Patent Application
20250153410
The disclosure relates to the field of computer-based simulations of processes in the manufacturing domain, and in particular to improving polymer-based production processes such as injection, compression, transfer molding or extrusion of polymers. The disclosure further relates to a computer-based systems and methods to design, to set up, or to control a production machine involved in the aforementioned processes.
Injection, compression or transfer molding are cyclic manufacturing processes characterized by pressing liquid material (especially polymers) into a cavity. In this cavity, surrounded by a mold, the liquid material solidifies, due to e.g. cooling down or vulcanization. During the solidification process the liquid material is transformed into a solid. When the material is solid, it is ejected out of the mold and a new production cycle starts with injecting new material into the empty cavity. The solidified polymer is the produced part. The extrusion process on the other hand is used to create parts of a fixed cross section. In this process the liquid material is continually pressed through a die. The liquid material then solidifies and thus the final shape of the part is fixed.
During the solidification process the material is building up internal stresses, which may lead to a deformation of the manufactured item.
It is desirable to set up and to control the production process (e.g. by controlling production machine parameters, such as temperature, injection speed or pressure) and to design the mold or die in a way that the properties and shape of the parts after all production steps match certain quality criteria (e.g. part shape, surface quality, mechanical or chemical properties).
The correlations of production machine parameters, material behavior and the aforementioned criteria are very complex. It is state of the art to use computer-based simulations in a multiplicity of scenarios, e.g to design a mold including cavity geometry and cooling layout, to find an initial setup of machine parameters, to control an injection molding machine in production or to help to understand why a process is not working as expected.
In all scenarios, all of these correlations are dominated by the used part material and its thermo-physical properties, which are therefore crucial inputs for such computer-based simulations.
Examples of the thermo-physical data needed for a computer-based simulation are thermal conductivity, specific heat, density, viscosity, curing and crystallization kinetics. The collection of all or a subset of needed thermo-physical data is referred to as a dataset.
It is state of the art to measure the needed thermo-physical properties. For each needed thermo-physical property, a specialized experimental setup and adapted interpretation of the experimental data has been developed. For example, the viscosity can be measured by pressing polymer melt at a certain temperature through a die. The pressures needed for a given volume flows are measured. From this pressure and volume flow data, the viscosity as a function of shear rate can be calculated.
Two problems hinder the usage of computer-based simulations of the aforementioned processes.
On the one hand, the deviation of all of thermo-physical quantities with respect to different polymers may be very large. If no concrete information about the thermo-physical quantities can be given, the deviation of a computer-based simulation and the real process behavior may also be very large. This deviation will reduce the benefit of the simulation, since simulated measures to improve the real process are less trustworthy or may even be wrong.
On the other hand, it is very time-consuming and expensive to gain an exact and complete characterization of one material. Due to the large variety of feasible materials, its combination with possible additives (such as fibers, carbon black, coloring or ageing agent) and also the use of recycled material, the exact knowledge about one specific material is often lacking.
Some existing methods attempt to calculate thermo-physical properties based on molecular structure. However, these methods are still not accurate enough, and require specific knowledge of the molecular structure of the polymers in question.
It is an object to overcome the lack of reliable material data for polymeric materials by providing a method for creating a dataset of acceptable quality, without having to perform the mentioned characterization. Acceptable in this context means that results of a computer-based simulation of a production process using a generated dataset is sufficiently accurate in order to gain a benefit in the mentioned scenarios.
This goal is achieved by a method for generating thermo-physical data for a certain material based on a known property of the material, such as polymer type. While the deviation of some of these thermo-physical properties between polymers in general may be very large, for a specific polymer type the deviation may be significantly smaller than the deviation between all polymers.
According to a first aspect, there is provided a computer-implemented method for improving a simulation of a polymer-based production process, the method including the steps of obtaining measured parameters for a set of thermo-physical properties for each of a plurality of polymers based on physical measurements, clustering the plurality of polymers into polymer types based on at least one clustering criteria, determining a representative parameter for each of the set of thermo-physical properties for each polymer type using a statistical method, obtaining a choice of polymer type from a user; and generating a dataset based on a choice of polymer type, the dataset including the determined representative parameters for the chosen polymer type.
