Method for generating thermophysical material data for polymer-based process simulation

Abstract

A computer-implemented method for improving a polymer-based production process (2) is performed by clustering a plurality of known polymers into a polymer type; determining a representative parameter (7) for each of the thermophysical properties (4) using a statistical method based on the measured parameters (3) for a set of thermophysical properties (4) for each polymer type (6); and generating a data set (8) for an unknown polymer based on the selection of the polymer type (6) for the unknown polymer. The generated data set (8) can be used as an input for simulating (1) the polymer-based production process (2).

Description

Technical Field

[0001] The present disclosure relates to the field of computer-based simulation of processes in the field of manufacturing, and in particular to improving polymer-based production processes, such as injection molding, compression, transfer molding or extrusion of polymers. The present disclosure also relates to computer-based systems and methods for designing, setting up or controlling production machines involved in the above processes. Background Art

[0002] Injection, compression or transfer molding are cyclic manufacturing processes characterized by the extrusion of a liquid material, especially a polymer, into a cavity. In this cavity, enclosed by the mold, the liquid material solidifies, for example due to cooling or vulcanization. During the solidification process, the liquid material transforms into a solid. When the material is solid, it is ejected from the mold and a new production cycle begins with the injection of new material into the cavity. The solidified polymer is the produced part.

[0003] On the other hand, the extrusion process is used to manufacture components with a fixed cross-section. In this process, a liquid material is continuously extruded through a die. The liquid material then solidifies, thereby fixing the final shape of the component.

[0004] During the solidification process, the material develops internal stresses, which may cause deformation of the manufactured item.

[0005] It is desirable to set up and 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 so that after all production steps the properties and shape of the component meet certain quality criteria (e.g. component shape, surface quality, mechanical properties or chemical properties).

[0006] The correlation between production machine parameters, material behavior and the above criteria is very complex. The use of computer-based simulation is state-of-the-art in a variety of situations, such as designing a mold including cavity geometry and cooling layout, finding initial settings for machine parameters, controlling an injection molding machine in production, or helping to understand why a process does not work as expected.

[0007] In all cases, all these dependencies are determined by the component materials used and their thermophysical properties, which are therefore key inputs for such computer-based simulations.

[0008] Examples of thermophysical data required for computer-based simulations are thermal conductivity, specific heat, density, viscosity, solidification and crystallization kinetics. The entire collection or subset of required thermophysical data is called a data set.

[0009] The measurement of the desired thermophysical properties is state of the art. For each desired thermophysical property, a dedicated experimental setup and a proper interpretation of the experimental data have been developed. For example, the viscosity can be measured by extruding a polymer melt at a specific temperature through a die. The pressure required for a given volume flow is measured. From this pressure and volume flow data, the viscosity can be calculated as a function of the shear rate.

[0010] Two problems have hampered the use of computer-based simulations of the above processes.

[0011] On the one hand, the deviations of all thermophysical quantities for different polymers can be very large. If no detailed information on the thermophysical quantities is given, the deviations of computer-based simulations from the actual process behavior can also be very large. Such deviations reduce the benefit of the simulation, since the simulated measures used to improve the actual process are less reliable or may even be wrong.

[0012] On the other hand, obtaining an accurate and complete characterization of a material is very time-consuming and expensive. Due to the large variety of possible materials, their combination with possible additives (such as fibers, carbon black, colorants or aging agents) and the use of recycled materials, there is often a lack of accurate knowledge of a specific material.

[0013] Some existing methods attempt to calculate thermophysical properties based on molecular structure. However, these methods are still not accurate enough and require specific knowledge of the molecular structure of the polymer being studied. Summary of the invention

[0014] The aim is to overcome the lack of reliable material data for polymer materials by providing a method for creating a data set of acceptable quality without having to perform the characterization mentioned above. Acceptable in this context means that the results of computer-based simulations of the production process using the generated data set are sufficiently accurate to be able to obtain the benefits in the above mentioned scenarios.

[0015] This objective is achieved by a method for generating thermophysical data for a particular material based on known properties of the material, such as polymer type. While in general the deviations in some of these thermophysical properties between polymers may be quite large, for a particular polymer type the deviations may be significantly less than the deviations between all polymers.

[0016] According to a first aspect, a computer-implemented method for improving the simulation of a polymer-based production process is provided, the method comprising the steps of obtaining measurement parameters of a set of thermophysical 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 criterion; determining representative parameters for each of a set of thermophysical properties for each polymer type using statistical methods; obtaining a user's selection of a polymer type; and generating a data set based on the selection of the polymer type, the data set comprising representative parameters determined for the selected polymer type.

[0017] In some embodiments, at least one polymer in the plurality of polymers can be clustered into a plurality of polymer types.

[0018] In a possible implementation form of the first aspect, the method comprises using the generated data set as input for a simulation of a polymer-based production process to evaluate the use of the selected polymer type in the polymer-based production process.

[0019] In a possible implementation form of the first aspect, the at least one clustering criterion includes at least one of the following: polymer morphology, polymer composition, T90 time, curing system or polymer hardness.

[0020] In a possible implementation form of the first aspect, at least one clustering criterion may be the presence of a filler added to the polymer or the amount of a filler added to the polymer. The added filler may include glass fiber, carbon fiber, calcium carbonate, colorant, carbon black, silica and / or oil.

