroxy-functional components—together, and then to add all the isocyanate-functional components and react them to give an isocyanate-functional polyurethane, which is then reacted with the amino-functional components. Preparation is also possible the other way around, that is taking the isocyanate component, adding the hydroxy-functional components, reacting to give polyurethane and then reacting with the amino-functional components to give the end product.
Conventionally, all or some of the hydroxy-functional components (A), optionally (C) and optionally (E) for preparing a polyurethane prepolymer are put into the reactor, optionally diluted with a water-miscible solvent which is, however, inert to isocyanate groups, and then homogenised. Then the component (B) is added at room temperature to 120° C. and an isocyanate-functional polyurethane is prepared. This reaction may be performed in a single stage or in multiple stages. A multi-stage reaction may be carried out for example in that a component (C) and/or (E) is reacted with the isocyanate-functional component (B) and then a component (A) is added thereto and can then be reacted with some of the isocyanate groups that are still present.
Suitable solvents are for example acetone, methyl isobutyl ketone, butanone, tetrahydrofuran, dioxan, acetonitrile, dipropylene glycol dimethyl ether and 1-methyl-2-pyrrolidone, which may be added not only at the start of preparation but optionally also later in portions. Acetone and butanone are preferred. It is possible to perform the reaction at standard pressure or under elevated pressure.
To prepare the prepolymer, the quantities of hydroxyl-functional and, optionally, amino-functional components that are used are such that a ratio of isocyanate of preferably 1.05 to 2.5, particularly preferably 1.15 to 1.95, most particularly preferably 1.2 to 1.7 is produced.
The further reaction, the so-called chain extension, of the isocyanate-functional prepolymer with further hydroxy- and/or amino-functional, preferably only amino-functional components (D) and optionally (C) is performed such that a degree of conversion of preferably 25 to 150%, particularly preferably 40 to 85%, of hydroxyl and/or amino groups in relation to 100% isocyanate groups is selected.
In the case of degrees of conversion greater than 100%, which are possible but less preferred, it is appropriate first to react all the components which are monofunctional for the isocyanate addition reaction with the prepolymer, and then to use the di- or higher-functional chain-extending components to obtain the greatest possible degree of incorporation of all the chain-extending molecules.
Conventionally, the degree of conversion is monitored by tracking the NCO content of the reaction mixture. For this, both spectroscopic measurements, such as infrared or near infrared spectra or determination of the refractive index, and chemical analyses such as the titration of samples may be carried out.
To accelerate the isocyanate addition reaction, conventional catalysts such as those known to those skilled in the art for acceleration of NCO—OH reactions may be used. Examples are triethylamine, 1,4-diazabicyclo-[2,2,2]octane, dibutyltin oxide, tin dioctoate or dibutyltin dilaurate, tin-bis-(2-ethyl hexanoate), zinc dioctoate, zinc-bis-(2-ethyl hexanoate) or other organo-metallic compounds.
The chain of the isocyanate-functional prepolymer may be extended with the component (D) and optionally (C) before, during or after dispersion. Preferably, the chain extension is carried out before dispersion. If component (C) is used as the chain-extending component, then it is imperative that chain extension with this component be carried out before the dispersion step. Conventionally, the chain extension is carried out at temperatures of 10 to 100° C., preferably from 25 to 60° C.
The term “chain extension”, in the context of the present disclosure, also includes the reactions of optionally monofunctional components (D) which, as a result of their monofunctionality, act as chain terminators and thus result not in an increase but a limitation of the molecular weight.
The components of chain extension may be added to the reaction mixture diluted with organic solvents and/or water. They may be added successively, in any order, or at the same time by adding a mixture.
For the purpose of preparing the polyurethane dispersion, the prepolymer may either be added to the dispersion liquid, optionally under pronounced shear, such as vigorous stirring, or conversely the dispersion liquid is stirred into the prepolymer. Then the chain extension step is carried out, unless this has already been done in the homogeneous phase.
During and/or after dispersion, the organic solvent which is optionally used, such as acetone, is distilled off.
Polyurethane dispersions useful in the practice of the present disclosure may be found under the BAYHYDROL, DISPERCOLL and IMPRANIL tradenames from Covestro.
A plurality of plots 210, 220, 230, 240, 250, 260 may be generated and displayed on the ternary map GUI page 200. The plurality of plots 210, 220, 230, 240, 250, 260 each may define a geometric shape and include a plurality of points arranged in a matrix. Each of the points may define a value for at least two variables and a predicted value of the property of the material for each of the plurality of plots. A visual representation of the predicted value of the property of the material for at least some of the plurality of points in a range of indicia, wherein the range of indicia represents a range of predicted values of the property may be displayed on the ternary map GUI page 200. A pointer 212, 222, 232, 242, 252, 262 is displayed on each of the plurality of plots, such as the heat maps 216, 226, 236, 246, 256, 266, for example.
As shown in the example of
In one aspect, the ternary plots 210, 220, 230, 240, 250, 260 may be generated by a model. The model may be generated, for example, based on design of experiments, regression analysis of a data set, an equation, machine learning, or artificial intelligence, and/or any combination thereof.
In the example illustrated in
In one aspect, the geometric shape defines a closed shape in Euclidian space. In one aspect, the closed shape defines a polygon. In the example illustrated in
A heat map 216, 226, 236, 246, 256, 266 is a graphical representation of data, where the individual values contained in a matrix are represented as colors as shown, for example, in the corresponding color scales 214, 224, 234, 244, 254, 264. A unique color scale 214, 224, 234, 244, 254, 264 may be provided for each Property 1-Property 6 represented by the ternary plots 210, 220, 230, 240, 250, 260. With respect to the ternary map GUI 209 the various colors represent a range of measured values of the property described by the heat map 216, 226, 236, 246, 256, 266. The measured values may be stored in a data table 1732 as shown in
Turning back to
Based on the position of the pointer 212, 222, 232, 242, 252, 262 on the heat map 216, 226, 236, 246, 256, 266, the ternary map GUI 209 provides a graphical display of the corresponding property of the material for that point. As shown in
In one aspect, the present disclosure provides formulating a composition based on a plurality of properties for at least some of the plurality of points in the range of indicia. Accordingly, once the presented ternary plots 210, 220, 230, 240, 250, 260 shown in
For example, using the provided pointer, the user can change the ratio of amounts of components, such as resins, used in a formulation. To change the amounts of each component, such as a resin (such as a PUD), the curser 316 is used to click and drag the pointer 302 on the heat map 216, 226, 236, 246, 256, 266 on any of the provided ternary plots 210, 220, 230, 240, 250, 260. No matter the ternary plot 210, 220, 230, 240, 250, 260 on which the pointer was moved, the corresponding pointer 212, 222, 232, 242, 252, 262 on each of the remaining ternary plots 210, 220, 230, 240, 250, 260 moves to the same location. Turning back to
Turning to
Using the slider 348 in “Mixture 2 Selection” tool bar 206, the user can specify the ratio of the amounts of isocyanates (e.g., ISO E, ISO F) used in a formulation. Upon changing the isocyanate ratio, the color distribution of the heat maps 216, 226, 236, 246, 256, 266 in the provided ternary plots 210, 220, 230, 240, 250, 260 for each Property 1-Property 6 will update accordingly. Ternary plots 210, 220, 230, 240, 250, 260 that do not change in color distribution, if any, are independent of the type and amount of isocyanate used in the formulation.
Further, the present disclosure provides optimizing one or more than one property of the material within one or more than one defined range of indicia. A gridded region that represents one or more than one optimized region based on the one or more than one defined range of indicia may be displayed on the ternary map GUI page 200.
By specifying the range minimum 404 and range maximum 406 values of the Property 2, the color gradient is forced to be contained within the specified range for that property ternary plot. Clicking the “Opt” checkbox 410 outputs a grid on each map over the area to which Property 2 is within the specified range on each of the property ternary plots.