In some embodiments, at least one of the plurality of polymers can be clustered into multiple polymer types.
In a possible implementation form of the first aspect, the method includes using the generated dataset as input for a simulation of a polymer-based production process for evaluating use of the chosen polymer type in the polymer-based production process.
In a possible implementation form of the first aspect, the at least one clustering criteria comprises at least one of polymer morphology, polymer composition, T90-time, curing system, or polymer hardness.
In a possible implementation form of the first aspect, at least one clustering criteria may be the presence or amount of added fillers in the polymers. The added fillers may include glass fibers, carbon fibers, calcium carbonate, coloring agents, carbon black, silica and/or oil.
In some embodiments, at least one of the polymer types may be further subdivided based on their atomic structure or the chemical structure of their base polymer.
In some embodiments, the polymer types may include at least one of elastomers, thermoset or thermoplastic materials.
In some embodiments, the measured parameters are 1-dimensional and comprise scalar measured values, such as viscosity, and the determined representative parameters comprise scalar representative values.
In other embodiments, the measured parameters are multidimensional and may comprise a plurality of measured values of a thermo-physical property in relation to certain variables, such as thermal conductivity values measured at different temperatures. In such embodiments the determined representative parameters are corresponding multidimensional representative parameters.
The plurality of measured values may also be interpolated or extrapolated to define e.g. a continuous line or curve (in case of 2-dimensional parameters) or a surface (in case of 3-dimensional parameters). In such cases the determined representative parameters are corresponding lines, curves, or surfaces.
In some embodiments, the statistical method includes calculating a median parameter using the Gaussian distribution of the measured parameters of each of the set of thermo-physical properties in the respective cluster for each polymer type.
In embodiments where the measured parameters are scalar values, the median parameter is a mean scalar value, while in the case of multi-dimensional measured parameters the median parameter is correspondingly multi-dimensional, such as a median curve.
In some embodiments, the set of thermo-physical properties may include thermal conductivity, specific heat, density, viscosity, T90-time, curing or crystallization kinetics, solidification behavior, and/or mechanical properties, such as stiffness, shrinkage, cure shrinkage.
In a possible implementation form of the first aspect, the method further includes the steps of calculating a statistical deviation (σ) from the representative parameter for at least one of the set of thermo-physical properties using the statistical method, and determining a plurality of alternative representative parameters for the thermo-physical property based on the representative parameter and the calculated statistical deviation (σ).
In a possible implementation form of the first aspect, a plurality of alternative representative values (Vvar) are calculated by deducting or adding a multiple of the statistical deviation (σ) from/to a representative value (V), i.e. Vvar=V+/−(n×σ).
In a possible implementation form of the first aspect, the plurality of alternative representative values (Vvar) comprise at least one of a lowest representative value (Vmin), a lower representative value (Vlow), an average representative value (Vavg), a higher representative value (Vhigh), and highest representative value (Vmax).
In an embodiment the lowest representative value (Vmin) is calculated as Vmin=V−2σ.
In an embodiment the lower representative value (Vlow) is calculated as Vlow=V−σ.
In an embodiment the average representative value (Vavg) is given as Vavg=V.
In an embodiment the higher representative value (Vhigh) is calculated as Vhigh=V+0.
In an embodiment the highest representative value (Vmax) is calculated as Vmax=V+2σ.
Corresponding methods for calculating lower and higher representative parameters can also be implemented in the case of multi-dimensional representative parameters, such as median curves, where the median curve would be shifted in a positive or negative direction to determine a lower or higher representative curve.
In a possible implementation form of the first aspect, the step of generating a dataset is further based on a choice of a representative parameter from a plurality of alternative representative parameters for at least one thermo-physical property.