[0021] In some embodiments, at least one of the polymer types can be further subdivided based on the atomic structure of the at least one polymer type or the chemical structure of the base polymer of the at least one polymer type.

[0022] In some embodiments, the polymer type may include at least one of an elastomer, a thermoset, or a thermoplastic.

[0023] In some embodiments, the measured parameter is one-dimensional and the measured parameter comprises a scalar measurement value, such as viscosity, and the determined representative parameter comprises a scalar representative value.

[0024] In other embodiments, the measured parameter is multidimensional and may include multiple measured values ​​of a thermophysical property associated with certain variables, such as thermal conductivity values ​​measured at different temperatures. In such embodiments, the determined representative parameter is the corresponding multidimensional representative parameter.

[0025] It is also possible to interpolate or extrapolate a plurality of measured values ​​to define, for example, a continuous line or curve (in the case of a two-dimensional parameter) or a surface (in the case of a three-dimensional parameter). In this case, the determined representative parameter is the corresponding line, curve or surface.

[0026] In some embodiments, the statistical method includes calculating a median parameter using a Gaussian distribution of measured parameters for each thermophysical property in a set of thermophysical properties in a corresponding cluster for each polymer type.

[0027] In embodiments where the measured parameter is a scalar value, the median parameter is the average scalar value, whereas in case of a multidimensional measured parameter, the median parameter is correspondingly multidimensional, such as a median curve.

[0028] In some embodiments, the set of thermophysical 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.

[0029] In a possible implementation form of the first aspect, the method also includes the following steps: using a statistical method to calculate the statistical deviation (σ) of a representative parameter for at least one thermophysical property in the set of thermophysical properties; and determining multiple alternative representative parameters for the thermophysical property based on the representative parameter and the calculated statistical deviation (σ).

[0030] In a possible implementation form of the first aspect, the plurality of substitute representative values ​​(V) are selected by subtracting a multiple of the statistical deviation (σ) from the representative value (V) or adding a multiple of the statistical deviation (σ) to the representative value (V). var ) is calculated, that is, V var =V+ / -(n×σ).

[0031] In a possible implementation form of the first aspect, a plurality of alternative representative values ​​(V var ) including the lowest representative value (V min ), lower representative value (V low ), average representative value (V avg ), higher representative value (V high ) and the highest representative value (V max )

[0032] In an embodiment, the minimum representative value (V min ) is calculated as V min =V-2σ.

[0033] In an embodiment, the lower representative value (V low ) is calculated as V low =V-σ.

[0034] In an embodiment, the average representative value (V avg ) is given as V avg =V.

[0035] In an embodiment, the higher representative value (V high ) is calculated as V high =V+σ.

[0036] In an embodiment, the highest representative value (V max ) is calculated as V max =V+2σ.

[0037] In the case of a multi-dimensional representative parameter (eg, a median curve), corresponding methods for calculating lower representative parameters and higher representative parameters may also be implemented, wherein the median curve may be shifted in a positive or negative direction to determine a lower representative curve or a higher representative curve.

[0038] In a possible implementation form of the first aspect, the step for generating the data set is further based on selecting a representative parameter from a plurality of alternative representative parameters for the at least one thermophysical property.

[0039] In a possible implementation form of the first aspect, the method further comprises the steps of defining a set of input parameters based on a selection of a polymer type and optionally a representative parameter for at least one thermophysical property; and generating a plurality of data sets by varying the input parameters.

[0040] In a possible implementation form of the first aspect, the method further comprises: running a simulation of a production process for each generated data set, wherein the production process has at least one known output parameter measured in physical reality, and wherein the generated data set can be used as input thermophysical material data for a given simulation run to obtain simulated output parameters of the production process.

[0041] In a possible implementation form of the first aspect, the method further comprises: identifying at least one acceptable data set, the at least one acceptable data set causing the simulated output parameter to satisfy at least one quality criterion when compared with the known output parameter.

[0042] In a possible implementation form of the first aspect, identification of acceptable data sets is evaluated based on multiple quality criteria, which define multidimensional quality thresholds regarding known output parameters, wherein the acceptable data set is selected as a data set such that the simulated output parameters meet conditions regarding the quality thresholds.

[0043] In an embodiment, the condition is to select an acceptable data set as a data set such that the simulation output parameter does not exceed a quality threshold.

[0044] In embodiments where the acceptable data set is based on evaluation of two quality criteria, the multidimensional quality threshold may be represented by a two-dimensional line or curve. In other embodiments where the acceptable data set is based on evaluation of more than two quality criteria, the multidimensional quality threshold may be represented by a surface represented in three or more dimensions.

[0045] In a possible implementation form of the first aspect, at least one of the thermophysical properties is a multi-parameter thermophysical property, for example, the multi-parameter thermophysical property is a degree of curing that varies with time at a given temperature, and the method further comprises the following steps:

[0046] - obtaining a plurality of measurements for a multi-parameter thermophysical property based on a plurality of physical measurements of the polymer; and

[0047] - determining a master curve for a multi-parameter thermophysical property, or a constitutive equation defining said master curve, that best approximates a plurality of measured values ​​for a given polymer type;

[0048] Therein, for generating a data set, representative parameters for multi-parameter thermophysical properties for the selected polymer type are determined using a master curve or its constitutive equation.