An example of an optimized ternary plot 500 is shown in
To further optimize the ternary plots 210, 220, 230, 240, 250, 260 with a second desired characteristic, the user can change the Property 5 range to be from 60 to 66 as shown in
The second ternary plot 630 includes a heat map 636 colored according to the color scheme scale 634. The value 34.0 at the location of the pointer 632 is shown next to a horizontal bar 741 and box element 741 in the scale 634 section of the ternary map and next to a box element 740 below the Property 2 label of the ternary map. The color of the box element 740 and the horizontal bar 741 is equal to the color representing the predicted property value based on the location of the pointer 632 on the heat map 636. The color of the box element 740 and the horizontal bar 741 is dynamically updated based on the position of the pointer 632 as the pointer 632 is dragged over the heat map 636. The value 34.0 is also shown in a Property 2 cell 706 of the current selection table 702. The cell 706 is highlighted in a first color because the pointer 632 is located in an optimized region of the ternary plot 630. A gridded region 638 is provided over the optimized portion of the heat map 636 for Property 2. A non-optimized region 744 is defined outside the gridded region 638 region. The third section 715 of the current selection table 702 shows the predicted property values for each Property 1-Property 6.
In the example depicted in
The user may generate a starting point formulation guide 850 for any stored resin combination and may print the guide by right clicking the web-page and selecting “Print.”
In one aspect, the geometric shape defines a closed shape in Euclidian space such as a four-sided polygon, for example. In the four-sided polygon example, each of the points may define a value for two variables, wherein each variable is, for example, a value for an amount of a component in a composition, a processing condition, or a value representing an amount of two components of the composition relative to each other. In one aspect, a square map graphical user interface (GUI) allows a user to design products using resins, or other products, based on properties of interest. Many degrees of freedom can be embedded in the software, allowing the user to explore an entire design space of available products. In one aspect, such as in the case where the material is a polyurethane foam, the square maps can plot water versus Isocyanate Index. If desired, however, the user may change an axis by selecting the radio button next to the variable of interest. For conciseness and clarity of disclosure, the default setting will be utilized in the following description.
In the example illustrated in
As shown in the example of
In one aspect, the square plots 1020-1031 may be generated by a model. The model may be generated based on, for example, design of experiments, regression analysis of a data set, an equation, machine learning, or artificial intelligence, and/or any combination thereof.
As previously discussed, the heat maps 1068-1079 are graphical representations of data, where the individual values contained in a matrix are represented as colors based on a color scheme scale 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, respectively. With respect to the square map GUI 1014, the various colors represent a range of predicted values of the property it describes. As shown in
Once the color scheme is selected, each of the heat maps 1068-1079 includes a pointer 1056-1067, respectively, which provides current selection data values based on its position within the heat map. The pointer 1056-1067 can be moved within the heat map 1068-1079 by clicking and dragging using the cursor 1094. As one pointer is moved within a particular heat map, all pointers 1056-1067 will move at the same time in the same manner. As the pointers 1056-1067 are moved within the heat maps 1068-1079 the values of the variables are simultaneously updated in a table that can be displayed simultaneously with the square map GUI 1014. In the illustrated example, each of the heat maps represents “Water” along the horizontal axis and “Index” (i.e., Isocyanate Index) along the vertical axis as discussed in more detail hereinbelow. In the example illustrated in
The illustrated example of
As the pointer 1056-1067 is moved over the heat map 1068-1079, predicted property values (and base cost) for the material is displayed in two locations. First, a predicted property value (and base cost) is displayed above a horizontal bar 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 10531054, 1055 in the color scale 1032-1043 region of the square plot 1020-1031. Second, a predicted property value is displayed next to a box element 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091 located below the “Property” label on the square plot 1020-1031. The color of the horizontal bar 1044-1055 and the box element 1080-1091 is the same color as the corresponding color associated with the property of the material as determined by the underlying software based on the current location of the pointer 1056-1067. As illustrated in the example of
The unit selection GUI window 1125 includes a unit selection bar 1126. Below the unit selection bar 1126, there is radio button selection area that includes global units 1128 and cost 1130 radio buttons. Units for the displayed property (Property 1-Property 11 in
Using the provided pointer 1061, a user can change the amount of water and the NCO Index of a proposed formulation, by clicking and dragging the pointer 1061 with the cursor 1094 on the heat map 1073 or any of the other provided heat maps 1068-1079 (
Now that the presented square plots have been identified, the formulating process can begin. It should be noted that use of the square map GUI 1014 (
The second variable selection and slider bar GUI window 1008 displays a slide to change bar 1180, a variable bar 1182, a level bar 1184, and x-axis bar 1186, and a y-axis bar 1188. The first variable displayed below the variable bar 1182 is “Index.” A radio button selects whether a variable is displayed along the x-axis or the y-axis. In the illustrated example, the variable “Index” is displayed along the y-axis as indicated by the selected radio button 1190. As shown in the examples illustrated in
The third variable selection and slider bar GUI window 1010 displays a slide to change bar 1200, a variable bar 1202, a level bar 1204, and x-axis bar 1206, and a y-axis bar 1208. The first variable displayed below the variable bar 1202 is “Filler (%).” A radio button selects whether the variable is displayed along the x-axis or the y-axis. In the illustrated example, none of the variables are displayed along the x-axis or y-axis as indicated by the unselected radio buttons. The level for the “Filler (%)” variable is controlled with the slider 1210. As shown, the “Filler (%)” level is currently set to minimum or zero (0). The next variable is “AtmP (mmHg)” (atmospheric pressure in mm of Hg) and its level is controlled with the slider bar 1212. As shown, the “AtmP (mmHg)” level is currently set to 30 mmHg. The next variable is “Temp (° F.)” (temperature) and its level is controlled with the slider bar 1214. As shown, the “Temp (° F.)” level is currently set to 70° F. The next variable is “Relative Humidity (%)” and its level is controlled with the slider bar 1216. As shown, the “Relative Humidity (%)” level is currently set to 50%. The final variable is “Material Temp (° F.)” and its level is controlled with the slider bar 1218. As shown, the “Material Temp (° F.)” level is currently set to 70° F. For all the slider bars 1210, 1212, 1214, 1216, 1218 sliding to the left decreases the level and sliding to the right increases the level.
Upon changing a value, the plots temporarily disappear. This occurs so that all of the background equations can recalculate to update the plots, based on the new selected value.
Foams currently related to the square plots 1020-1031 described with reference to
The components include a polyisocyanate component and an isocyanate-reactive component that includes several ingredients such as polyols, monols, blowing agents, catalysts, surfactants, and other additives as described hereinbelow.
Suitable polyisocyanate components to be used as component (1) include, for example, aromatic polyisocyanates characterized by a functionality of greater than or equal to about 2.0. In particular, the suitable polyisocyanates and/or prepolymers thereof to be used as component (1) typically have NCO group contents of greater than about 20%. Suitable aromatic polyisocyanates include toluene diisocyanate including 2,4-toluene diisocyanate, 2,6-toluene diisocyanate and mixtures thereof, diphenylmethane diisocyanate including 2,2′-diphenylmethane diisocyanate, 2,4′-diphenylmethane diisocyanate, 4,4′-diphenylmethane diisocyanate, and isomeric mixtures thereof, polyphenylmethane polyisocyanates, etc. One suitable aromatic polyisocyanate component comprises a mixture of 80% by weight of 2,4-toluene diisocyanate and 20% by weight of 2,6-toluene diisocyanate.
Suitable polyoxyalkylene polyether polyols include those having a hydroxyl functionality of at least about 2. The hydroxyl functionality of the polyoxyalkylene polyether polyols is often less than or equal to about 8, such as less than or equal to about 6 or less than or equal to 4. Suitable polyoxyalkylene polyether polyols may also have functionalities ranging between any combination of these upper and lower values, inclusive, e.g., from at least 2 to no more than 8, such as from at least 2 to no more than 6 or from at least 2 to no more than 4. Typically, the average OH (hydroxyl) numbers of suitable polyoxyalkylene polyether polyols is at least about 20, such as at least 25. Polyoxyalkylene polyether polyols typically also have average OH numbers of less than or equal to 250, such as less than or equal to 150.
Suitable polyoxyalkylene polyether polyols for the isocyanate-reactive component (2) of the flexible foams are typically the reaction product of a suitable initiator or starter and one or more alkylene oxides. The polyoxyalkylene polyether polyols typically have less than or equal to about 85% by weight of copolymerized oxyethylene, based on 100% by weight of oxyalkylene present.