In a possible implementation form of the first aspect, the method further includes the steps of defining a set of input parameters based on the choice of polymer type and optionally a choice of a representative parameter for at least one thermo-physical property, and generating a plurality of datasets by varying the input parameters.
In a possible implementation form of the first aspect, the method further includes running a simulation of a production process for each generated dataset, wherein the production process has at least one known output parameter measured in physical reality, and wherein a generated dataset may be used as input thermo-physical material data for a given simulation run to obtain a simulated output parameter of the production process.
In a possible implementation form of the first aspect, the method further includes identifying at least one acceptable dataset that resulted in a simulated output parameter fulfilling at least one quality criteria when compared to the known output parameter.
In a possible implementation form of the first aspect, identifying an acceptable dataset is based on evaluating a plurality of quality criteria defining a multi-dimensional quality threshold with respect to a known output parameter, wherein the acceptable datasets are selected as the datasets resulting in simulated output parameters fulfilling a condition with respect to the quality threshold.
In an embodiment, the condition is that the acceptable datasets are selected as the datasets resulting in simulated output parameters not exceeding the quality threshold.
In an embodiment where the acceptable datasets are based on evaluating two quality criteria, the multi-dimensional quality threshold can be represented by a 2-dimensional line or curve. In other embodiments where the acceptable datasets are based on evaluating more than two quality criteria, the multi-dimensional quality threshold can be represented by a surface expressed in 3 or more dimensions.
In a possible implementation form of the first aspect, at least one of the thermo-physical properties is a multi-parameter thermo-physical property, such as curing degree over time at a given temperature, and the method further comprises the steps of:
In a possible implementation form of the first aspect, the constitutive equation is a combination of a mathematical expression and associated parameters, and the method further comprises optimizing the associated parameters by
In an embodiment the error measure is the sum of all squared errors.
In a possible implementation form of the first aspect, the method further comprises the steps of:
In a possible implementation form of the first aspect, for generating a dataset, the representative values of a multi-parameter thermo-physical property for a chosen polymer type are determined using a manipulated master curve or adjusted constitutive equation.
In a possible implementation form of the first aspect, generating a manipulated master curve comprises stretching a master curve in a direction or applying an offset until the obtained input value fits on the master curve.
In another possible implementation form of the first aspect, generating a manipulated master curve comprises inverse fitting associated parameters of the corresponding constitutive equation defining the master curve until the obtained input value fits on the master curve.
In a possible implementation form of the first aspect, the method further includes using a result of the simulation of a polymer-based production process to set up or to control a production machine involved in the polymer-based production process, such as an injection molding machine.
In some embodiments, the simulation result may be used for controlling production machine parameters, such as temperature, injection speed or pressure.
In a possible implementation form of the first aspect, the method further includes using a result of the simulation of a polymer-based production process in the process of designing a part to be used in the production process, such as a mold for an injection molding process or a die for an extrusion process.
In some embodiments, the simulation result may be used to design part shape, surface quality, mechanical or chemical properties of a mold or die.
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:
Such clustering criteria can be based on polymer morphology, which refers to the overall form of polymer structure, including crystallinity, branching, molecular weight, or cross-linking. Small molecules usually have crystalline solids, which are highly-ordered 3-dimensional arrays of the molecules. Solid polymers can be crystalline or amorphous (disordered arrangements of randomly coiled and entangled chains). Thermoplastics usually are semicrystalline—a combination of crystalline and amorphous regions. The properties of thermoplastics are therefore strongly influenced by their morphology.
The clustering criteria can also be based on polymer composition. As polymers are not pure materials, numerous additives can be added to improve the polymer's functionality or stability. Such additives are, for example, plasticizers, anti-aging stabilizers, flame retardants and colorants. The presence, type, or amount of each additive individually can be used as a clustering criterion.
The clustering criteria can also be based on T90-time for elastomers and thermosets. The T90-time in the technical field refers to the time needed until 90% of the maximal crosslinks between polymer chains are created at a given temperature during the vulcanization. This value describes how fast the curing reaction is about to happen and thus may serve as a clustering criterion.