[0049] 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: - ​​calculating an error as a difference between a plurality of measured values ​​and corresponding calculated values ​​given by the constitutive equation;

[0050] - calculating an error measure; and

[0051] - Determining a set of associated parameters that minimizes the error measure.

[0052] In an embodiment, the error measure is the sum of all squared errors.

[0053] In a possible implementation form of the first aspect, the method further includes the following steps:

[0054] - obtaining input values ​​for multi-parameter thermophysical properties of a selected polymer type;

[0055] - based on the selected polymer type, selecting a master curve for multi-parameter thermophysical properties or a constitutive equation defining said master curve; and

[0056] -Adjustments are made to the master curve or constitutive equations to generate a manipulated master curve that matches the input values ​​obtained.

[0057] In a possible implementation form of the first aspect, for generating the data set, representative values ​​of the multi-parameter thermophysical properties for the selected polymer type are determined using a manipulated master curve or an adjusted constitutive equation.

[0058] In a possible implementation form of the first aspect, generating the manipulation master curve includes stretching the master curve in a certain direction or applying an offset until the obtained input value fits onto the master curve.

[0059] In another possible implementation form of the first aspect, generating the manipulation master curve includes inversely fitting associated parameters of a corresponding constitutive equation defining the master curve until the obtained input value fits to the master curve.

[0060] In a possible implementation form of the first aspect, the method further comprises: using the result of the simulation of the polymer-based production process to set or control a production machine involved in the polymer-based production process, for example, the production machine is an injection molding machine.

[0061] In some embodiments, simulation results can be used to control production machine parameters, such as temperature, injection speed, or pressure.

[0062] In a possible implementation form of the first aspect, the method further comprises using results of a simulation of a polymer-based production process in the design of a component to be used in the production process, for example, a mold for an injection molding process or a die for an extrusion process.

[0063] In some embodiments, the simulation results can be used to design the component shape, surface quality, mechanical properties, or chemical properties of a mold or die.

[0064] 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 comprising 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 of the method according to any one of the possible implementation forms of the first aspect.

[0065] 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 the operations of the method according to any one of the possible implementation forms of the first aspect.

[0066] These and other aspects will become apparent from the detailed description given below. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In the following detailed section of the present disclosure, various aspects, embodiments and implementations will be explained in more detail with reference to example embodiments shown in the accompanying drawings, in which:

[0068] Figure 1 A possible way to cluster polymers into polymer types according to an embodiment of the present disclosure is shown.

[0069] Figure 2 Deviations from the values ​​of a given thermophysical property among all polymer types are shown.

[0070] Figure 3 is a flow chart illustrating a method for improving simulation of polymer-based production processes according to an embodiment of the present disclosure.

[0071] Figure 4 A method of clustering polymers into polymer types according to an embodiment of the present disclosure is shown.

[0072] Figure 5 A method of determining a representative value of a thermophysical property for a given polymer type according to an embodiment of the present disclosure is shown.

[0073] Figure 6 is a flow chart illustrating a method for generating an enhanced data set for polymer-based process simulation according to an embodiment of the present disclosure.

[0074] Figure 7 A set of input parameters based on selection of polymer type and selection of representative values ​​for thermophysical properties according to an embodiment of the present disclosure is shown.

[0075] Figure 8 is a flow chart illustrating a method of determining an acceptable data set by running multiple simulations according to an embodiment of the present disclosure.

[0076] Fig. 9 is a diagram illustrating the selection of acceptable data sets based on multiple quality criteria according to an embodiment of the present disclosure.

[0077] Fig.10 is a diagram illustrating steps for determining a master curve of multi-parameter thermophysical properties for a given polymer type according to an embodiment of the present disclosure.

[0078] Fig.11 and Fig.12is a diagram showing the steps of manipulating a master curve to match a given input value according to an embodiment of the present disclosure.

[0079] Fig.13 is a flow chart illustrating a method for generating a data set for a polymer-based process simulation using given input values ​​for multi-parameter thermophysical properties according to an embodiment of the present disclosure.

[0080] Fig.14 is a flow chart illustrating a method for setting up or controlling a production machine, or designing a component to be used in a polymer-based production process, according to an embodiment of the present disclosure.

[0081] Fig.15 is a block diagram illustrating a computer-based system according to a second aspect of the present disclosure. DETAILED DESCRIPTION

[0082] Figure 1 5 shows a possible way to cluster polymers 5 into polymer types 6 according to an embodiment of the present disclosure. Figure 1 Clusters (represented in a simplified manner from letters A to H) can be clustered at different levels based on different clustering criteria.

[0083] Such clustering criteria can be based on polymer morphology, which refers to the overall form of the polymer structure, including crystallinity, branching, molecular weight, or crosslinking. Small molecules typically have crystalline solids, which are highly ordered three-dimensional arrays of molecules. Solid polymers can be crystalline or amorphous (a disordered arrangement of random coils and tangled chains). Thermoplastics are typically semi-crystalline—a combination of crystalline and amorphous regions. Therefore, the properties of thermoplastics are strongly affected by their morphology.

