Method and system for predicting mold fill processing of foam mixtures

Abstract

A method for predicting a mold filling process of a foam mixture is disclosed, wherein the foam mixture expands and several characteristics of the foam mixture change during a foaming process. The disclosed method comprises: receiving first experimental data, the first experimental data comprising first measurements of at least one of a number of characteristics, where at least some of the first measurements are measured at different points in time during a foaming process of a foam mixture, preferably in a cavity; receiving a first model describing a time-dependent behavior of at least one of the plurality of characteristics during expansion of the foam mixture, where the first model includes one or more parameters; determining a first parameter set based on the first experimental data and the first model; receiving second experimental data, the second experimental data comprising a second measurement representative of the propagation of the foam mixture expanding on a predefined surface, where the predefined surface is formed by an inclined surface such that the foam mixture flows on the surface during the foaming process by the influence of gravity; receiving a second model describing foam propagation on a predefined inclined surface during a foaming process of the foam mixture, where the second model includes one or several parameters; determining a second parameter set based on the second experimental data and a second model; and outputting at least one of the first set of parameters, the second set of parameters, and a combination of the first set of parameters and the second set of parameters for predicting a mold filling process of the foam mixture. A corresponding system, computer program product and computer readable storage medium are additionally disclosed.

Description

[0001] The present disclosure relates to methods and systems for predicting a mold filling process of a foam mixture during which the foam mixture expands and several properties of the foam mixture change.

[0002] To name just a few possible uses, foam mixtures are widely used, for example, for thermal insulation, acoustic insulation, reinforcement of structures, as adhesive and / or as filling material, which uses are sometimes realized simultaneously. An important representative of such foam mixtures is polyurethane foam. The foam mixture may comprise several components and may even be a combination of two or more different types of foam. The components of the foam mixture react with one another during the foaming process, thus leading to an expansion of the foam mixture. During the foaming process, several properties of the foam mixture change, such as temperature, volume, chemical composition, etc.

[0003] In many cases, foam mixtures are used to fill molds. For example, such a mold can be a car door or a vehicle dashboard that should be filled with a foam mixture for thermal insulation and sound insulation. When filling the mold, it is desirable that the mold is fully filled with the expanding and solidifying foam. For this purpose, the properties of the foaming process must be controlled, such as temperature, injection pressure, (one or more) injection points, etc. However, these properties are based on the foam mixture used at the mold filling process, the mold to be filled, and various other constraints. This means that for a foam mixture with unknown expansion behavior, several mold filling processes with several different specifications must be used to fill several molds until the desired mold filling result is found. This causes a huge experimental workload. Changes at the mold may result in additional mold filling behavior of the foam mixture, so that each mold change results in a new mold filling experiment.

[0004] Even if after many experiments a molding process specification is found that results in adequate filling of the mold, it is still unclear whether the result is close to the optimal mold filling behavior. It is possible that similar results can be achieved with a reduced amount of foam mixture, with a better quality of the resulting foam, or with an even more homogeneous distribution of the foam in the mold. This means that mold filling experiments may not lead to optimal results.

[0005] There are several known methods in the prior art that can be used to estimate at least part of the behavior of an expanding polymer foam mixture, such as viscosity. For example, WO 2021 / 249897 A1 relates to the determination of at least one empirical coefficient that can be used to calculate the viscosity of a thermoplastic polymer melt. A capillary rheometer measures at least one rheological property of a polymer melt. A solvable generalized Newtonian fluid model describes the same at least one rheological property and includes at least one empirical coefficient. By iteratively adapting the empirical coefficients, the difference between the measured values ​​and the results of the generalized Newtonian fluid model is minimized. The empirical coefficients so determined are input into a solvable Navier-Stokes model. Based on the results of the Navier-Stokes model and the measured values, the empirical coefficients are further iteratively adapted. Although viscosity is an important property of a foaming mixture, it is not sufficient to predict mold filling behavior.

