Patent Application
20240399450
The disclosure relates to processes that involve casting an object in a cavity in a mold. In particular, the disclosure relates to improving such casting processes by predicting local properties of the casting through integrated casting process simulation for optimizing cast object design during design phase and applying statistical analysis for optimizing mold and tool design and casting process conditions during production.
Casting is a manufacturing process by which a liquid material is poured into a mold, which contains a hollow cavity of the desired shape. Dependent on local heat exchange with its environment (e.g. mold, air) the liquid material is cooling down and solidifying in accordance with material specific phase transformation physics. The solidified part is also known as a casting, which is ejected or broken out of the mold to complete the process. Casting is most often used for producing complex shapes that would otherwise be difficult or uneconomical to be produced by other methods. If the casting material is a metal, this metal material is heated-up to a pouring temperature that is higher than the melting temperature and poured into the mold, which may also include runners and risers that enable a controlled filling and solidification of the casting. The metal is then cooling in the mold, the metal solidifies, and the solidified part (the casting) is removed from the mold at a process and material specific metal temperature or process time. Subsequent operations remove excess material required by the casting process (such as the runners and risers).
A simulation of the metal casting process may use multiple techniques including numerical methods to calculate cast component quality considering mold filling, solidification and further cooling, and may provide a quantitative prediction of casting mechanical properties at each point in time during an entire casting process and additional processing steps such as heat treatment. Thus, such simulations can describe the quality of a cast component up-front before production starts. The casting methoding can then be designed with respect to the required component quality and properties. This has benefits beyond a reduction in pre-production sampling, as the precise layout of the complete casting system also leads to energy, material, and tooling savings. The simulation may support product design during design and/or manufacturing stage in determining the casting method, including mold and tool making, as well as subsequent heat treatment and finishing. This may save costs, resources and time along the entire casting manufacturing route.
In particular, for the design of castings for a given part a specific service life needs to be considered. For this, typically the mechanical properties of the material based on given standards are used. Casting properties are related to the basic material and the process conditions for producing the part. In general, there are two different main parameters that influence the final mechanical properties: 1. The local microstructure in the cast part that is determined by the alloy composition, the metal treatment and the local solidification conditions; 2. the presence or absence of local defects that depend on the process layout and the process conditions. Both the local microstructure and the local defects determine the final properties of the part.
During casting design, certain quality requirements are specified to be met during manufacture. These requirements are expressed in terms of property levels for specific areas of the casting that are meeting the expected service loads. This is done to minimize the risk of failure in service. Since the final process conditions are not known during design, the relationship between process, microstructure and final properties is considered a risk. This uncertainty results in a safety factor that is applied to the part design and reduces the optimum potential of the cast material. The result is a part that is usually too heavy for the required service loads.
To realize more optimized lightweight casting designs, taking advantage of the consideration of locally varying properties resulting from a process simulation and use in stress analysis is proposed. This requires that the expected minimum local properties in the casting can be statistically proven at the design stage without experimental validation.
In addition, when the casting part design is fixed, decisions regarding the applied methoding and mold or tool design and decisions on settings for robust process conditions are required. This increases the variety of parameters influencing part quality and related mechanical properties significantly. To realize robust mold and tool designs and process conditions, statistically proven information is needed regarding the influencing parameters on the expected final part properties. The objective is to determine optimal nominal operating points and robust process windows for achieving reliably the required mechanical properties of the part relative to the specification. This information allows a molding and process design based on expected part quality. It also allows manufacturing to establish operating conditions that will help ensure a seamless and short ramp-up phase. It also allows the manufacturer to provide quantitative information on the expected reliability of the parts before the first casting is produced.
Some commercially available simulation software solutions can provide means for predicting local mechanical properties of an object to be cast to a limited extent, such as providing information about possible porosity formation during the metal casting process, or about mechanical properties based on the expected microstructure. However, these known simulation software solutions have not yet been able to provide robust statistical evidence of the varying local properties of a given part area of an object to be cast in a way that the obtained information can be used in order to design robust cast parts as well as to optimize the overall manufacturing process.