Thus, the isocyanate-reactive component (2) of the flexible foams comprises one or more polyoxyalkylene polyether polyols and is typically described in terms of their hydroxyl functionality, OH (hydroxyl) number, and the amount of copolymerized oxyethylene. Generally speaking, suitable polyoxyalkylene polyether polyols include those which contain from 2 to 8 hydroxyl groups per molecule, having an OH (hydroxyl) number of from 20 to 250, and containing less than equal to about 85% by weight of copolymerized oxyethylene, based on 100% by weight of oxyalkylene present in the polyether polyol.
As used herein, the hydroxyl number is defined as the number of milligrams of potassium hydroxide required for the complete hydrolysis of the fully phthalylated derivative prepared from 1 gram of polyol. The hydroxyl number can also be defined by the equation: OH=(56.1×1000/eq. wt.)=(56.1×1000)×(f/mol. wt.) where: OH: represents the hydroxyl number of the polyol; eq. wt.: weight per molar equivalents of contained OH groups; f: represents the nominal functionality of the polyol, i.e. the average number of active hydrogen groups on the initiator or initiator blend used in producing the polyol; and mol. wt.: represents the nominal number average molecular weight based on the measured hydroxyl number and the nominal functionality of the polyol.
Among the polyoxyalkylene polyols which can be used are the alkylene oxide adducts of a variety of suitable initiator molecules. Non-limiting examples include dihydric initiators such as ethylene glycol, diethylene glycol, triethylene glycol, propylene glycol, dipropylene glycol, tripropylene glycol, neopentyl glycol, 1,3-propanediol, 1,4-butanediol, 1,6-hexanediol, 1,4-cyclo-hexanediol, 1,4-cyclohexane-dimethanol, hydroquinone, hydroquinone bis(2-hydroxyethyl)ether, the various bisphenols, particularly bisphenol A and bisphenol F and their bis(hydroxyalkyl) ether derivatives, aniline, the various N—N-bis(hydroxyalkyl)anilines, primary alkyl amines and the various N—N-bis(hydroxyalkyl)amines; trihydric initiators such as glycerine, trimethylolpropane, trimethylolethane, the various alkanolamines such as ethanolamine, diethanolamine, triethanolamine, propanolamine, dipropanolamine, and tripropanolamine; tetrahydric initiators such as pentaerythritol, ethylene diamine, N,N, N′,N′-tetrakis[2-hydroxyalkyl]ethylenediamines, toulene diamine and N,N,N′,N′-tetrakis[hydroxyalkyl]toluene diamines; pentahydric initiators such as the various alkylglucosides, particularly α-methylglucoside; hexahydric initiators such as sorbitol, mannitol, hydroxyethylglucoside, and hydroxypropyl glucoside; octahydric initiators such as sucrose; and higher functionality initiators such as various starch and partially hydrolyzed starch-based products, and methylol group-containing resins and novolak resins such as those prepared from the reaction of as aldehyde, such as formaldehyde, with a phenol, cresol, or other aromatic hydroxyl-containing compound.
Such starters or initiators are typically copolymerized with one or more alkylene oxides to form polyether polyols. Examples of such alkylene oxides include ethylene oxide, propylene oxide, butylenes oxide, styrene oxide and mixtures thereof. Mixtures of these alkylene oxides can be added simultaneously or sequentially to provide internal blocks, terminal blocks or random distribution of the alkylene oxide groups in the polyether polyol. A suitable mixture comprises ethylene oxide and propylene oxide, provided the total amount of copolymerized oxyethylene in the resultant polyether polyol is less than 85% by weight.
The most common process for polymerizing such polyols is the base catalyzed addition of the oxide monomers to the active hydrogen groups of the polyhydric initiator and subsequently to the oligomeric polyol moities. Potassium hydroxide or sodium hydroxide are the most common basic catalyst used. Polyols produced by this process can contain significant quantities of unsaturated monols resulting from the isomerization of oxypropylene monomer to allyl alcohol under the conditions of the reaction. This monofunctional alcohol can then function as an active hydrogen site for further oxide addition.
One class of suitable polyoxyalkylene polyols are the low unsaturation (low monol) poly(oxypropylene/oxyethylene) polyols manufactured with double metal cyanide catalyst. The poly(oxypropylene/oxyethylene) low unsaturation polyols are prepared by oxyalkylating a suitably hydric initiator compound with propylene oxide and ethylene oxide in the presence of a double metal cyanide catalyst. The amount of ethylene oxide in the ethylene oxide/propylene oxide mixture may be increased during the latter stages of the polymerization to increase the primary hydroxyl content of the polyol. Alternatively, the low unsaturation polyol may be capped with ethylene oxide using non-DMC catalysts.
When the oxyalkylation is performed in the presence of double metal cyanide catalysts, it may be desirable that initiator molecules containing strongly basic groups such as primary and secondary amines be avoided. Further, when employing double metal cyanide complex catalysts, it is generally desirable to oxyalkylate an oligomer which comprises a previously oxyalkylated “monomeric” initiator molecule.
Polyol polymer dispersions represent another suitable class of polyoxyalkylene polyol compositions. Polyol polymer dispersions are dispersions of polymer solids in a polyol. Polyol polymer dispersions which are useful in the production of polyurethane foams include the “PHD” and “PIPA” polymer modified polyols as well as the “SAN” polymer polyols. Any “base polyol” known in the art can be suitable for production of polymer polyol dispersions, such as the poly(oxyalkylene) polyols described previously herein.
SAN polymer polyols are typically prepared by the in-situ polymerization of one or more vinyl monomers, such as acrylonitrile and styrene, in a polyol, such as a poly(oxyalkylene) polyol, having a minor amount of natural or induced unsaturation.
SAN polymer polyols typically have a polymer solids content within the range of from 3 to 60 wt. %, such as from 5 to 55 wt. %, based on the total weight of the SAN polymer polyol. As mentioned above, SAN polymer polyols are typically prepared by the in situ polymerization of a mixture of acrylonitrile and styrene in a polyol. When used, the ratio of styrene to acrylonitrile polymerized in-situ in the polyol is typically in the range of from about 100:0 to about 0:100 parts by weight, based on the total weight of the styrene/acrylonitrile mixture, such as from 80:20 to 0:100 parts by weight.
PHD polymer modified polyols are typically prepared by the in-situ polymerization of an isocyanate mixture with a diamine and/or hydrazine in a polyol, such as a polyether polyol. PIPA polymer modified polyols are typically prepared by the in situ polymerization of an isocyanate mixture with a glycol and/or glycol amine in a polyol.
PHD and PIPA polymer modified polyols typically have a polymer solids content within the range of from 3 to 30 wt. %, such as from 5 to 25 wt. %, based on the total weight of the PHD or PIPA polymer modified polyol. As mentioned above, PHD and PIPA polymer modified polyols are typically prepared by the in-situ polymerization of an isocyanate mixture, typically, a mixture which is composed of about 80 parts by weight, based on the total weight of the isocyanate mixture, of 2,4-toluene diisocyanate and about 20 parts by weight, based on the total weight of the isocyanate mixture, of 2,6-toluene diisocyanate, in a polyol, such as a poly(oxyalkylene) polyol.
By the term “polyoxyalkylene polyol or polyoxyalkylene polyol blend” is meant the total of all polyoxyalkylene polyether polyols, whether polyoxyalkylene polyether polyols containing no polymer dispersion or whether the base polyol(s) of one or more polymer dispersions.
It should also be appreciated that blends or mixtures of various useful polyoxyalkylene polyether polyols may be used if desired. It is possible that one of the polyether polyols has a functionality, OH number, etc. outside of the ranges identified above. In addition, the isocyanate-reactive component may comprise one or more polyoxyalkylene monols formed by addition of multiple equivalents of epoxide to low molecular weight monofunctional starters such as, for example, methanol, ethanol, phenols, allyl alcohol, longer chain alcohols, etc., and mixtures thereof. Suitable epoxides can include, for example, ethylene oxide, propylene oxide, butylene oxide, styrene oxide, etc. and mixtures thereof. The epoxides can be polymerized using well-known techniques and a variety of catalysts, including alkali metals, alkali metal hydroxides and alkoxides, double metal cyanide complexes, and many more. Suitable monofunctional starters can also be made, for example, by first producing a diol or triol and then converting all but one of the remaining hydroxyl groups to an ether, ester or other non-reactive group.