[0006] US 5,740,074 A is related to a method for filling a compartment cavity (such as a refrigerator cavity) with foam produced by the expansion and curing of a foaming mixture of a predetermined chemical substance. To this end, a known amount of foaming mixture is created in a test box cavity, and preselected parameters (such as free surface height and pressure) are measured to obtain the average density and apparent viscosity of the foam as a function of time. Using these parameters (average density and apparent viscosity), a computer simulation is performed to simulate the foam expansion as a function of time of a preselected amount of preselected foaming mixture created at a preselected position in a closed cavity with the same geometry as the compartment cavity. The method of US 5,740,047 A characterizes the foaming process by estimating the viscosity of the mixture using only the properties of the free-rising expanding foam. However, in order to accurately describe the spatial evolution of the foam front, it is necessary to take into account the flow behavior at the early stage of the foaming process. Therefore, the use of a "semi-empirical method" to extrapolate the viscosity of the foaming mixture and not including basic rheological dynamics limits the possibility of accurately describing the mold filling process of expanded polyurethane foam.

[0007] The research article "Numerical Simulation of Mold Filling Processes with Polyurethane Foams" (Chem. Eng. Technol. 2009, 32, No. 9, p. 1438-1447) takes a similar approach by also using the time change of the foaming mixture to characterize the free rise behavior of the expanding foam, for example in a beaker. Although additional equations for chemical kinetics are solved, the influence of polyurethane chemistry on the flow behavior of the expanding mixture is not explicitly shown. The foam viscosity model applied is purely theoretical, as no experiments for validation of the model are disclosed. In addition, validation in a beaker is often insufficient to fully capture the flow behavior of the expanding foam. In order to achieve an optimal mold design and the filling behavior of the associated foaming mixture in the mold, extensive experiments and tests must be performed. However, it would be beneficial if such extensive testing were minimized. Therefore, the purpose of the present disclosure is to provide a method and system that allows prediction of the mold filling behavior of a foam mixture.

[0008] The present disclosure proposes a method for predicting a mold filling process of a foam mixture during which the foam mixture expands and several properties of the foam mixture change, the method comprising:

[0009] receiving first experimental data, the first experimental data comprising first measured values ​​of at least one of the several properties, wherein at least some of the first measured values ​​are measured at different points in time during a foaming process of the foam mixture, preferably in the cavity,

[0010] receiving a first model describing a time-dependent behavior of at least one of several properties during expansion of the foam mixture, wherein the first model comprises one or several parameters,

[0011] determining a first parameter set based on first experimental data and a first model,

[0012] receiving second experimental data, the second experimental data comprising second measurements representing the spread of the foam mixture expanding on a predefined surface, wherein the predefined surface is formed by an inclined surface such that the foam mixture flows on the surface during the foaming process by the influence of gravity,

[0013] receiving a second model describing foam propagation during a foaming process of the foam mixture on a predefined inclined surface, wherein the second model comprises one or several parameters,

[0014] determining a second set of parameters based on the second experimental data and the second model, and

[0015] At least one of the first parameter set, the second parameter set, and a combination of the first parameter set and the second parameter set is output for use in predicting a mold filling process of the foam mixture.

[0016] With respect to the first experimental data and the second experimental data and the first model and the second model, it should be noted that "first" and "second" do not indicate a specific order of data acquisition or a specific order of data and / or models or a specific order of associated steps according to the method of the present disclosure. The adjectives should only distinguish between experimental data and models.

[0017] Additionally, it should be noted that the use of “and / or” between two features should be understood as a general expression for one of three different embodiments: a first embodiment including the first feature, a second embodiment including the second feature, and a third embodiment including the first and second features.

[0018] It has been recognized that the mold-filling behavior of a foam mixture can be derived from basic tests performed outside the mold. In general, the mold defines constraints for the expansion of the foam mixture: during the foaming process, each wall of the mold may hinder the expansion of the foam mixture perpendicular to the wall, and each change in the surface structure of the mold may affect the flow behavior of the foam mixture. But apart from these constraints, the foam mixture behaves similarly both outside the mold and inside the mold. Therefore, the values ​​that describe the foaming behavior of the foam mixture outside the mold also describe the foaming behavior of the foam mixture inside the mold. That is, based on basic tests outside the mold, the behavior of the foam mixture inside the mold can be derived.