It is an object to provide an improved method and system for improving a process of casting an object by predicting the probability distribution of local properties of the casting that overcomes or at least reduces the problems mentioned above.
The foregoing and other objects are achieved by the features of the independent claims. Further implementation forms are apparent from the dependent claims, the description and the figures.
According to a first aspect, there is provided a computer-implemented method for improving a process of casting an object by predicting the probability distribution of local mechanical properties of the casting, the method comprising obtaining input variables defining at least one of the designs of the casting, at least one of the designs of the molding or tooling to be used for casting, the composition of the casting material, any pre-treatment of the casting material, and casting and post treatment process conditions; obtaining a process variability parameter defining the variability of said process conditions in terms of probability distribution; executing a simulation of the casting process on a computing device using a numerical model of the casting process, the numerical model being configured to predict local microstructures of each part of the casting and local defects mapped to the design of the casting, as a function of the input variables; calculating local microstructure based mechanical properties of each part of the casting as a function of the local microstructures and calculating a probability distribution of the local microstructure based mechanical properties as a function of said the variability parameter; calculating a local damage factor for each part of the casting as a function of the local defects and calculating a probability distribution of the local damage factors as a function of the process variability parameter for representing probability and magnitude of detrimental effects of the local defects on local material performance at each part of the casting; and calculating adjusted local mechanical properties by applying respective local damage factors to the local microstructure based mechanical properties at each part of the casting representing local weakening in the respective material performance; and calculating a probability distribution of the adjusted local mechanical properties at each part of the casting as a combined function of the probability distribution of local microstructure based mechanical properties and the probability distribution of local damage factors.
The first aspect provides an improved method for predicting casting process outcomes by using the simulated information of microstructures and local defects in a probabilistic manner from a single simulation for providing a robust statistical analysis of local mechanical properties of the object to be cast, taking into account the impact of the probability and distribution of both the predicted local defects and local microstructures. This approach is different from the known prior art methods that aim for a deterministic prediction of microstructures and mechanical properties, where any statistical analysis is generally based on repeated deterministic simulations. This is achieved by the claimed method using a process variability value as input parameter to consider the variation in the process as such as well as the scatter that is evident in any repeated testing. The method includes calculating local damage factors for each part of the casting representing different detrimental effects of the predicted local defects on local material performance and variability, thereby increasing the accuracy of the prediction of local weakening in the respective mechanical performance at each part caused by these local defects.
In a possible implementation form of the first aspect, executing the simulation of the casting process comprises using an integrated microstructure prediction model of mold filling and solidification during casting.
In an embodiment, the integrated microstructure prediction model comprises a numerical model integrating equations defining phase formation, solidification kinetics, segregation, latent heat and solidification, and thermal equilibrium of the mold filling and solidification processes.
In an embodiment, the integrated microstructure prediction model provides output about local microstructures that comprise at least one of local information about grain size, phase distributions for primary and intermetallic phases, and related phase sizes of a given part of the casting.
In an embodiment, the integrated microstructure prediction model calculates the local microstructure based mechanical properties and related local stress-strain curves describing the mechanical performance, such as yield strength, tensile strength and elongation of a given part of the casting.
In a further possible implementation form of the first aspect, calculating the adjusted local mechanical properties comprises applying respective local damage factors to local stress-strain curves at each part of the casting to calculate a local reduction in the respective mechanical performance at each part.
In an embodiment, calculating adjusted local mechanical properties comprises determining a simulated local defect map by mapping the simulated local defects to the geometrical model of the casting, defining one or more evaluation areas in the geometrical model that may reflect virtual local tensile strength tests on the local defect map, and applying respective local defects to the local tensile strength tests to determine local stress-strain curves.
In a further possible implementation form of the first aspect the predicted local defects comprise attributes of a given part of the casting that can reduce the material performance locally, such as entrapped air and gases, cold shuts, weld lines, porosity, and other detrimental inclusions, and the local damage factor is calculated as a function of these attributes, such as defect size, shape or distribution.