Suitable blowing agents to be used as component (3) include, for example, halogenated hydrocarbons, water, liquid carbon dioxide, low boiling solvents such as, for example, pentane, and other known blowing agents. Water may be used alone or in conjunction with other blowing agents such as, for example, pentane, acetone, cyclopentanone, cyclohexane, partially or completely fluorinated hydrocarbons, methylene chloride and liquid carbon dioxide. In some cases water is used as the sole blowing agent or water used in conjunction with liquid carbon dioxide. Generally, speaking, the quantity of blowing agent present is from 0.3 to 30 parts, such as from 0.5 to 20 parts by weight, based on 100 parts by weight of component (2) present in the formulation.
Suitable catalysts for component (4), include, for example, the various polyurethane catalysts which are known to be capable of promoting the reaction between the aromatic polyisocyanate component and the isocyanate-reactive components, including water. Examples of such catalysts include, but are not limited to, tertiary amines and metal compounds as are known and described in the art. Some examples of suitable tertiary amine catalysts include triethylamine, triethylenediamine, tributylamine, N-methylmorpholine, N-ethyl-morpholine, N,N,N′,N′-tetra-methylethylene diamine, pentamethyl-diethylene triamine, and higher homologs, 1,4-diazabicyclo[2.2.2]octane, N-methyl-N′(dimethylaminoethyl) piperazine, bis(dimethylaminoalkyl)-piperazines, N,N-dimethylbenzylamine, N,N-dimethylcyclohexylamine, N,N-diethylbenzylamine, bis(N,N-diethyl-aminoethyl) adipate, N,N,N′,N′-tetramethyl-1,3-butanediamine, N,N-dimethyl-β-phenylethylamine, 1,2-dimethylimidazole, 2-methylimidazole, monocyclic and bicyclic amidines, bis(dialkylamino)alkyl ethers (such as bis(N,N-dimethylaminoethyl) ether), and tertiary amines containing amide groups (such as formamide groups). The catalysts used may also be the known Mannich bases of secondary amines (such as dimethylamine) and aldehydes (such as formaldehyde) or ketones (such as acetone) and phenols.
Suitable catalysts also include certain tertiary amines containing isocyanate reactive hydrogen atoms. Examples of such catalysts include triethanolamine, triisopropanoamine, N-methyldiethanolamine, N-ethyl-diethanolamine, N,N-dimethylethanolamine, their reaction products with alkylene oxides (such as propylene oxide and/or ethylene oxide) and secondary-tertiary amines.
Other suitable catalysts include acid blocked amines (i.e. delayed action catalysts). The blocking agent can be an organic carboxylic acid having 1 to 20 carbon atoms, such as 1-2 carbon atoms. Examples of blocking agents include 2-ethyl-hexanoic acid and formic acid. Any stoichiometric ratio can be employed, such as one acid equivalent blocking one amine group equivalent. The tertiary amine salt of the organic carboxylic acid can be formed in situ, or it can be added to the polyol composition ingredients as a salt, such as a quaternary ammonium salt. Additional examples of suitable organic acid blocked amine gel catalysts which may be employed are the acid blocked amines of triethylene-diamine, N-ethyl or methyl morpholine, N,N dimethylamine, N-ethyl or methyl morpholine, N,N dimethylaminoethyl morpholine, N-butyl-morpholine, N,N′ dimethylpiperazine, bis(dimethylamino-alkyl)-piperazines, 1,2-dimethyl imidazole, dimethyl cyclohexylamine. Further examples include DABCO® 8154 catalyst based on 1,4-diazabicyclo[2.2.2]octane and DABCO® BL-17 catalyst based on bis(N,N-dimethylaminoethyl) ether (available from Air Products and Chemicals, Inc., Allentown, Pa.) and POLYCAT® SA-1, POLYCAT® SA-102, and POLYCAT® SA-610/50 catalysts based on POLYCAT® DBU amine catalyst (available from Air Products and Chemicals, Inc.) as are known.
Other suitable catalysts include organic metal compounds, especially organic tin, bismuth, and zinc compounds. Suitable organic tin compounds include those containing sulfur, such as dioctyl tin mercaptide, and, such as tin(II) salts of carboxylic acids, such as tin(II) acetate, tin(II) octoate, tin(II) ethylhexoate, and tin(II) laurate, as well as tin(IV) compounds, such as dibutyltin dilaurate, dibutyltin dichloride, dibutyltin diacetate, dibutytin maleate, and dioctyltin diacetate. Suitable bismuth compounds include bismuth neodecanoate, bismuth versalate, and various bismuth carboxylates. Suitable zinc compounds include zinc neodecanoate and zinc versalate. Mixed metal salts containing more than one metal (such as carboxylic acid salts containing both zinc and bismuth) are also suitable catalysts.
The quantity of catalyst varies widely depending on the specific catalyst used. Generally speaking, suitable levels of catalyst would be readily determined by those skilled in the art of polyurethane chemistry.
Suitable surfactants to be used as component (5) include silicone surfactants such as, for example, polysiloxanes and siloxane/poly(alkylene oxide) copolymers of various structures and molecular weights. The structure of these compounds is generally such that a copolymer of ethylene oxide and propylene oxide is attached to a polydimethyl siloxane radical. In some cases, such surfactants are used in amounts of from 0.05 to 5% by weight, such as 0.2 to 3% by weight, based on the weight of component (2) present in the formulation.
In addition, other additives which may be used include, for example, release agents, pigments, cell regulators, flame retarding agents, foam modifiers, plasticizers, dyes, antistatic agents, antimicrobials, cross-linking agents, antioxidants, UV stabilizers, mineral oils, fillers (such as calcium carbonate and barium sulfate) and reinforcing agents such as glass in the form of fibers or flakes or carbon fibers.
In various aspects, a plot defining a geometric shape may include a closed shape defining n-sided polygons such as pentagons, hexagons, heptagons, octagons, and so forth. In other aspects, the plot may define a geometric shape including a closed shape defining an ellipse or a circle. In other aspects, the shape may define either a two-dimensional space or a two-dimensional perspective projection of a three-dimensional shape.
The plot defines a geometric shape including a plurality of points arranged in a matrix. Each of the points defines a value for the at least two variables and a predicted value of a property of the material. The at least two variables may be independent variables (selection of elements that can be controlled) and/or dependent variables (elements that are plotted in the heat maps to be predicted).
Each of the points of the n-sided polygon defines a value for the n-variables, where each of the n-variables is a value for an amount of a component in a composition. In a constrained case, the amounts may be expressed as a percentage and a sum of the amounts is 100%. In one aspect, the composition may be specified with constrained independent variables and properties, such as thickness and cure time, may be specified as unconstrained independent variables. For clarity, the term constrained is used to indicate the interdependence of independent variables. The unconstrained independent variables also may have limits (e.g., thickness between 0.001″ and 0.003″ or cure temperature from 100° C. to 150° C., etc.). Table 3 tabulates the number of in dependent variables for the constrained case and Table 4 tabulates the number of independent variables for the unconstrained case.
Accordingly, the present disclosure is not limited to generating heat maps of independent variables axes only and is not limited to only triangles or squares. For example, a ternary map may be generated where the composition may be varied by dragging the pointer over the heat map. In addition to ternary maps, a square map may be generated that maps all unique pairs of dependent variables. The mapping pointer could also be shown on the square maps as the pointer is dragged over the heat map and may appear in different relative x/y places as opposed to the square map example disclosed herein.
Furthermore, not all spaces on a heat map may be accessible by moving the composition pointer in the ternary plot(s). What may be mapped (e.g., red and green only) are possible dependent variable binary combinations attainable in the independent variable space of a ternary triangle. This scenario can be done with square map of dependent variables as well.