The clustering criteria can also be based on the curing system used for elastomers. The solidification of elastomers happens during the so-called vulcanization. Single polymer chains connect to other chains and thus build a network. This chemical reaction is driven by a curing system. The different curing systems (e.g. peroxidic or sulfur) may thus serve as a clustering criterion as well.
The clustering criteria can also be based on hardness (Shore or Vickers). The hardness in this context describes the plastic deformation of a part when an indentor is pressed against it at a standardized pressure and time. It is common practice in the technical field to try to deduce thermo-physical properties form this property, thus such a hardness may also serve as a clustering criterion.
According to an embodiment, it is possible to distinguish elastomers, thermoset and thermoplastic materials. Each of these polymers 5 can also be characterized by the chemical structure of their base polymer. Elastomers for example can be subdivided by their atomic structure (heteroatom). for these subdivisions are natural rubber, Examples polybutadiene, ethylene propylene rubber. The presence or amount of added fillers may also be used as a subdivision. Examples for fillers are glass fibers, carbon fibers, calcium carbonate, coloring agents, carbon black, silica and/or oil.
Such subdivisions are referred to in the disclosure as polymer types 6 or polymer type 6 in singular form.
Knowledge of certain thermo-physical properties 4 of these polymers 5 are essential for executing computer-based simulations of production processes 2 that involve use of such polymers 5, for example to design a mold including cavity geometry and cooling layout, to find an initial setup of machine parameters, to control an injection molding machine in production or to help to understand why a process is not working as expected. Examples of the thermo-physical data needed for a simulation computer-based are thermal conductivity, specific heat, density, viscosity, T90-time, curing or crystallization kinetics, solidification behavior, and/or mechanical properties, such as stiffness, shrinkage, cure shrinkage. The collection of all or a subset of needed data on thermo-physical properties 4 is referred to as a dataset 8.
For each needed thermo-physical property 4, specialized experimental setups and adapted interpretations of the experimental data have been developed to gather measured values 3A into such datasets 8 stored in a database.
When launching a simulation, users usually select a dataset 8 from a database based on one specific value, such as an expected value or measured value of the starting material. However, if no concrete information about the thermo-physical properties 4 of a certain polymer 5 can be given as input, the deviation of a computer-based simulation and the real process behavior may be very large. This deviation will reduce the benefit of the simulation, since simulated measures to improve the real process are less trustworthy or may even be wrong.
This problem occurs frequently with polymer-based simulations since it is very time-consuming and expensive to gain an exact and complete characterization of a specific polymer to be used. Due to the large variety of feasible materials, its combination with possible additives, such as fibers, carbon black, coloring or ageing agent, and also the use of recycled material, the exact knowledge about one specific polymer is therefore often lacking.
In particular, recycled material has large differences in the thermo-mechanical properties, since it is a combination of different materials with different histories. Thus, one cannot measure its properties accurately or at all, since the properties vary from batch to batch.
The disclosed method intends to drastically improve the simulation quality in such scenarios where there is no existing dataset for the exact polymer 5 intended to be used in the simulated production process, and the user needs to select a dataset that they believe matches best the properties of the polymer 5 to be used.
The disclosed method is also intended to be used in the context of a DOE (design of experiment) in order to find a stable process with respect to changes of the thermo-physical properties of the material.
The disclosed method may also improve the early design phase of a polymer-based production process where a specific polymer material has not been yet selected for a product to be manufactured. Nevertheless, even in this phase it is required that the material fulfills one or more criteria, or the final product needs to have certain predefined qualities. Here the proposed method can be used to check which properties a polymer material has to have in order to be able to set up a working process. With this knowledge a specific polymer material can be selected.
Moreover, for elastomers it is common that the polymer material is tailor-made for one application. Here the demands on a material have to be specified, e.g. with the help of the proposed method in the DOE context, and then a polymer material can produced which fulfills the specification.