In an embodiment the local damage factor for each part of the casting is calculated as a function of the size and other characteristics of the local defects in a given sampling area.
Each sampling area may correspond to sub-regions of the casting based on a sampling criteria, e.g. reflecting the dimensions of tensile strength samples, for determining the respective damage factors.
According to a second aspect, there is provided a computer-implemented method for improving a process of casting by obtaining a set of fixed input variables defining a nominal operating point for a casting process by nominal process a process variability parameter conditions; obtaining defining the variability of the nominal process conditions in terms of probability distribution;
In an embodiment the evaluation area corresponds to the entire geometrical model of the casting.
In another embodiment each sampling area may correspond to sub-regions of the casting based on a sampling criteria, e.g. reflecting the dimensions of tensile strength samples, for determining the respective damage factors.
In another embodiment each evaluation area corresponds to sub-regions of the casting based on different respective load levels to be applied.
In an embodiment the different load levels correspond to static, dynamic, and/or crash-related load cases.
In an embodiment the method further comprises obtaining a set of fixed input variables defining nominal process conditions based on a nominal operating point.
In one embodiment the set of fixed input variables define composition of the casting material, current part design, tool design to be used for casting, and nominal process conditions for filling and cooling.
In an embodiment determining the design capability comprises determining the adjusted local tensile strength in each evaluation area and applying a statistical assessment on each evaluation area based on local stress-strain curves corresponding to the respective local tensile strength samples defining variations in adjusted local mechanical properties based on variations in one of the set of input variables.
In a further possible implementation form of the first aspect determining the design capability for the at least one evaluation area comprises determining a minimum of expected local mechanical properties for the at least one evaluation area of the casting based on the probability distribution of adjusted local mechanical properties at every point of the evaluation area.
In a further possible implementation form of the first aspect the method further comprises mapping at least one of the determined minimum of expected local mechanical properties, or the distribution and/or probability distribution of adjusted local mechanical properties onto a computer-aided engineering (CAE) model, such as a Finite Element model, defining the geometry of the casting and the at least one evaluation area; analyzing the CAE model, such as using Finite Element Analysis (FEA) for evaluating mechanical performance based on user requirements defining local loads to be applied to the at least one evaluation area.
In an embodiment the method further comprises adjusting the CAE model and/or determining a set of adjusted input variables to fulfil the user requirements.
In a further possible implementation form of the second aspect the method further comprises obtaining a parametrized set of input variables defining variations in a plurality of input variables; executing a Design of Experiments by executing multiple simulations of the casting process using the numerical model of the casting process for possible combinations of the parametrized set of input variables to predict variations in local microstructures and variations in local defects at each part of the casting; and determining a statistical strength assessment for the parametrized set of input variables in each part of the casting by calculating distribution of local variations in probability microstructure based mechanical properties at each part of the casting function of the variations in local microstructures and the process variability parameter; calculating variations in probability distribution of local damage factors at each part of the casting as a function of the variations in local defects and the process variability parameter; and calculating variations in probability distribution of adjusted local mechanical properties as a combined function of the variations in probability distribution of local microstructure based mechanical properties and the variations in probability distribution of local damage factors.
In a further possible implementation form of the first aspect the method comprises determining a local defect probability for each of the parametrized set of input variables in each part of the casting as a function the variations in local defects and the process variability parameter.
In a further possible implementation form of the first aspect the method comprises a full factorial assessment by executing simulations of the casting process for all possible combinations of the parametrized set of input variables.
In another possible implementation form of the first aspect the method comprises a limited assessment by applying a statistically supported reduction of the number of the parametrized set of input variables and executing a number of simulations of the casting process for a reduced parametrized set of input variables and a set of fixed input variables.
In a further possible implementation form of the first aspect the method further comprises determining information about process capability for the parametrized set of input variables for defining a statistical robustness analysis of the casting process by determining a main effect and correlation diagram describing the impact of varied input parameters on the cast part quality, an expected minimum of mechanical properties and a distribution of mechanical properties of the casting based on predefined quality criteria, and/or a robust process window for the parametrized set of input variables.