[0084] Clustering criteria can also be based on polymer composition. Since polymers are not pure materials, various additives can be added to improve the functionality or stability of the polymer. These additives are, for example, plasticizers, anti-aging stabilizers, flame retardants and colorants. The presence, type or amount of each additive can be used as a clustering criterion alone.

[0085] Clustering criteria can also be based on the T90 time of elastomers and thermosets. The T90 time in the technical field refers to the time required to generate 90% of the maximum crosslinks between polymer chains at a given temperature during vulcanization. This value describes how quickly the curing reaction will occur and can therefore be used as a clustering criterion.

[0086] Clustering criteria can also be based on the curing system used for the elastomers. Solidification of elastomers occurs during the so-called vulcanization. Individual polymer chains connect to other chains, thus forming a network. This chemical reaction is driven by the curing system. Therefore, different curing systems (e.g. peroxide or sulfur) can also be used as clustering criteria.

[0087] The clustering criterion can also be based on hardness (Shore or Vickers). Hardness in this context describes the plastic deformation of a component when an indenter is pressed against it with a standard pressure and time. It is common practice in the technical field to try to infer thermophysical properties from this property, so this hardness can also be used as a clustering criterion.

[0088] Depending on the embodiment, elastomers, thermosetting materials and thermoplastic materials can be distinguished. Each of these polymers 5 can also be characterized by the chemical structure of its base polymer. For example, elastomers can be subdivided by their atomic structure (heteroatoms). Examples for these subdivisions are natural rubber, polybutadiene, ethylene propylene rubber. The presence of added fillers or the amount of added fillers can also be used as subdivisions. Examples of fillers are glass fibers, carbon fibers, calcium carbonate, colorants, carbon black, silica and / or oils. Such subdivisions are referred to as multiple polymer types 6 or polymer types 6 in the singular in the present disclosure.

[0089] Knowledge of certain thermophysical properties 4 of these polymers 5 is essential for performing computer-based simulations of production processes 2 involving the use of such polymers 5, such as designing molds including cavity geometry and cooling layout, finding initial settings for machine parameters, controlling an injection molding machine in production, or helping understand why a process is not operating as expected. Examples of thermophysical data required for computer-based simulations 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. All sets or subsets of the data required about thermophysical properties 4 are called data sets 8.

[0090] For each desired thermophysical property 4, a dedicated experimental setup and a suitable interpretation of the experimental data have been developed to collect the measured values ​​3A into a data set 8 stored in a database.

[0091] When starting a simulation, the user typically selects a data set from a database based on a specific value, such as an expected or measured value for a starting material. 8 However, if no specific information about the thermophysical properties 4 of a certain polymer 5 is provided as input, the deviation between the computer-based simulation and the actual process behavior can be very large. Such a deviation will reduce the benefit of the simulation, because the simulated measures used to improve the actual process will be less reliable or may even be wrong.

[0092] This problem often occurs in polymer-based simulations, since it is very time-consuming and expensive to obtain an accurate and complete characterization of the specific polymer to be used. Due to the wide variety of possible materials, their combination with possible additives (such as fibers, carbon black, colorants or aging agents), and the use of recycled materials, there is often a lack of accurate knowledge about one specific polymer.

[0093] In particular, recycled materials vary greatly in their thermomechanical properties, as they are a combination of different materials with different histories. As a result, since the properties of recycled materials vary from batch to batch, their properties cannot be measured accurately, or at all.

[0094] The disclosed method aims to significantly improve the simulation quality in situations 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 think best matches the properties of the polymer 5 to be used.

[0095] The disclosed method is also intended to be used in the context of DOE (Design of Experiments) in order to find a stable process with respect to changes in the thermophysical properties of a material.

[0096] The disclosed method can also improve the early design phase of a polymer-based production process, where a specific polymer material has not yet been selected for the product to be manufactured. However, even at this early design phase, the material needs to meet one or more criteria, or the final product needs to have certain predefined qualities. Here, the proposed method can be used to examine which properties the polymer material must have in order to establish a working process. With this knowledge, a specific polymer material can be selected.

[0097] Furthermore, for elastomers, polymer materials are often formulated specifically for an application. The requirements for the material must be specified, for example with the aid of methods presented in the context of DOE, before a polymer material can be produced that meets the specifications.

[0098] Figure 2 The graph shows large deviations in the measured values ​​3 of a certain thermophysical property 4 of a plurality of polymers 5. Figure 2 As shown, these deviations between the measurements 3 are much smaller for selected clusters of these polymers 5 of the same polymer type 6. This provided the inventors with the inventive idea to generate a data set 8 for a computer-based simulation 1 using a method based on clustering polymers 5 into polymer types 6, as explained below.

[0099] Figure 3 is a flow chart of a method for improving simulation 11 of a polymer-based production process according to an embodiment of the present disclosure.

[0100] In a first step 101, measured parameters 3 of a set of thermophysical properties 4 are obtained for each of a plurality of known polymers 5 using a state-of-the-art testing procedure.

[0101] In a next step 102, the plurality of polymers 5 are clustered into polymer types 6 based on at least one clustering criterion, such as Figure 1 The clustering criterion can be molecular structure, filler composition or any other criterion as described above.