[0019] To this end, the present disclosure uses first experimental data and second experimental data obtained from measurements during a first test and a second test.Both tests analyze the behavior of the foam mixture during the foaming process.

[0020] A first test may involve a free-rise expansion of the foam mixture. For ease of measurement, the expansion may be restricted in some directions. In one embodiment, the expansion takes place in a cavity formed, for example, by a beaker. With such a test, the chemical rheological and / or kinetic properties of the foaming process may be determined. In this first test, a measuring device may be used. The measuring device may include a base plate, a heater (for example incorporated in the base plate) and various sensors. The beaker may be positioned on the base plate and changes in the properties of the foam mixture may be monitored by the sensors. The sensors may include dielectric sensors, pressure sensors, temperature sensors and / or height sensors (for example a laser or ultrasonic sensor positioned above the beaker). The acquired data may additionally be used by a control unit of the measuring device. To cite just one possible, commercially available measuring device, such a measuring device is provided by Format Messtechnik GmbH of Karlsruhe, Germany as release.

[0021] Such a first test or a different first test generates a first measurement value, which is part of the first experimental data. The first measurement value generally contains several values ​​and may contain values ​​measured by different sensors and / or values ​​measured at different time points during the expansion of the foam mixture. The first experimental data can be input into a first experimental data interface of the method according to the present disclosure and / or the system according to the present disclosure.

[0022] The second test may involve the flow behavior of the foam mixture during the foaming process. During the second test, the foam mixture may expand on a predefined surface and the propagation of the foam mixture along the surface may be measured. The expansion may occur freely, i.e. only affected by the predefined surface in a predefined manner. Using the knowledge about the predefined surface, the measured value of the foam propagation provides information about the flow behavior of the foam during the foaming process. The predefined surface may be embodied in different ways, as long as the predefined surface affects the expanded foam mixture in a predefined and deterministic manner. According to the present invention, the predefined surface is formed by an inclined surface, so that the foam mixture flows on the surface by the influence of gravity during the foaming process. This test provides the following benefits: its results focus on the rheological properties of the foam mixture, rather than its expansion properties. The inclination should be large enough to affect the flow of the foam. However, the inclination should be small enough so that the foam does not flow too fast and the measurement during the foaming process is possible. In one embodiment, the inclination relative to the horizontal direction is greater than or equal to 1°, in another embodiment, the inclination is greater than or equal to 5°, and in yet another embodiment, the inclination is greater than or equal to 10°. In one embodiment, the inclination relative to the horizontal direction is less than or equal to 50°, in another embodiment, the inclination is less than or equal to 30°, and in yet another embodiment, the inclination is less than or equal to 20°.

[0023] The second test or a similar second test generates a second measurement value, which is part of the second experimental data. The second measurement value generally contains several values. The second measurement value can refer to different characteristics of the foam mixture and / or be measured at different time points during the expansion of the foam mixture. In addition to information about the propagation of the foam mixture during the second test, the second measurement value can also refer to additional characteristics, such as temperature, volume of the foam mixture, width of the foam mixture, etc. The second experimental data can be input into a second experimental data interface of the method according to the present disclosure and / or the system according to the present disclosure.

[0024] According to an advantageous embodiment, the second measurement is generated by capturing a time series of images showing the evolution of the foam front in the flow scene on a predefined surface. By implementing an image recognition algorithm, the evolution of the foam front can be analyzed in order to extract size information as well as the rheological behavior of the flowing foam mixture. For example, it is possible to design the predefined surface such that tracking the propagating foam front by an image recognition algorithm is relatively easy to implement (e.g. by a "chessboard" design).

[0025] Image recognition methods and algorithms that can be applied here are known in the art. For example, in the textbook "Computer Vision and Machine Learning for Intelligent Sensing Systems" (2023), MDPI - Multidisciplinary Digital Publishing Institute, https: / / doi.org / 10.3390 / books978-3-0365-7869-9, several use cases of different fields and technologies for solving computer vision related tasks are described. The publication CVIP, C. (7th:2022:NI (2023), "Computer vision and image processing: 7th International Conference", CVIP 2022, Nagpur, India, November 4-6, 2022, Revised Selected Papers, Part II, Springer. https: / / doi.org / 10.1007 / 978-3-031-31407-0 provides insights into deep neural network architecture configurations that are specifically tuned for application in computer vision tasks. Finally, the research paper "Deep Learning for Toxicity and Disease Prediction", (2020), Frontiers Media SA, https: / / doi.org / 10.3389 / 978-2-88963-632-7 provides valuable insights into how to solve the image segmentation problem. Since the field of computer vision overlaps with engineering, the underlying hardware setup for image analysis is crucial for the successful implementation of related image recognition algorithms. Therefore, this paper provides specific hardware setups for the challenges that arise in image analysis.