In an embodiment determining the process capability for the parametrized set of input variables comprises calculating a normal distribution of local mechanical properties for each part of the casting.
In an embodiment determining the robust process window comprises adjusting a process window defined by the parametrized set of input variables by reducing process variations for process robustness.
In an embodiment determining the robust process window comprises adjusting an operating point defined by the parametrized set of input variables to determine an optimal operating point.
In a further possible implementation form of the first aspect the method further comprises assessing the impact of variations in the parametrized set of input variables on the expected part quality and related mechanical properties using the main effect and correlation diagram.
In a further possible implementation form of the first aspect the method further comprises comparing at the minimum of expected mechanical properties and/or the distribution of expected mechanical properties to predefined user requirements regarding quality criteria of the casting; and determining an adjusted parametrized set of input variables to meet the user requirements.
In a further possible implementation form of the first aspect the method further comprises obtaining at least one of the set of fixed input variables or parametrized set of input variables via an input-output interface of the computing device; and outputting the determined result of at least one step of the performed method to a display of the computing device.
According to a second aspect, there is provided a computer-based system comprising an input-output interface; a display; a non-transitory machine-readable storage medium including a computer program product; and at least one processor operable to execute the program product, interact with the input-output interface and the display, and perform operations according to the methods of any one of the possible implementation forms of the first aspect.
According to a third aspect, there is provided a computer program product, encoded on a non-transitory machine-readable storage medium, operable to cause a processor to perform operations according to the methods of any one of the possible implementation forms of the first aspect.
These and other aspects will be apparent from the embodiment(s) described below.
In the following detailed portion of the present disclosure, the aspects, embodiments and implementations will be explained in more detail with reference to the example embodiments shown in the drawings, in which:
When designing a new cast part, the focus is on the required functions and the design evaluation is based on the load cases to be expected in service life. The cast part has to fulfil multiple functions. Therefore, diverse load cases will be investigated by CAE using Finite Element Analysis (FEA), which is a computerized method for predicting how a product reacts to real-world forces, vibration, heat, fluid flow, and other physical effects. FEA can show whether a product will break, wear out, or work the way it was designed, and requires either static, dynamic (durability) and/or crash related input for properties. With respect to the material specification, the design process usually relies on available standards for the given material and process. Deviations from accepted standards of the manufactured part are only accepted under quality system considerations, i.e. certain areas of different loads in the part and related acceptable defects are specified that must be met by manufacturing accordingly. This information is part of the specification sheet during procurement. Depending on the company standards safety factors are added that reduce the allowable load of a cast part in addition for safety reasons.
As a result, the cast part is usually over dimensioned for two reasons: on one hand, using only standard data instead of local properties; and on the other hand, using additional safety factors to cope with unknown local properties.
The innovation described in this application uses the predictions from virtual process simulation (material performance and local defects) and predicts local mechanical properties. It allows to associate given load levels of the part with the predicted local properties that can be met based on the given process conditions during the design process of the cast part. During the design process, the safety factors can be reduced and the expected local properties can be taken into account to reduce the weight of the part.
The input for the casting process simulation 2 of casting parts may consist of information regarding the part design, the composition of the casting material chosen, the molding or tooling design, any pre-treatment of the casting material, and casting and post treatment process conditions.
These process parameters are usually nominal and do not consider a scatter or fluctuation inherent in any manufacturing process. The process variability parameter 40 adds a probability distribution describing the reliability of the process anticipated in terms of variability of the process conditions.
As at this stage final decisions on the mold or tooling design and the process conditions have not been done, assumptions will be made based on previous reference examples or best practice.
In addition, certain evaluation areas 13 or zones can be defined that require a certain load level, as shown in more detail in
The numerical models 3 applied for the simulation 2 are describing the filling and the solidification of the part to be cast. An integrated microstructure prediction model 5 can be applied during filling and solidification, shown more detail in
The output of these numerical models is twofold: on one hand, predicted local microstructures 6 describing local information about grain size, phase distributions for primary and intermetallic phases, and related phase sizes of a given part of the object to be cast, as well as related material performances in terms of local microstructure based mechanical properties that describe the fundamental behavior of the material in use.