Furthermore, in one aspect, the closed shape defines a two-dimensional perspective projection of a three-dimensional (3D) shape. The 3D output may be accessed with virtual reality hardware. In one aspect, the 3D output may resemble a cube-like 3D shape made of individual smaller cubes (e.g., a Rubik's cube-like configuration) with a matrix of heat maps (either triangle maps or square maps) on each face of the smaller cube being a different set of independent variables levels. In another aspect, the 3D output may resemble a pyramid-like 3D shape made of individual smaller pyramids (e.g., a pyramid-like pyramid-like configuration) with a matrix of heat maps (either triangle maps or square maps) on each face of the smaller pyramid being a different set of independent variables levels.
In other aspects, the computing device 1712 may include additional features and/or functionality. For example, the computing device 1712 also may include additional storage (e.g., removable and/or non-removable) including, but not limited to, magnetic storage, optical storage, and the like. Such additional storage is illustrated in
The term “computer readable media” as used herein includes computer storage media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions or other data. The memory 1718 and the storage 1720 are examples of computer storage media. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVDs) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computing device 1712. Computer storage media does not, however, include propagated signals. Rather, computer storage media excludes propagated signals. Any such computer storage media may be part of the computing device 1712.
The computing device 1712 also may include one or more communication connection(s) 1726 that allows the computing device 1712 to communicate with other devices such as the computing device 1730. The communication connection(s) 1726 may include, but is not limited to, a modem, a Network Interface Card (NIC), an integrated network interface, a radio frequency transmitter/receiver, an infrared port, a USB connection, or other interfaces for connecting the computing device 1712 to other computing devices. The communication connection(s) 1726 may include a wired connection or a wireless connection. The communication connection(s) 1726 may transmit and/or receive communication media.
The term “computer readable media” may include communication media. Communication media typically embodies computer readable instructions or other data in a “modulated data signal” such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” may include a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.
The computing device 1712 may include one or more input device(s) 1724 such as keyboard, mouse, pen, voice input device, touch input device, infrared cameras, video input devices, and/or any other input device. Output input device(s) 1722 such as one or more displays, speakers, printers, and/or any other output device may also be included in the computing device 1712. The one or more input device(s) 1724 and one or more output device(s) 1722 may be connected to the computing device 1712 via a wired connection, wireless connection, or any combination thereof. In one aspect, an input device or an output device from another computing device may be used as the input device(s) 1724 or the output device(s) 1722 for the computing device 1712.
Components of the computing device 1712 may be connected by various interconnects, such as a bus. Such interconnects may include a Peripheral Component Interconnect (PCI), such as PCI Express, a Universal Serial Bus (USB), firewire (IEEE 1394), an optical bus structure, and the like. In another aspect, components of the computing device 1712 may be interconnected by a network. For example, the memory 1718 may be comprised of multiple physical memory units located in different physical locations interconnected by a network.
Storage devices utilized to store computer readable instructions may be distributed across a network. For example, a computing device 1730 accessible via a network 1728 may store computer readable instructions to implement one or more aspects provided herein. The computing device 1712 may access the computing device 1730 and download a part or all of the computer readable instructions for execution. Alternatively, computing device 1712 may download pieces of the computer readable instructions, as needed, or some instructions may be executed at the computing device 1712 and some at the computing device 1730. The computing device 1730 may be coupled to a stored data table 1732. The contents of the data table 1732 can be accessed by both computing devices 1712, 1730. In one aspect, the data table 1732 stores the formulation data set that is used to generate the ternary plots and the square plots described herein. The data table 1732 may be employed to store the data tables described herein.
The computing device 1730 may include all or some of the components of the computing device 1712. For example, in one aspect the computing device 1730 includes at least one processing unit and a memory, e.g., a volatile memory (such as RAM, for example), a non-volatile memory (such as ROM, flash memory, etc., for example) or some combination of the two. In other aspects, the computing device 1730 may include additional storage (e.g., removable and/or non-removable) including, but not limited to, magnetic storage, optical storage, and the like. In one aspect, computer readable instructions to implement one or more aspects provided herein may be stored in the storage. The storage also may store other computer readable instructions to implement an operating system, an application program, and the like. Computer readable instructions may be loaded in the memory for execution by the processing unit, for example.
The computing device 1730 also may include one or more communication connection(s) that allows the computing device 1730 to communicate with other devices such as the computing device 1712. The communication connection(s) may include, but is not limited to, a modem, a Network Interface Card (NIC), an integrated network interface, a radio frequency transmitter/receiver, an infrared port, a USB connection, or other interfaces for connecting the computing device 1730 to other computing devices. The communication connection(s) may include a wired connection or a wireless connection. The communication connection(s) may transmit and/or receive communication media.
The computing device 1730 may include one or more input device(s) such as keyboard, mouse, pen, voice input device, touch input device, infrared cameras, video input devices, and/or any other input device. Output input device(s) such as one or more displays, speakers, printers, and/or any other output device may also be included in the computing device 1730. The one or more input device(s) and one or more output device(s) may be connected to the computing device via a wired connection, wireless connection, or any combination thereof. In one aspect, an input device or an output device from another computing device may be used as the input device(s) or the output device(s) for the computing device 1730.
Components of the computing device 1730 may be connected by various interconnects, such as a bus. Such interconnects may include a Peripheral Component Interconnect (PCI), such as PCI Express, a Universal Serial Bus (USB), firewire (IEEE 1394), an optical bus structure, and the like. In another aspect, components of the computing device 1730 may be interconnected by a network. For example, the memory may be comprised of multiple physical memory units located in different physical locations interconnected by a network.
According to the process 1800, the processing unit 1716 generates 1802 a plot defining a geometric shape and comprising a plurality of points arranged in a matrix, each of the points defining a value for at least two variables and a predicted value of a property of the material. At least one of the at least two variables may be an independent variable and the other variables may be dependent variables. In one aspect, the processing unit 1716 may be configured to generate a predicted value of a property of a material that includes, without limitation, a foam, a coating, an adhesive, a sealant, an elastomer, a sheet, a film, a binder, or any organic polymer. In one aspect, the processing unit 1716 may be configured to generate a model for generating the plot. In one aspect, the processing unit 1716 generates the model based on design of experiments, regression analysis of a data set, an equation, machine learning, or artificial intelligence, and/or any combination thereof.
In one aspect, the processing unit 1716 may be configured to generate a geometric shape in the form of a closed shape in Euclidian space, either in a two-dimensional space or a two-dimensional perspective projection of a three-dimensional shape. The closed shape may define a polygon such as, for example, a triangle, a four-sided polygon, among other polygons, or an ellipse, a circle, among other single sided enclosed shapes. The triangle and each of the points may, for example, define a value for three variables, where each variable is a value for an amount of a component in a composition. The amounts may be expressed as a percentage and a sum of the amounts is 100%. The four-sided polygon and each of the points may, for example, define a value for two variables, where each variable is a value for an amount of a component in a composition, a processing condition, or a value representing an amount of two components of the composition relative to each other.
According to the process 1800, the processing unit 1716 displays 1804, on the output device 1722, a visual representation of the predicted value of the property of the material at each of the plurality of points in a range of indicia, wherein the range of indicia represents a range of predicted values of the property. In various aspects, the visual representation may be a heat map, a color heat map, or a contour map, and/or combinations thereof.
The processing unit 1716 may be configured to display, on the output device 1722, the value of the indicia and property of the material based on a position of a cursor on the visual representation. The processing unit 1716 further may be configured to dynamically update the location of the pointer and an element as the pointer is dragged over the visual representation. The element may be displayed in the form of a numeric value or a descriptor of the property. The element may be displayed in the form of indicia within the range of indicia that represents the predicted value or the descriptor of the property in the visual representation.
According to the process 1800, the processing unit 1716 displays 1806, on the output device 1722, a pointer on the visual representation. In one aspect, the processing unit 1716 may be configured to update a table with current values of the at least two variables and the predicted value of the property based on the location of the pointer on the visual representation. In one aspect, the processing unit 1716 may be configured to generate a set of instructions for producing a product that exhibits the predicted value of the property of the material at one of the plurality of points in the range of indicia.