[0026] The first model describes the time-dependent behavior of at least one of several properties of the foam mixture, which properties change during the foaming process. The first model comprises one or several parameters, the values ​​of which are related to the changing properties of the foam mixture. The second model describes the foam propagation during the expansion of the foam mixture. The second model comprises one or several parameters, the values ​​of which are related to the propagation of the foam mixture. The term "propagation" refers to the movement of the foam mixture parallel to a predefined surface. This may describe to what extent the outer boundary of the foam mixture has moved in a predefined direction within a certain period of time.

[0027] With regard to the "first model" and the "second model", it should be noted that these models generally describe a specific behavior of the foam mixture under specific conditions. This means that the models describe different aspects of the behavior of the foam mixture. This can be achieved in various ways. The first model and the second model can be completely independent of each other, i.e. the models are based on different assumptions or modeling schemes. However, the first model and the second model can also be based on the same modeling scheme, i.e. the models can be derived from the same concept. In this regard, the two models can be derived from a more general concept, wherein the first model is derived as a first subset or a first generalization or a first simplification, and the second model is derived as a second subset or a second generalization or a second simplification. But the first model and the second model can be derived from each other, for example, one of those models is a more general version of the other.

[0028] The first model and / or the second model may be based on the modeling approach disclosed in Clément Raimbault et al., “Foaming parameter identification of polyurethane using device", Polym Eng Sci. 2021;61:1243-1265 or Joe Wang's "Combination of PU System with CAE Simulation for Accurate PU FoamingPrediction", 2021, https: / / www.moldex3d.com / combination-of-pu-foamat-system-with-cae-si mulation-for-accurate-pu-foaming-prediction / or "Density predictions using a finite element / level set model of polyurethanefoam expansion and polymerization" by R. Rao et al., Computers and Fluids (2018), 175:20-35.

[0029] The first model and the second model can be represented in various ways. They can be represented by a solvable formula that provides a relationship between several variables and / or coefficients. The one or several parameters can be one of these variables and / or coefficients, or a term that affects the relationship. If the model has a small number of variables and / or coefficients, they can also be represented by a lookup table. However, with an increased number of variables and / or coefficients, this approach may reach its limits because many relative correlations are possible and the lookup table may have a high dimension. The first model and the second model can also incorporate artificial intelligence methods. For example, the model can include a neural network that has been trained using measurements of various foaming processes from similar foaming mixtures.

[0030] It should be noted that one or several parameters of the first model and the second model can be formed by various things. The parameters should be related to the foam mixture and / or its behavior during the foaming process. The parameters can be constants, they can be related to the characteristics of the foam mixture (changing or constant throughout the foaming process), or they can be related to physical and / or chemical quantities during the foaming process. These examples are provided for illustration only and should not be understood as limiting. It is possible that a parameter of the first model is also a parameter of the second model, and vice versa. Since the parameters are related to the foam mixture and / or its foaming behavior, the shared parameters can be customary and can support the quality of predicting the mold filling behavior.

[0031] One or several parameters of the first model are combined in the first parameter set and one or several parameters of the second model are combined in the second parameter set. The combination of the first parameter set and the second parameter set may contain one or several parameters of the first model and / or one or several parameters of the second model. The combination of the first parameter set and the second parameter set may additionally contain parameters derived from the parameters of the first model and / or the second model. The first parameter set, the second parameter set and / or the combination of the first parameter set and the second parameter set may describe at least part of the behavior of the foam mixture during the foaming process, thereby making it possible to predict the mold filling process.