[0102] In the next step 103, statistical methods are applied to identify a representative parameter 7 for each thermophysical property 4 for all polymer types 6. This can be achieved by calculating the mean 9 of the Gaussian distribution 10 of the measured values ​​3A of a given thermophysical property 4, such as Figure 5 shown and described in more detail below.

[0103] Once the polymer type 6 has been identified and the representative parameters 7 have been calculated, a data set 8 may be generated based on the selection of the polymer type 6 in a next step 104. The data set 8 will include the representative parameters 7 determined for the selected polymer type 6.

[0104] In a final step 105 , the generated data set 8 may be used as input for a simulation 1 of a polymer-based production process 2 .

[0105] Figure 4 A method of clustering polymers 5 into polymer types 6 according to an embodiment of the present disclosure is shown. As shown in the figure, some polymers 5 can be clustered into only one polymer type 6 based on the clustering criteria described above, while other polymers 5 (shown as polymer F in the figure) can be clustered into multiple polymer types 6.

[0106] Figure 5 and Figure 6 A statistical method for determining representative values ​​7 of thermophysical properties 4 of a given polymer type 6 for use in generating an enhanced data set 8 for a simulation 1 of a polymer-based production process 2 is shown in accordance with an embodiment of the present disclosure.

[0107] exist Figure 5 In the illustrated embodiment, the statistical method comprises calculating the mean 9 of a Gaussian distribution 10 of measured scalar values ​​3A of a given thermophysical property 4. This mean 9 can then be used as the only representative parameter 7, or can be used as one of the representative parameters 7 to be selected when generating a data set 8, such as Figure 6 As shown in the flow chart, Figure 6 The results of the steps shown in Figure 5 As shown in the figure.

[0108] In particular, in the latter case mentioned, the statistical method also comprises, after the determination of the representative parameter 7 , a next step 106 of calculating the statistical deviation 11 from the representative parameter 7 for the selected thermophysical property 4 .

[0109] In a subsequent step 107, a plurality of alternative representative parameters 71, 72, 73, 74, 75 for the selected thermophysical property 4 may be calculated based on the representative parameter 7 and the calculated statistical deviation 11. The plurality of alternative representative parameters 71, 72, 73, 74, 75 may be calculated by subtracting multiples of the statistical deviation 11 from the representative parameter 7 or adding multiples of the statistical deviation 11 to the representative parameter 7. This may result in the selection of five alternative representative parameters, for example: a lowest representative parameter 71, a lower representative parameter 72, an average representative parameter 73, a higher representative parameter 74, and a highest representative parameter 73, as shown in FIG. Figure 5 shown.

[0110] According to an embodiment, when the representative parameter is a scalar value, the alternative representative value may be calculated as follows:

[0111] -Minimum representative value (V min ) is calculated as V min =V-2σ;

[0112] - Lower representative value (V low ) is calculated as V low =V-σ;

[0113] -Average representative value (V avg ) is given as V avg =V;

[0114] -Higher representative value (V high ) is calculated as V high =V + σ; and

[0115] -Highest representative value (V max ) is calculated as V max =V+2σ.

[0116] Then, if Figure 3 As shown and as previously described, the step 104 of generating the data set 8 is based on one or more selections between alternative representative parameters 71 , 72 , 73 , 74 , 75 for at least one thermophysical property 4 .

[0117] Figure 7Such a selection is also shown in the figure, where a user via a graphical user interface (GUI) 21 gives a selection between polymer types 6 and a selection of representative parameters 7 for different thermophysical properties 4 as a set of input parameters 12 for generating a data set 8 to be used as input for a simulation 1 of a polymer-based production process 2.

[0118] This embodiment is particularly useful if the trend of the thermophysical property 4 is known for a particular data set 8 compared to other polymer types 6. For example, using a Gaussian distribution: if the conductivity is known to be particularly low compared to other polymers 5 of the same polymer type 6, then the mean value 9 minus the statistical deviation 11 of the conductivity can be used. Multiple data sets 8 can then be generated by varying the selection between alternative representative parameters 7. This multiple data sets 8 can then be used to simulate a production process that is already well understood. It can then be identified which data set 8 performs best, such as Figure 8 As shown in more detail in .

[0119] Figure 8 The flowchart shows a method for determining an acceptable data set 15 by running multiple simulations 1 based on a set of varying input parameters 12, which can be determined by Figure 7 The GUI shown is given.

[0120] In a first step 108, a set of input parameters 12 is defined so that a plurality of data sets 8 can be generated. The input parameters 12 are based on the selection of the polymer type 6 and the selection of a representative parameter 7 for at least one thermophysical property 4. The number of these alternative parameters can vary between thermophysical properties 4 based on known trends for a given thermophysical property 4. For some thermophysical properties 4, only one representative parameter 7 may be given, for other thermophysical properties 4, a lower parameter 72 and an upper parameter 74 as well as an average parameter 73 may be given as alternatives, and for other thermophysical properties 4, additional lowest parameters 71 and highest parameters 75 may be given as alternatives to choose from.

[0121] Then, in a next step 109 , a plurality of data sets 8 are generated by varying these input parameters 12 .

[0122] In a following step 110 , a simulation 1 of the production process 2 is performed for each generated data set 8 , wherein each generated data set 8 is used as input thermophysical material data for a given simulation 1 to generate simulation output parameters 14 of the production process 2 .