On the other hand, local defects 7 are also predicted and mapped to the design of the object to be cast, both as a function of the input variables 1. The predicted local defects 7 comprise attributes of a given part of the object to be cast that can reduce the material performance locally, such as entrapped air and gases, cold shuts, weld lines, porosity and other detrimental inclusions.
Based on the assessment of the local microstructures 6 the method can predict local microstructure based mechanical properties 8 based on the local microstructures 6 and related local stress-strain curves 81 as shown in more detail in
The predicted local defects 7 are also assessed in terms of their detrimental effect on the material performance by a local damage factor 9. As outlined before, defects can mean a number of detrimental discrete weaknesses in the matrix such as entrapped air and gases, cold shuts, weld lines, porosity, and other detrimental inclusions or even a combination of the above. The local damage factor 9 can be a function of defect size, shape, distribution, or other characteristics of the predicted individual defects 7.
To make sure that the predicted defects represent the probability distribution for a given sampling area (e.g. the dimensions of a tensile strength sample) a statistical analysis of all defects in a sampling area is done. The calculated defect probability distribution is used to determine the damage factor for each sampling area.
The local damage factor 9 is then be applied locally to the predicted microstructure stress-strain curves 81 reducing the optimal material performance to lower values of tensile strength and ductility and thereby arrive at a local weakening in the respective mechanical performance at any location of the part.
The final output of the illustrated flow is an integral assessment of the part in terms of a probability distribution of adjusted local mechanical properties 10 at each part of the casting as a function of the process variability parameter 40 and the input variables 1, with intermediate probability distribution calculations of the local microstructure based mechanical properties 8 and local damage factors 9.
The above methodology can be applied to a single simulation 2 for a given part and process condition, providing statistically secured adjusted local mechanical properties 10 to be used in part design to avoid unnecessary safety margins that lead to increased part weight.
As illustrated in
The output of this numerical model are local microstructures (such as phase amounts and size of primary and intermetallic phases) and provides information about local microstructures 6 and local defects 7 occurring in the cast part.
As
In particular, the local microstructure based mechanical properties 8 are calculated based on the local microstructures 6 and related local stress-strain curves 81 describing the associated material performance, to which the local damage factors 9 are applied at each part of the object to be cast to calculate a local weakening in the respective mechanical performance at each part of the object.
The method uses the predicted information to calculate adjusted local mechanical properties 10 in the entire cast part for the given conditions, defined in the form of adjusted values of stress-strain curves. These are illustrated in
The above methodology can be applied to a single simulation for a given object and process condition when combined with a process variability value 40 as defined before, thus providing statistically secured local mechanical properties to be used in part design to avoid unnecessary safety margins that lead to increased part weight.
The user can also decide in which sections of the part, so-called evaluation areas (EV) 13 the analysis will be performed, wherein the analysis of all locations in the respective evaluation areas 13 will result in statistically proven minimum mechanical properties that can be used as input for further analysis for part performance, such as using FEA.
In this case the required functions and the layout of the object to be cast are evaluated based on the expected load cases. The object to be cast has to fulfil multiple functions and therefore, diverse load cases will be investigated by FEA that require either static, dynamic (durability) and/or crash related input for properties.
The method thus comprises selecting at least one evaluation area 13 (in this case EV 1, EV 2, EV 3) of the object to be cast according to different user requirements 11, in this case different local loads 11 expected in different parts of the object based on a so-called zone model that assigns specific zones to a given stress level to be considered. However, it is also possible that a single evaluation area 13 corresponds to the entire geometrical 3D-model of the object to be cast.
As described before, at this stage preliminary input for a casting process simulation 2 of the cast part may be defined, consisting of information about the current part design, the composition of the selected material, a first mold or tool design and nominal process conditions for filling and cooling defined as set of fixed input variables 12. As at this stage final decisions on the molding or tooling design and the process conditions have not been done, assumptions will be made based on previous reference examples or best practice. A process variability parameter 40 is further defined in addition to the set of fixed input variables 12, adding a probability distribution describing the reliability of the process anticipated.