In one aspect, the processing unit 1716 may be configured to formulate a composition based on the visual representation of the predicted value of the property of the material for at least some of the plurality of points in the range of indicia. In one aspect, the composition may be formulated based on a plurality of properties for at least some of the plurality of points in the range of indicia. In one aspect, the processing unit 1716 may be configured to optimize one or more than one property of the material within one or more than one defined range of indicia. The processing unit 1716 may be configured to display on the output device a gridded region to represent one or more than one optimized region based on the one or more than one defined range of indicia.
In one aspect, the processing unit 1716 may be configured to generate a plurality of plots each defining a geometric shape and each comprising a plurality of points arranged in a matrix, each of the points defining a value for at least two variables and a predicted value of the property of the material for each of the plurality of plots and to display, on the output device 1722, a visual representation of the predicted value of the property of the material for at least some of the plurality of points in a range of indicia, where the range of indicia represents a range of predicted values of the property and to display, on the output device 1722, a pointer on each of the plurality of plots.
As previously discussed, the dataset may be generated by a variety of techniques, including, without limitation, design of experiments, regression analysis of a data set, an equation, machine learning, or artificial intelligence, and/or any combination thereof. In one aspect, a model may be used to generate the predicted values of the properties for a visual representation generated from a design of experiment technique. In other aspects, models for generating predictive values of properties include a statistical analysis of unstructured data, such as that generated by a historian of a distributive control system of a chemical manufacturing plant.
According to the process 1900, the processing unit 1716 generates 1902 a plot defining a triangle and comprising a plurality of points arranged in a matrix, each of the points defining a value for three variables and a predicted value of a property of the material. (See
Examples of a plot defining a triangle include the ternary plots 210, 220, 230, 240, 250, 260 described in connection with the ternary map GUI 209 (
According to the process 1900, the processing unit 1716 displays 1904, on the output device 1722, a color heat map representation of the predicted value of the property of the material for at least some of the plurality of points in a range of colors, wherein the range of colors represents a range of predicted values of the property. Examples of color heat maps include the ternary heat maps 216, 226, 236, 246, 256, 266 described in connection with
In one aspect, the processing unit 1716 is configured to display, on the output device 1722, the variables and predicted property of the material based on a position of a cursor on the heat map 216, 226, 236, 246, 256, 266, 526, 616, 626, 636, 646, 656, 666, and 3004. In one aspect, the processing unit 1716 is configured to dynamically update the location of a pointer and an element as the pointer is dragged over the heat map. The element may be displayed in the form of a numeric value or a descriptor of the property. The element may be displayed in the form of a color within the range of colors that represents the predicted value of the property in the heat map.
According to the process 1900, the processing unit 1716 displays 1906, on the output device 1722, a pointer on the heat map 216, 226, 236, 246, 256, 266, 526, 616, 626, 636, 646, 656, 666, and 3004. An example of a pointer includes the pointers 212, 222, 232, 242, 252, 262 described in connection with
In one aspect, the processing unit 1716 may be configured to formulate a composition based on the color heat map representation of the predicted value of the property of the material for at least some of the plurality of points in the range of colors. The processing unit 1716 may be configured to optimize one or more than one property of the material within one or more than one defined range of colors. The processing unit 1716 may be configured to display, on the output device 1722, a gridded region that represents one or more than one optimized region based on the one or more than one defined range of colors.
In one aspect, the processing unit 1716 is configured to generate a plurality of plots each defining a triangle shape and each comprising a plurality of points arranged in a matrix, each of the points defining a value for at least two variables and a predicted value of the property of the material for each of the plurality of plots; display, on the output device 1722, a visual representation of the predicted value of the property of the material for at least some of the plurality of points in a range of colors, where the range of colors represents a range of predicted values of the property; and display a pointer on each of the plurality of plots.
As previously discussed, the dataset may be generated by a variety of techniques, including, without limitation, design of experiments, regression analysis of a data set, an equation, machine learning, or artificial intelligence, and/or any combination thereof. In one aspect, a model may be used to generate the predicted values of the properties for a visual representation generated from a design of experiment technique. In other aspects, models for generating predictive values of properties include a statistical analysis of unstructured data, such as that generated by a historian of a distributive control system of a chemical manufacturing plant.
According to the process 2000, the processing unit 1716 generates 2002 a plot defining a four-sided polygon and comprising a plurality of points arranged in a matrix, each of the points defining a value for at least two variables and a predicted value of the property of the material. (See
Examples of a plot defining a four-sided polygon include the square plots 1020-1031, 3102 described in connection with
According to the process 2000, the processing unit 1716 displays 2004, on the output device 1722, a color heat map representation of the predicted value of the property of the material for at least some of the plurality of points in a range of colors, wherein the range of colors represents a range of predicted values of the property. Examples of color heat maps include the square plot heat maps 1068-1079, 3104 described in connection with
In one aspect, the processing unit 1716 is configured to display, on the output device 1722, the value of the predicted property of the material based on a position of a cursor on the heat map 1068-1079, 3104. In one aspect, the processing unit 1716 is configured to dynamically update the location of the pointer and an element as the pointer is dragged over the heat map 1068-1079, 3104. The element may be displayed in the form of numeric value or a descriptor of the property. The element may be displayed in the form of a color within the range of colors that represents the predicted value of the property in the heat map 1068-1079, 3104.
According to the process 2000, the processing unit 1716 displays 2006, on the output device 1722, a pointer on the heat map 1068-1079, 3104. An example of a pointer includes the pointers 1056-1067, 3106 described in connection with
In one aspect, the processing unit 1716 may be configured to formulate a composition based on the color heat map 1068-1079, 3104 representation of the predicted value of the property of the material for at least some of the plurality of points in the range of colors. The processing unit 1761 may be configured to optimize one or more than one property of the material within one or more than one defined range of colors. The processing unit 1761 may be configured to display, on the output device 1722, a gridded region that represents one or more than one optimized region based on the one or more than one defined range of colors.
In one aspect, the processing unit 1716 is configured to generate a plurality of plots each defining a four-sided polygon shape and each comprising a plurality of points arranged in a matrix, each of the points defining a value for at least two variables and a predicted value of the property of the material for each of the plurality of plots; display, on the output device, a visual representation of the predicted value of the property of the material for at least some of the plurality of points in a range of colors, where the range of colors represents a range of predicted values of the property; and display a pointer on each of the plurality of plots.
In some aspects, a digital formulation service is provided for generating optimized material configurations, both in types of materials and cost. A computerized system may be configured to provide a digital formulation service module that allows a user to generate a custom material configuration based on a specified constraint, such as cost or performance. The digital formulation service may provide a recommended material configuration that satisfies the specified constraint. The digital formulation service module may be an augmented or supplemental service with the other user interfaces described herein, such as those described in
In some aspects, the digital formulation service module 4405 may be configured to generate a material configuration, such as a custom coating, by optimizing coating formulation based on performance. In this example, the user or customer 4400 may specify one or more criteria that one or more of the particular qualities of a coating must satisfy. For example, the user may specify that the custom coating must possess at least a minimum amount of smoothness, or must resist DEET at a particular minimum level. The digital formulation service module 4405 is then configured to analyze all known recipes, in some cases using just default ingredients that satisfy the performance constraint(s). The module 4405 then may provide a recommendation at the least expensive cost. The known recipes may be based on empirical research and tabulation that are stored in a database.
In some aspects, the digital formulation service module 4405 may also be configured to provide optimization configurations using substitute ingredients. For example, if a user 4400 instructs the service module 4405 to generate a custom coating by optimizing the formulation based on performance, the user 4400 may also specify to analyze all known recipes to satisfy the performance constraint using default ingredients as well as all permutations of substitute ingredients. The substitute ingredients may be based on empirical research and knowledge of physical properties that are stored in a database.
In other cases, the customer 4400 may simply supply to the digital formulation service module 4405 the specifications for performance with the full recipe and workup information for how to generate the desired custom coating. From here, the digital formulation service module 4405 may determine the most efficient or effective method for obtaining the materials. For example, the ingredients may come from one or more sources, and it may not be relevant to the customer 4400 what the sources are, so long as the proper ingredients are obtained. Alternatively, the digital formulation service 4405 may allow for the customer to specify the sources for obtaining the ingredients.