In a first step 101, measured parameters 3 are obtained for a set of thermo-physical properties 4 for each of a plurality of known polymers 5, using state of the art testing procedures.
In a next step 102, the plurality of polymers 5 are clustered into polymer types 6 based on at least one clustering criteria, as shown in
In a next step 103, for all polymer types 6, statistical methods are applied to identify one representative parameter 7 for each thermos-physical property 4. This may be performed by calculating the mean value 9 of the Gaussian distribution of measured values 3A of a given thermo-physical property 4, as illustrated in
Once polymer types 6 are identified, and the above representative parameters 7 are calculated, a dataset 8 can be generated based on a choice of polymer type 6 in a next step 104. This dataset 8 will comprise the determined representative parameters 7 for the chosen polymer type 6. In a final step 105, the generated dataset 8 can be used as input for a simulation 1 of a polymer-based production process 2.
In the illustrated embodiment as shown in
In particular, in the latter mentioned case, the statistical method further comprises, after determining a representative parameter 7, a next step 106 of calculating a statistical deviation 11 from the representative parameter 7 for a chosen thermo-physical property 4.
In a following step 107, based on the representative parameter 7 and the calculated statistical deviation 11, a plurality of alternative representative parameters 71, 72, 73, 74, 75 can be calculated for the chosen thermo-physical property 4. The plurality of alternative representative parameters 71, 72, 73, 74, 75 can be calculated by deducting or adding multiples of the statistical deviation 11 from/to the representative parameter 7. This can result in a selection of five alternative representative parameters, such as: a lowest representative parameter 71, lower representative parameter 72, average representative parameter 73, higher representative parameter 74, and highest representative parameter 73, as illustrated in
According to an embodiment, where the representative parameters are scalar values, alternative representative values can be calculated as follows:
The step 104 of generating a dataset 8, as illustrated in
This choice is further illustrated in
This embodiment is especially useful if for a particular dataset 8 a tendency of thermo-physical property 4, in comparison to other polymer types 6, is known. For example, using a Gaussian distribution: if it is known that the conductivity is particularly low in comparison to other polymers 5 of the same polymer type 6, the mean value 9 minus the statistical deviation 11 of the conductivity may be used. It is then possible to generate multiple datasets 8 by varying the choices between alternative representative parameters 7. One may then simulate already well understood production processes with this variety of datasets 8. Then one can identify which dataset 8 has performed best, as illustrated in more detail in
In a first step 108 a set of input parameters 12 are defined so that multiple datasets 8 can be generated. The input parameters 12 are based on the choice of polymer type 6 and a choice of a representative parameter 7 for at least one thermo-physical property 4. The number of these alternative parameters can vary between thermo-physical properties 4 based on known trends regarding a given thermo-physical property 4. For some thermo-physical properties 4 only one representative parameter 7 may be given, for others a lower 72 and a higher 74 parameter may be given as alternative choices as well as an average 73 parameter, and for others a further lowest 71 and highest 75 parameter may be given as alternative options to choose from.
Then in a next step 109 a plurality of datasets 8 are generated by varying these input parameters 12.
In a following step 110 a simulation 1 of a production process 2 is executed for each generated dataset 8, where each generated dataset 8 is used as input thermo-physical material data for a given simulation 1 to result in a simulated output parameter 14 of the production process 2.
It is an important condition for this method that the production process 2 has at least one known output parameter 13 measured in physical reality.
This way, in a final step 111 at least one acceptable dataset 15 can be identified, based on which simulation 1 resulted in a simulated output parameter 14 fulfilling a quality criterion 16 when compared to the known output parameter 13. This step may be based on only one quality criterion 16, such as a deviation of a simulated output parameter 14 from the known output parameter 13 being below a given threshold, or may be determined based on evaluating multiple quality criteria 16, as further illustrated in
In an embodiment, the condition is that the acceptable datasets 15 are selected as the datasets 8 resulting in simulated output parameters 14 not exceeding the quality threshold 20.