[0032] In order to determine the first set of parameters and the second set of parameters, the first test is associated with the first model and the second test is associated with the second model. This may mean that the test measures the things modeled by the corresponding model. For example, if the first model models the chemical kinematics and free expansion of the foam mixture, the first test should also be associated with the chemical kinematics and / or free expansion. In one embodiment, the first test measures the height, temperature and / or pressure of the foam mixture during the foaming process. The first model may then also be associated with at least one of these measurements, preferably all three of them. For example, if the second model models the viscosity of the foam mixture, the second test may also be associated with the viscosity. In one embodiment, the second test measures the spread of the foam mixture on an inclined surface. The second model may then also be associated with the spread of the foam mixture.

[0033] "Determining a first parameter set" and / or "determining a second parameter set" can be performed in various ways. "Determining" is based on experimental data and a corresponding model. This means that "determining a first parameter set" establishes a connection between the first parameter set, the first experimental data and the first model, and "determining a second parameter set" establishes a connection between the second parameter set, the second experimental data and the second model. How this connection is established is not essential to the present disclosure. "Determining" can be understood as a matching of a model with corresponding experimental data, where parameters are variables processed for this matching. This can be done by a heuristic algorithm, a Monte Carlo method or a Bayesian estimator. However, it can also be obtained via artificial intelligence, for example using a neural network.

[0034] "Foaming process" refers to the expansion of the foam mixture, i.e. the conversion of the (one or more) initial liquid resin materials of the foam mixture into a (cured) foam matrix by chemical reaction. The foaming process may start with the initiation of a reaction of the components of the foam mixture, e.g. with other components of the foam mixture and / or with the surrounding air. The foaming process may end with the termination of the reaction and / or the curing of the foam. For the purposes of the present disclosure, the foaming process may also be limited to representative stages of the foaming of the foam mixture, e.g. to a period during which the foam undergoes a considerable increase in volume.

[0035] A "foam mixture" may comprise one or several components and may even be a combination of two or more different types of foam. During the foaming process, the components of the foam mixture react with each other and / or with the surrounding air, thus resulting in expansion of the foam mixture. According to one embodiment, the foam mixture comprises polyurethane foam.

[0036] The "properties" of the foam mixture that change during the foaming process can refer to a variety of things. It can refer to various chemical and / or physical properties, such as volume, rise height, pressure, temperature, dielectric polarization, consistency, available amount of reactants, chemical composition, weight, weight loss, flowability, etc. With respect to the present disclosure, the term can be limited to properties that have an impact on the mold filling behavior of the foam mixture, such as volume, rise height, temperature, pressure, and flowability.

[0037] "Mold" can describe various objects that can be filled with foam mixture, and the objects limit the expansion of the foam mixture at least to a certain extent. This means that the cavity is confined in the mold, and the foam mixture can be injected into the cavity. The mold may include one or several openings. A single opening can be formed by a foam mixture receiving opening, and the foam mixture can be injected into the cavity through the foam mixture receiving opening. Other openings can be used to release the gas generated during the foaming process. There can also be openings that do not have direct use, for example, openings here for reducing the weight of the mold. The mold can even be completely opened at one side. In one embodiment, the mold is formed by a vehicle component. Such a vehicle component can be a door, a dashboard, a seat arrangement and / or the like. It should also be noted that the mold does not necessarily have to be composed of a single material. The mold can also be composed of several materials, and can even be defined by another solidified foam mixture at one or several sides.

[0038] "Mold filling process" refers to the process of filling the mold. This may include filling the foam mixture into the mold, i.e. before the foaming process begins. This may include the actual foaming process of filling the mold with the foam. In some embodiments, the mold filling process may be a combination of injecting the foam mixture and a foaming process, since the foam mixture may expand as soon as it leaves the injection device.

[0039] The parameter sets determined by the methods according to the present disclosure can be used in various ways in relation to the mould filling process. Since the parameter sets describe the behaviour of the foam mixture during the foaming process, the parameter sets can be used in any situation in which the flow of the foam mixture is relevant. They can be used to predict the mould filling process in a virtual mould filling simulation system. They can be used to adapt the mould and / or the mould filling process in such a way that the desired mould filling can be achieved. In any case, the parameter sets allow predicting the mould filling behaviour of the foam mixture so that the amount of extensive experimental work required is significantly reduced.