[0123] An essential condition for the method is that the production process 2 has at least one known output parameter 13 measured in physical reality.

[0124] Thus, in a final step 111, at least one acceptable data set 15 may be identified, based on which the simulation 1 causes the simulated output parameter 14 to satisfy the quality criterion 16 when compared with the known output parameter 13. This step may be based on only one quality criterion 16, such as the deviation of the simulated output parameter 14 from the known output parameter 13 being below a given threshold, or the step may be based on the evaluation of multiple quality criteria 16, such as Fig. 9 As shown further.

[0125] Fig. 9 1 is a diagram illustrating the selection of acceptable data sets 15 based on multiple quality criteria 16 according to an embodiment of the present disclosure. In the illustrated embodiment, the identification of acceptable data sets 15 is based on multiple quality criteria 16 that define multidimensional quality thresholds 20 with respect to known output parameters 13, wherein the acceptable data sets 15 are selected as data sets 8 that cause the simulated output parameters 14 to satisfy conditions with respect to the quality thresholds 20.

[0126] In an embodiment, the condition is selecting the acceptable data set 15 as the data set 8 that causes the simulation output parameter 14 not to exceed the quality threshold 20 .

[0127] In an embodiment, if Fig. 9 As shown, the acceptable data set 15 is based on the evaluation of two quality criteria 16, and the multidimensional quality threshold 20 is represented by a two-dimensional line or a two-dimensional curve. Each of the simulation output parameters 14 that meet the quality threshold criteria can represent an acceptable data set 15, and there is no difference in the quality of these acceptable data sets 15. The sum of the acceptable data sets 15 represents a Pareto set, which is located at Fig. 9 The simulated output parameter 14 is represented by the curve 20 in FIG.

[0128] In other embodiments where the acceptability of the data set 15 is based on evaluating more than two quality criteria 16 , the multi-dimensional quality threshold 20 may also be represented by a surface represented in three or more dimensions.

[0129] There may also be situations where the given thermophysical property 4 used to generate the data set 8 is a multi-parameter thermophysical property 4A, such as the degree of cure at a given temperature as a function of time. Figures 10 to 13 The steps of the method are shown which in this case involve at least one multi-parameter thermophysical property 4A of a given set of thermophysical properties 4 .

[0130] Fig.10 is a diagram illustrating steps in determining a master curve 18 of multi-parameter thermophysical properties 4A for a given polymer type 6 according to an embodiment of the present disclosure.

[0131] In this case, the method comprises a first step 101A of obtaining a plurality of measured values ​​3A of a multi-parameter thermophysical property 4A, namely the degree of cure at a given temperature as a function of time, based on physical measurements of a plurality of polymers 5. Fig.10 In the example shown, for temperatures of 160° C., 175° C. and 190° C., measured values ​​3A of the degree of cure between 0 and 1 (or 0 to 100%) are obtained over a time period of 0 to 1000 seconds.

[0132] Based on these obtained measurements 3A, in a next step 112 , a master curve 18 for the multi-parameter thermophysical property 4A (or a constitutive equation defining the master curve 18 ) is determined based on the best approximation of the plurality of measurements 3A.

[0133] A data set 8 for the selected polymer type 6 may then be generated by determining representative parameters 7 for the multi-parameter thermophysical property 4A using the master curve 18 or its constitutive equation.

[0134] The constitutive equation is a combination of a mathematical expression and associated parameters which can be further optimized. In this case, the method also comprises calculating the error, ie the difference between the plurality of measured values ​​3A and the corresponding calculated values ​​given by the constitutive equation.

[0135] Then, an error measure, such as the sum of all squared errors, is defined and an optimized set of associated parameters is calculated as the associated parameters that minimize this error measure.

[0136] In some cases, when given input values ​​17 for the multi-parameter thermophysical property 4A are known, the master curve 18 or its constitutive equations may be adjusted to generate a data set that better matches the given input values ​​17 .

[0137] Fig.11 and Fig.12 is a continuous graph, and Fig.13 is a flow chart illustrating steps for manipulating a master curve 18 to match a given input value 17 according to an embodiment of the present disclosure.

[0138] like Fig.11 and Fig.13 As shown in the flowchart of FIG. 1 , in a first step 113, an input value 17 for the multi-parameter thermophysical property 4A of the selected polymer type 6 is obtained. For example, the input value 17 defines the curing kinetics, as described above. Fig.10 As shown in the example of , in this particular example, the input value 17 is a degree of cure of 90% or 0.9 at a given temperature of 160° C. at a time of 750 seconds.

[0139] In the next step 114, a master curve 18 (or constitutive equation defining a master curve 18) is selected for the multi-parameter thermophysical property 4A based on the selected polymer type 6 - in this case, a master curve 18 for a given temperature of 160°C. Fig.11 It can be seen that the input value 17 does not match the master curve 18 .

[0140] Fig.12 The next step for manipulating the master curve 18 to accommodate the input value 17 is shown, as Fig.13 as explained in the flowchart.

[0141] In the next step 115 , the master curve 18 (or constitutive equation) is adjusted to generate a manipulation master curve 19 that matches the obtained input values ​​17 .