Referring to
In another scenario, in the case where the customer 4400 may specify the performance of a coating but where the recipe information for the exact type of materials or ingredients is not specified, the digital formulation service module 4405 may complete the order by performing optimization calculations to determine the best types of materials that satisfy the performance constraints. The interfaces described in
Referring to
Referring to
In some aspects, in another variation of the neutral or hybrid platform, the digital formulation service 4405 may be configured to send orders to either the first or second supplier 4500 and 4605 based on a competitive bidding process undertaken by the first and second (and possibly additional) suppliers 4500 and 4605. The bidding system may be setup as an automatic bidding system, where analysts from the different suppliers may input automatic bidding rules for various types of recipes or materials. The bidding process may be resolved automatically as part of the process to complete the customer order. In other cases, the bidding process may be conducted more manually, and the digital formulation service 4405 may be configured to provide the forum to conduct this process. The winning bid may be the bid that offers to fulfill the order with the lowest cost to the customer.
Referring to
Referring to
Various operations of aspects are provided herein. In one aspect, one or more of the operations described may constitute computer readable instructions stored on one or more computer readable media, which if executed by a computing device, will cause the computing device to perform the operations described. The order in which some or all of the operations are described should not be construed as to imply that these operations are necessarily order dependent. Alternative ordering will be appreciated by one skilled in the art having the benefit of this description. Further, it will be understood that not all operations are necessarily present in each aspect provided herein. Also, it will be understood that not all operations are necessary in some aspects.
Further, unless specified otherwise, “first,” “second,” and/or the like are not intended to imply a temporal aspect, a spatial aspect, an ordering, etc. Rather, such terms are merely used as identifiers, names, etc. for features, elements, items, etc. For example, a first object and a second object generally correspond to object A and object B or two different or two identical objects or the same object.
Moreover, “exemplary” is used herein to mean serving as an example, instance, illustration, etc., and not necessarily as advantageous. As used herein, “or” is intended to mean an inclusive “or” rather than an exclusive “or”. In addition, “a” and “an” as used in this application are generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form. Also, at least one of A and B and/or the like generally means A or B and/or both A and B. Furthermore, to the extent that “includes”, “having”, “has”, “with”, and/or variants thereof are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising”.
Also, although the disclosure has been shown and described with respect to one or more implementations, equivalent alterations and modifications will occur to others skilled in the art based upon a reading and understanding of this specification and the annexed drawings. The disclosure includes all such modifications and alterations and is limited only by the scope of the following claims. In particular regard to the various functions performed by the above described components (e.g., elements, resources, etc.), the terms used to describe such components are intended to correspond, unless otherwise indicated, to any component which performs the specified function of the described component (e.g., that is functionally equivalent), even though not structurally equivalent to the disclosed structure. In addition, while a particular feature of the disclosure may have been disclosed with respect to only one of several implementations, such feature may be combined with one or more other features of the other implementations as may be desired and advantageous for any given or particular application.
Various aspects of the subject matter described herein are set out in the following numbered examples:
Example 1. A method of producing a graphical depiction of a predicted value of a property of a material, the method comprising: generating, by a processing unit, a plot defining a geometric shape and comprising a plurality of points arranged in a matrix, each of the points defining a value for at least two variables and a predicted value of a property of the material; displaying, on an output device, a visual representation of the predicted value of the property of the material for at least some of the plurality of points in a range of indicia, wherein the range of indicia represents a range of predicted values of the property; and displaying, on the output device, a pointer on the visual representation.
Example 2. The method of Example 1, wherein displaying, on the output device, comprises displaying, on the output device, the visual representation of the predicted value of the property of the material at each of the plurality of points in the range of indicia.
Example 3. The method of one or more of Example 1 to Example 2, further comprising displaying, on the output device, the value of the indicia and the predicted value of the property of the material based on a position of a cursor on the visual representation.
Example 4. The method of one or more of Example 1 to Example 3, further comprising dynamically updating the location of the pointer and an element as the pointer is dragged over the visual representation.
Example 5. The method of Example 4, wherein the element comprises a numeric value or a descriptor of the property.
Example 6. The method of Example 5, wherein the element comprises indicia within the range of indicia that represents the predicted value or the descriptor of the property in the visual representation.
Example 7. The method of one or more of Example 1 to Example 6, wherein at least one of the at least two variables is an independent variable.
Example 8. The method of one or more of Example 1 to Example 7, wherein the geometric shape defines a closed shape in Euclidian space.
Example 9. The method of Example 8, wherein the closed shape defines a polygon.
Example 10. The method of Example 9, wherein the polygon is a triangle or a four-sided polygon.
Example 11. The method of Example 10, wherein the polygon is a triangle and each of the points defines a value for three variables, wherein each variable represents a value for an amount of a component in a composition.
Example 12. The method of Example 11, wherein the amounts are expressed as a percentage and a sum of the amounts is 100%.
Example 13. The method of one or more of Example 10 to Example 12, wherein the polygon is a four-sided polygon and each of the points defines a value for two variables, wherein each variable is a value representing an amount of a component in a composition, a value for a processing condition, or a value representing an amount of two components of the composition relative to each other.
Example 14. The method of one or more of Example 8 to Example 13, wherein the closed shape defines an ellipse or a circle.
Example 15. The method of one or more of Example 8 to Example 14, wherein the closed shape defines either a two-dimensional space or a two-dimensional perspective projection of a three-dimensional shape.
Example 16. The method of one or more of Example 1 to Example 15, further comprising formulating, by the processing unit, a composition based on the visual representation.
Example 17. The method of Example 16, further comprising formulating, by the processing unit, the composition based on a plurality of predicted values of a property.
Example 18. The method of one or more of Example 16 to Example 17, further comprising optimizing, by the processing unit, one or more than one predicted property of the material within one or more than one defined range of indicia.
Example 19. The method of Example 18, further comprising displaying, on the output device, a gridded region that represents one or more than one optimized region based on the one or more than one defined range of indicia.
Example 20. The method of one or more of Example 1 to Example 19, further comprising updating, by the processing unit, a table with current values of the at least two variables and the predicted value of the property based on the location of the pointer on the visual representation.
Example 21. The method of Example 20, further comprising generating, by the processing unit, a set of instructions for producing a product based on the predicted value of the property of the material at one of the plurality of points in the range of indicia.
Example 22. The method of one or more of Example 1 to Example 21, wherein the material is a foam, a coating, an adhesive, a sealant, an elastomer, a sheet, a film, a binder, or any organic polymer.
Example 23. The method of one or more of Example 1 to Example 22, further comprising: generating, by the processing unit, a plurality of plots each defining a geometric shape and each comprising a plurality of points arranged in a matrix, each of the points defining a value for at least two variables and a predicted value of the property of the material for each of the plurality of plots; displaying, on the output device, a visual representation of the predicted value of the property of the material for at least some of the plurality of points in a range of indicia, wherein the range of indicia represents a range of predicted values of the property; and displaying a pointer on each of the plurality of plots.
Example 24. The method of Example 23, further comprising generating, by the processing unit, a plot based on a model.
Example 25. The method of Example 24, wherein the model is generated based on design of experiments, regression analysis of a data set, an equation, machine learning, or artificial intelligence, and/or any combination thereof.
Example 26. The method of one or more of Example 1 to Example 25, wherein the visual representation is a heat map, a color heat map, or a contour map.
Example 27. The method of Example 16, further comprising: generating, by the processing unit, a recipe for producing the composition that satisfies a specified user constraint; and transmitting the recipe to one or more suppliers to obtain ingredients sufficient to produce the material and satisfy the specified user constraint.
Example 28. The method of Example 27, wherein transmitting the recipe to the one or more suppliers is based on determining a supplier that can obtain the ingredients at the lowest total cost.
Example 29. The method of Example 27, wherein transmitting the recipe to the one or more suppliers is based on conducting a competitive bidding process between two or more suppliers.
Example 30. The method of Example 27, wherein transmitting the recipe to the one or more suppliers is based on determining which suppliers are capable of obtaining the ingredients sufficient to fulfill the recipe.