Subsequently the method automatically evaluates the pre-defined evaluation areas 13 in the part using the probability distribution of adjusted local mechanical properties 10 and provides statistically based information about the minimum properties and the distribution of properties that can be expected.
In an example, this is done by executing a simulation 2 of the casting process using the numerical model 3 of the casting process to predict a probability distribution of local microstructures 6 and a probability distribution of local defects 7 of each part of the evaluation areas (EV1, EV2, EV3) 13; calculating a probability distribution of local microstructure based mechanical properties 8 at each part of the evaluation areas 13 as a function of the probability distribution of local microstructures 6; calculating a probability distribution of local damage factors 9 at each part of the evaluation area 13 as a function of the probability distribution of local defects 7; and then calculating a probability distribution of adjusted local mechanical properties 10 at each part of the evaluation area 13 as a combined function of the probability distribution of local microstructure based mechanical properties 8 and the probability distribution of local damage factors 9.
The method comprises the evaluation of the above information in the different evaluation areas or throughout the entire object to be cast by statistical means. Output of this statistical evaluation is a design capability 14 comprising a set of minimal local mechanical properties 101 for any evaluation area 13 and its scatter, i.e. a distribution of these local mechanical properties (e.g. yield strength, tensile strength and elongation). This output allows to associate the given load levels of the part with the predicted local properties that can be met based on the given process conditions during the design process.
The information about minimal local mechanical properties 101 can be exported from the process simulation tool to the load analysis 16 using FEA as an additional input or optimization of the part design through existing interfaces.
The result is a part design (e.g. a 3D-model 4) that meets the user requirements (i.e. local loads 11) with minimal material consumption. The part design thus benefits from the expected minimum local properties in the defined areas as a function of the design and process and gain additional information about the robustness of his design.
In other words, using this method given uncertainties regarding the tooling layout or process parameter settings can be accepted by the designer as the output compensates these uncertainties offering by additional valuable information about local properties at an early stage, so that the designer can reduce the safety factors and use the expected local properties to reduce the weight of the part.
At a later stage in the product and process development process the manufacturing has to set up the casting process in detail. The primary goal of the manufacturing is to use a casting and gating layout and process conditions that provide robust tooling designs, operating conditions, and a robust process window that can handle the inevitable process variation at an acceptable cost. Since no casting is defect free, the control of defects and their impact on the specified quality levels (in the final mechanical properties) is paramount.
To help in this, the methodology of the disclosure can also be applied in the manufacturing (or generally spoken whoever is responsible for the casting manufacturing process) on a chosen process window or typical process scatter (see also
The method allows not only to simulate a nominal operating point 25 but also offers the assessment of the influence of the geometry, the material or process variables on the adjusted mechanical properties via parametrization of geometries or definition of variations of selected input variables 1.
The methodology applied is principally the same as described before with respect to
In some cases, this includes running a full factorial assessment by executing a simulation 2 of the casting process for all possible combinations of parametrized input variables 18.
In other cases, the method comprises the reduction of the number of variants to be simulated by statistical methods for the virtual Design of Experiments (using methods such as Reduced Factorial or Sobol), resulting in a reduction of the number of parametrized input variables 18 and executing a simulation 2 of the casting process for the reduced number of parametrized input variables 18 and a set of fixed input variables 12.
Variations in local microstructure based mechanical properties 8 of each part of the casting and probability distributions thereof can then be calculated as a function of the variations in local microstructures 6, as well as a variation of local damage factors 9 and probability distributions thereof based on a list of variations in local defects 21, using the process variability parameter 40 for calculating the probability distributions.
The initial output is information about the local mechanical property variation in the form of variations in probability distribution of adjusted local mechanical properties 20 as a combined function of variations in probability distribution of local microstructure based mechanical properties 8 and variations in probability distribution of local damage factors 9, and the defect variation 21 as a function of the process or design variation. The different defect levels and distributions and associated mechanical properties for the varied conditions are automatically analyzed for all designs 1 to N by built-in statistical tools.