[0142] Generating the manipulation master curve 19 may include different methods, as simple as stretching the master curve 18 in a certain direction or applying an offset until the obtained input values ​​17 fit to the generated manipulation master curve 19, or even inverse fitting the associated parameters of the constitutive equations until the obtained input values ​​17 fit to the generated manipulation master curve 19.

[0143] like Fig.13 As shown, once the manipulation master curve 19 is generated, it can be used to generate a data set 8, wherein the manipulation master curve 19 (or its adjusted constitutive equation) is used to determine representative parameters 7 for the multi-parameter thermophysical properties 4A for the selected polymer type 6.

[0144] Fig.14 is a flow chart illustrating a computer-based method for setting up or controlling a production machine 22, or designing a component to be used in a polymer-based production process 2, according to an embodiment of the present disclosure.

[0145] As shown in the flow chart, the aforementioned method may also include: after step 104 of generating a data set and step 105 of running a simulation 105 of the polymer-based production process 2, step 112 of setting or controlling a production machine 22 (e.g., an injection molding machine) involved in the polymer-based production process 2 using the simulation output parameters 14 of the simulation 1 of the polymer-based production process 2.

[0146] In some embodiments, the simulation results 14 can be used to control parameters of the production machine 22, such as temperature, injection speed or pressure. Fig.15 shown.

[0147] like Fig.14As further shown in the flowchart, the above method may also include: after step 104 of generating a data set and step 105 of running the simulation 1 of the polymer-based production process 2, step 113 of designing a component to be used in the production process 2 (e.g., a mold for an injection molding process or a die for an extrusion process) using the simulation output parameters 14 of the simulation 1 of the polymer-based production process 2.

[0148] In some embodiments, the simulation results 14 may be used to design the shape, surface quality, mechanical properties, or chemical properties of a mold or die.

[0149] Fig.15 is a schematic block diagram showing an example of a hardware configuration of a computer-based system 28 for running a simulation 1 of a polymer-based production process 2, and optionally for setting up or controlling a production machine 22 involved in the polymer-based production process 2, according to the present disclosure.

[0150] The computer-based system 28 may include 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 implementations described above.

[0151] The computer-based system 28 may also include a computer-readable storage medium 31 configured to store software-based instructions as part of a program product executed by the CPU 32 .

[0152] The computer-based system 28 may also include a memory 33 configured to (temporarily) store data for applications and processes.

[0153] The computer-based system 28 may also include an input-output interface (I / O) 29 for user interaction between the system and a user 38, the input-output interface 29 being connected to or including an input interface (e.g., a keyboard and / or a mouse) for receiving input from the user 38, and a graphical user interface (GUI) 21 (e.g., Figure 7 An output device (eg, electronic display 30 ) that communicates information to a user 38 , as shown in GUI 21 .

[0154] The computer-based system 28 may also include a communication interface (COMM) 34 for communicating with external devices (eg, remote clients 37 ) directly or indirectly via a computer network 35 .

[0155] The mentioned hardware elements within the computer-based system may be connected via an internal bus configured to handle data communications and processing operations.

[0156] The computer-based system 28 may also be connected to a database 36 configured to store data to be used as input for the above method (e.g., measured parameters 3 for the thermophysical properties 4 of individual polymers 5, or a series of measured values ​​3A for multi-parameter thermophysical properties 4A), wherein the type of connection between the two may be direct or indirect. The computer-based system 28 and the database 36 may both be included in the same physical device, connected via an internal bus, or they may be part of physically different devices and directly connected via a communication interface 34, or indirectly connected via a computer network 35.

[0157] As previously described, the computer-based system 28 may also be connected to the production machine 22 of the polymer-based production process 2, and the simulation results 14 generated by the computer-based system 28 may be used to control parameters of the production machine 22, such as temperature, injection speed or pressure. Parameters may also be obtained from the production machine 22 for improving the simulation 1 and / or improving the data set 8 used for the simulation 1, as previously described with respect to Figure 8 described.

[0158] Various aspects and implementations are described herein in conjunction with various embodiments. However, other variations of the disclosed embodiments may be understood and implemented by those skilled in the art in practicing the claimed subject matter by studying 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 implement the functions of several items described in the claims. The fact that certain measures are described in mutually different dependent claims does not indicate that a combination of these measures cannot be fully utilized. The computer program may be stored / distributed on a suitable medium, such as an optical storage medium or solid-state medium provided 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 telecommunications systems.

[0159] Reference signs used in the claims should not be construed as limiting the scope.

Claims

1. A computer-implemented method for improving a polymer-based production process (2), the method comprising the steps of: Step (101): obtaining measurement parameters (3) of a set of thermophysical properties (4) for each polymer in a plurality of polymers (5) based on physical measurements; Step (102): clustering the plurality of polymers (5) into polymer types (6) based on at least one clustering criterion; Step (103): Determine a representative parameter (7) for each thermophysical property in a set of thermophysical properties (4) for each polymer type (6) by applying a statistical method to the measured parameters (3) for a given thermophysical property (4) for a given polymer type (6); Obtaining a user's selection of a polymer type (6); as well as Step (104): Based on the selection of the polymer type (6), a data set (8) is generated, the data set (8) comprising representative parameters (7) determined for the selected polymer type (6).

2. The method according to claim 1, wherein: The method further comprises the steps of: Step (105): Using the generated data set (8) as input for simulation (1) of a polymer-based production process (2) to evaluate the use of the selected polymer type in the polymer-based production process (2).