Example 31. A method of producing a graphical depiction of a predicted value of a property of a material, the method comprising: generating, by a processing unit, a plot defining a triangle and comprising a plurality of points arranged in a matrix, each of the points defining a value for three variables and a predicted value of a property of the material; displaying, on an output device, a color heat map representation of the predicted value of the property of the material for at least some of the plurality of points in a range of colors, wherein the range of colors represents a range of predicted values of the property; and displaying, on the output device, a pointer on the heat map.
Example 32. The method of Example 31, wherein displaying, on the output device, comprises displaying, on the output device, the color heat map representation of the predicted value of the property of the material at each of the plurality of points in the range of colors.
Example 33. The method of Example 32, further comprising displaying, on the output device, the value of the variables and the predicted value of the property of the material based on a position of a cursor on the heat map.
Example 34. The method of one or more of Example 32 to Example 3, further comprising dynamically updating the location of the pointer and an element as the pointer is dragged over the heat map.
Example 35. The method of Example 34, wherein the element comprises a numeric value or a descriptor of the property.
Example 36. The method of one or more of Example 34 to Example 35, wherein the element comprises a color within the range of colors that represents the predicted value of the property in the heat map.
Example 37. The method of one or more of Example 32 to Example 36, wherein at least one of the three variables is an independent variable.
Example 38. The method of one or more of Example 32 to Example 37, wherein each of the points of the triangle defines a value for the three variables, wherein each of the three variables represents a value for an amount of a component in a composition.
Example 39. The method of Example 38, wherein the amounts are expressed as a percentage and a sum of the amounts is 100%.
Example 40. The method of one or more of Example 32 to Example 39, further comprising formulating, by the processing unit, a composition based on the color heat map representation.
Example 41. The method of Example 40, further comprising optimizing, by the processing unit, one or more than one property of the material within one or more than one defined range of colors.
Example 42. The method of Example 41, further comprising displaying, on the output device, a gridded region that represents one or more than one optimized region based on the one or more than one defined range of colors.
Example 43. The method of one or more of Example 32 to Example 42, further comprising updating, by the processing unit, a table with current values of the three variables and the predicted value of the property based on a location of the pointer on the heat map.
Example 44. The method of Example 41, further comprising generating, by the processing unit, a set of instructions for producing a product based on the predicted value of the property of the material at one of the plurality of points in the range of colors.
Example 45. The method of one or more of Example 32 to Example 44, wherein the material is a coating, an adhesive, a sealant, an elastomer, a sheet, a film, a binder, or any organic polymer.
Example 46. The method of one or more of Example 32 to Example 45, further comprising: generating, by the processing unit, a plurality of plots each defining a triangle and each comprising a plurality of points arranged in a matrix, each of the points defining a value for at least two variables and a predicted value of the property of the material for each of the plurality of plots; displaying, on the output device, a visual representation of the predicted value of the property of the material for at least some of the plurality of points in a range of colors, wherein the range of colors represents a range of predicted values of the property; and displaying a pointer on each of the plurality of plots.
Example 47. The method of one or more of Example 32 to Example 46, further comprising generating, by the processing unit, a plot based on a model.
Example 48. The method of Example 47, wherein the model is generated based design of experiments, regression analysis of a data set, an equation, machine learning, or artificial intelligence, and/or any combination thereof.
Example 49. The method of Example 40, further comprising: generating, by the processing unit, a recipe for producing the composition that satisfies a specified user constraint; and transmitting the recipe to one or more suppliers to obtain ingredients sufficient to produce the material and satisfy the specified user constraint.
Example 50. The method of Example 49, wherein transmitting the recipe to the one or more suppliers is based on determining a supplier that can obtain the ingredients at the lowest total cost.
Example 51. The method of Example 49, wherein transmitting the recipe to the one or more suppliers is based on conducting a competitive bidding process between two or more suppliers.
Example 52. The method of Example 49, wherein transmitting the recipe to the one or more suppliers is based on determining which suppliers are capable of obtaining the ingredients sufficient to fulfill the recipe.
Example 53. A method of producing a graphical depiction of a predicted value of a property of a material, the method comprising: generating, by a processing unit, a plot defining a four-sided polygon and comprising a plurality of points arranged in a matrix, each of the points defining a value for at least two variables and a predicted value of the property of the material; displaying, on an output device, a color heat map representation of the predicted value of the property of the material for at least some of the plurality of points in a range of colors, wherein the range of colors represents a range of predicted values of the property; and displaying, on the output device, a pointer on the heat map.
Example 54. The method of Example 53, wherein displaying, on the output devices, comprises displaying, on the output device, the color heat map representation of the predicted value of the property of the material at each of the plurality of points in the range of colors.
Example 55. The method of Example 54, further comprising displaying, on the output device, the predicted value of the property of the material based on a position of a cursor on the heat map.
Example 56. The method of one or more of Example 54 to Example 55, further comprising dynamically updating the location of the pointer and an element as the pointer is dragged over the heat map.
Example 57. The method of Example 56, wherein the element comprises a numeric value or a descriptor of the property.
Example 58. The method of one or more of Example 56 to Example 57, wherein the element comprises a color within the range of colors that represents the predicted value of the property in the heat map.
Example 59. The method of one or more of Example 54 to Example 58, wherein at least one of the two variables is an independent variable.
Example 60. The method of one or more of Example 54 to Example 59, wherein each of the at least two variables is a value for an amount of a component in a composition, a value for a processing condition, or a value representing an amount of two components of the composition relative to each other.
Example 61. The method of one or more of Example 54 to Example 60, further comprising formulating, by the processing unit, a composition based on the color heat map representation.
Example 62. The method of Example 61, further comprising optimizing, by the processing unit, one or more than one property of the material within one or more than one defined range of colors.
Example 63. The method of Example 62, further comprising displaying, on the output device, a gridded region that represents one or more than one optimized region based on the one or more than one defined range of colors.
Example 64. The method of one or more of Example 54 to Example 63, further comprising updating, by the processing unit, a table with current values of the at least two variables and the predicted value of the property based on a location of the pointer on the heat map.
Example 65. The method of Example 64, further comprising generating, by the processing unit, a set of instructions for producing a product based on the predicted value of the property of the material at one of the plurality of points in the range of colors.
Example 66. The method of one or more of Example 54 to Example 65, wherein the material is a polyurethane foam.
Example 67. The method of Example 54, further comprising: generating, by the processing unit, a plurality of plots each defining a four-sided polygon shape and each comprising a plurality of points arranged in a matrix, each of the points defining a value for at least two variables and a predicted value of the property of the material for each of the plurality of plots; displaying, on the output device, a visual representation of the predicted value of the property of the material for at least some of the plurality of points in a range of colors, wherein the range of colors represents a range of predicted values of the property; and displaying a pointer on each of the plurality of plots.
Example 68. The method of one or more of Example 54 to Example 67, further comprising generating, by the processing unit, a plot based on a model.
Example 69. The method of Example 68, wherein the model is generated based design of experiments, regression analysis of a data set, an equation, machine learning, or artificial intelligence, and/or any combination thereof.
Example 70. The method of Example 61, further comprising: generating, by the processing unit, a recipe for producing the composition that satisfies a specified user constraint; and transmitting the recipe to one or more suppliers to obtain ingredients sufficient to produce the material and satisfy the specified user constraint.
Example 71. The method of Example 70, wherein transmitting the recipe to the one or more suppliers is based on determining a supplier that can obtain the ingredients at the lowest total cost.
Example 72. The method of Example 70, wherein transmitting the recipe to the one or more suppliers is based on conducting a competitive bidding process between two or more suppliers.
Example 73. The method of Example 70, wherein transmitting the recipe to the one or more suppliers is based on determining which suppliers are capable of obtaining the ingredients sufficient to fulfill the recipe.
Example 74 is at least one computer readable medium comprising instructions that, when executed, implement a method as described in one or more of Example 1 to Example 30.
Example 75 is at least one computer readable medium comprising instructions that, when executed, implement a method as described in one or more of Example 31 to Example 52.
Example 76 is at least one computer readable medium comprising instructions that, when executed, implement a method as described in one or more of Example 53 to Example 72.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2018/051430 | 9/18/2018 | WO | 00 |
| Number | Date | Country | |
|---|---|---|---|
| 62560262 | Sep 2017 | US | |
| 62608627 | Dec 2017 | US |