In an embodiment, as illustrated in
In other embodiments where the acceptable datasets 15 are based on evaluating more than two quality criteria 16, the multi-dimensional quality threshold 20 can also be represented by a surface expressed in 3 or more dimensions.
There may also be cases where a given thermo-physical property 4 to be used for generating datasets 8 is a multi-parameter thermo-physical property 4A, such as curing degree over time at a given temperature.
In this case, the method comprises a first step 101A of obtaining a plurality of measured values 3A for the multi-parameter thermo-physical property 4A, i.e. the curing degree over time at a given temperature, based on physical measurements of a plurality of polymers 5. In the illustrated example of
Based on these obtained measured values 3A, in a next step 112 a master curve 18—or a constitutive equation that defines a master curve 18—is determined for the multi-parameter thermo-physical property 4A, based on a best approximation of the plurality of measured values 3A.
The dataset 8 for a chosen polymer type 6 can then be generated by determining representative parameters 7 of this multi-parameter thermo-physical property 4A using the master curve 18 or its constitutive equation.
This constitutive equation is a combination of a mathematical expression and associated parameters, which associated parameters can be further optimized. In such cases, the method further comprises calculating errors as differences between the plurality of measured values 3A and corresponding calculated values given by the constitutive equation.
Then, an error measure, such as the sum of all squared errors is defined, and a set of optimized associated parameters are calculated as the associated parameters that result in a minimum of this error measure.
In certain cases, where a given input value 17 for a multi-parameter thermo-physical property 4A is known, the master curve 18 or its constitutive equation can be adjusted, in order to generate a dataset better matching the given input value 17.
As shown in
In a next step 114, a master curve 18 (or a constitutive equation that defines the master curve 18) for the multi-parameter thermo-physical property 4A is selected based on the chosen polymer type 6—in this case the master curve 18 for the given temperature of 160° C. As can be seen in
In a next step 115, the master curve 18 (or the constitutive equation) is adjusted to generate a manipulated master curve 19 that matches with the obtained input value 17.
Generating the manipulated master curve 19 may comprise different methods, as simple as stretching the master curve 18 in a direction or applying an offset until the obtained input value 17 fits on the generated manipulated master curve 19, or even inverse fitting associated parameters of the constitutive equation until the obtained input value 17 fits on the generated manipulated master curve 19.
As shown in
As illustrated in the flow diagram, the aforementioned methods may further include, following the step 104 of generating a dataset and step 105 of running a simulation 105 of polymer-based production process 2, a step 112 of setting up or controlling a production machine 22 involved in the polymer-based production process 2, such as an injection molding machine, using a simulated output parameter 14 of the simulation 1 of the polymer-based production process 2.
In some embodiments, the simulation results 14 may be used for controlling parameters of the production machine 22, such as temperature, injection speed or pressure, as illustrated in
As further illustrated in the flow diagram of
In some embodiments, the simulation result 14 may be used to design part shape, surface quality, mechanical or chemical properties of a mold or die.
The computer-based system 28 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 28 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 28 may also comprise a memory 33 configured for (temporarily) storing data of applications and processes.
The computer-based system 28 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 for conveying information to a user 38 via a graphical user interface (GUI) 21, such as the GUI 21 illustrated in
The computer-based system 28 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 28 may further be connected to a database 36 configured for storing data to be used as input for the above-described methods (such as measured parameters 3 of thermo-physical properties 4 for various polymers 5, or sets of measured values 3A for multi-parameter thermo-physical properties 4A), wherein the type of connection between the two can be direct or indirect. The computer-based system 28 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.
As mentioned before, the computer-based system 28 may further be connected to a production machine 22 of a polymer-based production process 2, and simulation results 14 generated by the computer-based system 28 may be used for controlling parameters of the production machine 22, such as temperature, injection speed or pressure. Parameters can also be obtained from the production machine 22 for improving the simulations 1 and/or the datasets 8 used for the simulations 1, as described before in relation to
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 |
|---|---|---|---|
| 23209813.7 | Nov 2023 | EP | regional |