3. The method according to claim 1, wherein: The at least one clustering criterion includes polymer morphology, polymer composition, T90 time, cure system, or polymer hardness.

4. The method according to claim 1, wherein: At least one of the polymer types (6) is further subdivided based on the atomic structure of the at least one polymer type or the chemical structure of the base polymer (5) of the at least one polymer type.

5. The method according to claim 1, wherein: The statistical method comprises calculating a median parameter (9) using a Gaussian distribution (10) of the measured parameters (3) for each thermophysical property in a set of thermophysical properties (4) in the corresponding cluster for each polymer type (6).

6. The method according to claim 1, further comprising the steps of: Step (106): Calculating the statistical deviation (11) of the representative parameter (7) for at least one thermophysical property in the set of thermophysical properties (4) using a statistical method, and Step (107): Determine a plurality of alternative representative parameters (71, 72, 73, 74, 75) for the at least one thermophysical property (4) based on the representative parameter (7) and the calculated statistical deviation (11), wherein: The step (104) for generating the data set (8) is also based on the selection of a representative parameter (7) among a plurality of alternative representative parameters (71, 72, 73, 74, 75) for the at least one thermophysical property (4).

7. The method according to claim 1, further comprising the steps of: Step (108): defining a set of input parameters (12) based on the selection of the polymer type (6) and optionally the selection of a representative parameter (7) for at least one thermophysical property (4); Step (109): generating a plurality of data sets (8) by changing the input parameters (12); Step (110): Running a simulation (1) of the production process (2) for each generated data set (8), wherein: The production process (2) has at least one known output parameter (13) measured in physical reality, and wherein the generated data set (8) is used as input thermophysical material data for a given simulation (1) run to obtain a simulated output parameter (14) of the production process (2); as well as Step (111): Identifying at least one acceptable data set (15) such that a simulated output parameter (14) satisfies a quality criterion (16) when compared to the known output parameter (13).

8. The method according to claim 7, wherein: The identification of the at least one acceptable data set (15) is based on a plurality of quality criteria (16) defining a multidimensional quality threshold (20) with respect to the known output parameters (13), wherein the acceptable data set (15) is selected as a data set (8) such that the simulated output parameters (14) satisfy conditions with respect to the quality threshold (20).

9. The method according to claim 1, wherein: At least one of the set of thermophysical properties (4) is a multi-parameter thermophysical property (4A), for example, the multi-parameter thermophysical property (4A) is a degree of curing as a function of time at a given temperature, and wherein the method further comprises the following steps: Step (101A): obtaining a plurality of measurement values ​​(3A) for the multi-parameter thermophysical property (4A) based on physical measurements of a plurality of polymers (5); and Step (112): determining a master curve (18) for the multi-parameter thermophysical property (4A) or a constitutive equation defining the master curve (18) to best approximate a plurality of measured values ​​(3A) for a given polymer type; Wherein, for step (104) generating a data set (8), the master curve (18) or the constitutive equation is used to determine the representative parameters (7) for the multi-parameter thermophysical properties (4A) of the selected polymer type (6).

10. The method according to claim 9, wherein: The constitutive equation is a combination of a mathematical expression and associated parameters, and wherein the method further comprises optimizing the associated parameters by: - calculating an error as the difference between said plurality of measured values ​​(3A) and the corresponding calculated values ​​given by said constitutive equation; - calculating an error measure, for example the sum of all squared errors; and - determining a set of associated parameters that minimizes said error measure.

11. The method according to claim 9, further comprising the steps of: Step (113): obtaining input values ​​(17) for multi-parameter thermophysical properties (4A) of the selected polymer type (6); Step (114): selecting a master curve (18) for the multi-parameter thermophysical property (4A) or a constitutive equation defining the master curve (18) based on the selected polymer type (6); and Step (115): adjusting the master curve (18) or the constitutive equation to generate a manipulation master curve (19) matching the obtained input value (17); in, For step (104) generating the data set (8), representative values ​​(7) of the multi-parameter thermophysical properties (4A) for the selected polymer type (6) are determined using the manipulated master curve (19) or adjusted constitutive equation.

12. The method according to any one of claims 1 to 11, further comprising the step (112) of using simulation output parameters (14) of a simulation (1) of a polymer-based production process (2) to set or control a production machine (22) involved in the polymer-based production process (2), for example, the production machine (22) is an injection molding machine.

13. The method according to any one of claims 1 to 11, further comprising the step (113) of using simulation output parameters (14) of a simulation (1) of a polymer-based production process (2) to design a component to be used in the production process (2), for example, a mold for an injection molding process or a die for an extrusion process.

14. A computer-based system, the computer-based system comprising: Input and output interface (29); Display (30); a non-transitory machine-readable storage medium (31), the non-transitory machine-readable storage medium (31) comprising a computer program product; as well as At least one processor (32) operable to execute the program product, to interact with the input-output interface (29) and the display (30), and to perform the operations of the method according to any one of claims 1 to 11.

15. A computer program product encoded on a non-transitory machine-readable storage medium (31), the non-transitory machine-readable storage medium (31) being operable to cause a processor (32) to perform the operations of the method according to any one of claims 1 to 11.

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