The intermediate result of this process is a statistical strength assessment 22 for each part of the object to be cast as a function the variations 1 to N in local mechanical property variation 20 and the local defect variation 21. The statistical analysis provides information about the lowest properties and their distribution for any of the investigated designs 1 to N.
The method also provides information about a local defect probability 23 or any other predicted quality criterion for any of the investigated designs 1 to N.
The final output for the manufacturer is information about a process capability 24 that provides quantitative data on the correlation between input variables and the expected casting quality (in form of main effect and correlation diagrams 100), the minimum 101 and distribution 102 of expected mechanical properties for the process variation for the defined design or process variation so that the chosen design and process decisions can be assessed, arriving at a statistically proven robustness 103 (in form of a robust process window) to produce reliable parts under all circumstances before the first real part is produced.
The outcome benefits the manufacturer in terms of a safe production start and ramp-up. The available information can also be passed on to the casting user as proof that reliable operating conditions have been chosen.
The above-described process capability information 24 can further help determining a robust process window 103 (grey area between 26 and 27) for the parametrized set of input variables 18 as shown in
This illustrated scatter diagram describing the output of a virtual Design of Experiments for the example of two process variables. Each dot/point reflects the predicted mechanical property as a function of a combination of process variables 1 and 2. The nominal operating point 25 can be described as either reflecting the set of fixed variables 12 to determine the design capability 14 or as the nominal center point of a process window for a parametrized set of input variables 18 with a given scatter to assess the process capability 24. The bold line 26 describes the process limits to meet the required specification. Any design exceeding this line will not meet the specification; any design under this line shows results meeting the required specification. For the number N of investigated results the grey line 27 reflects the Pareto frontier, i.e. the limits for the combination of optimal robust process conditions.
In this illustrated example, the method of the disclosure comprises adjusting a process window defined by a fixed set of input variables 12 and a parametrized set of input variables 18 by reducing process variations for process robustness for determining a robust process window 103 in a first step.
Once this robust process window 103 is established, in a next step the operating point 25 defined by the parametrized input variables 18 can be adjusted to determine an optimal operating point 25.
The computer-based system may comprise one or more processors (CPU) 32 configured to execute instructions that cause the computer-based system to perform a method according to any of the possible embodiments described above.
The computer-based system may also comprise computer-readable storage medium 31 configured for storing software-based instructions as part of a program product to be executed by the CPU 32.
The computer-based system may also comprise a memory 33 configured for (temporarily) storing data of applications and processes.
The computer-based system may further comprise an input-output interface 29 for user interaction between the system and a user 38, connected to or comprising an input interface (such as a keyboard and/or mouse) for receiving input from a user 38, and an output device such as an electronic display 30 for conveying information to a user 38.
The computer-based system may further comprise a communications interface 34 for communicating with external devices such as a remote client 37 directly or indirectly via a computer network 35.
The mentioned hardware elements within the computer-based system may be connected via an internal bus configured for handling data communication and processing operations.
The computer-based system may further be connected to a database 36 configured for storing data to be used as input for the above-described simulations (such as material properties for cast materials, experimental data, etc.), wherein the type of connection between the two can be direct or indirect. The computer-based system and the database 36 can be both included in the same physical device, connected via the internal bus, or they can be part of physically different devices, and connected via the communication interface 34 either directly, or indirectly via a computer network 35.
The various aspects and implementations have been described in conjunction with various embodiments herein. However, other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed subject-matter, from a study of the drawings, the disclosure, and the appended claims. In the claims, the word “comprising” does not exclude other elements or steps, and the indefinite article “a” or “an” does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims.
The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. A computer program may be stored/distributed on a suitable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems.
The reference signs used in the claims shall not be construed as limiting the scope.
| Number | Date | Country | Kind |
|---|---|---|---|
| 23175003.5 | May 2023 | EP | regional |