[0040] In one or more embodiments, when determining the first parameter set, the first model is matched to the first experimental data in such a way that a predefined criterion is satisfied. In this way, a termination criterion for the determination step can be provided. Such a predefined criterion can be formed by a threshold value, which should be the maximum difference between the measured value and the corresponding result of the first model. The predefined criterion can also be the least mean square between several measured values ​​and the parameterized first model.

[0041] In one or several embodiments, matching the first model to the first experimental data comprises determining a difference between the first experimental data and the time-dependent behavior determined by the first model, and wherein one or several parameters of the first model are iteratively adapted such that the difference is minimized. In this way, the parameterized first model can describe a time-dependent behavior that is close to the behavior measured during the first test.

[0042] In one or more embodiments, when determining the second parameter set, the second model is matched to the second experimental data in such a way that a predefined criterion is satisfied. In this way, a termination criterion for the determination step can be provided. Such a predefined criterion can be formed by a threshold value, which should be the maximum difference between the measured value and the corresponding result of the second model. The predefined criterion can also be the minimum mean square between several measured values ​​and the parameterized first model.

[0043] In one or several embodiments, matching the second model to the second experimental data comprises determining a difference between the foam propagation determined by the second model and the second experimental data, and wherein one or several parameters of the second model are iteratively adapted such that the difference is minimized. In this way, the parameterized second model can describe a time-dependent behavior and space that is close to the behavior measured during the second test.

[0044] In one or more embodiments, one or more parameters of the first model include at least one of pressure, pressure difference, length difference, volume rate, extension rate, reaction rate, melt viscosity, temperature, weight and density. Pressure can define how the expanding foam mixture is squeezed forward during the foaming process. The pressure difference can define how the pressure at the outer surface of the expanding foam is different from the internal pressure at various points of the expanding foam mixture. The volume rate can indicate how fast the foam mixture expands in all directions. The extension rate can indicate how fast the foam mixture extends in general. The reaction rate can indicate how fast or slow the chemical reaction within the foaming mixture proceeds. The melt viscosity can describe how well the foam mixture flows during the foaming process. The temperature can describe at what temperature the foam mixture starts the foaming process and / or how the temperature develops during the foaming process. The weight can show the amount of foam mixture present in the mold and able to react.

[0045] In one or more embodiments, one or more parameters of the second model include at least one of shear rate, pressure, pressure difference, length difference, volume rate, extension rate, melt viscosity, temperature, weight and density. The shear rate can define how fast the foaming mixture is sheared or deformed during flow. The pressure can define how the foaming mixture that expands during the foaming process is squeezed forward. The pressure difference can define how the pressure at the outer surface of the expanding foam is different from the internal pressure at various points of the expanding foam mixture. The volume rate can indicate how fast the foam mixture expands in all directions. The extension rate can indicate how fast the foam mixture extends in general. The melt viscosity can describe how well the foam mixture flows during the foaming process. The temperature can describe at what temperature the foam mixture starts the foaming process and / or how the temperature develops during the foaming process. The weight can show the amount of foam mixture that is present in the mold and can react.

[0046] In one or several embodiments, at least one parameter in the first parameter set is used to determine the second parameter set. In this way, the determination of the first parameter set can directly support the determination of the second parameter set. In this case, the order of the determination steps may be relevant.

[0047] In one or several embodiments, determining the first set of parameters and / or determining the second set of parameters is performed iteratively, wherein the initial values ​​of the one or several parameters are preferably based on empirical parameters. The iterative determination can be easily implemented on a processor or the like. The empirical parameters may be selected from earlier measurements with similar foam mixtures.

[0048] In one or several embodiments, the method additionally includes inputting at least one of the first parameter set and the combination of the first parameter set and the second parameter set into a mold simulation system, and the mold simulation system uses the input of at least one of the first parameter set and the combination of the first parameter set and the second parameter set to perform an injection molding simulation. The mold simulation system may have a model of the mold. The mold model may be provided by a three-dimensional representation of the mold, for example via CAD (computer-aided design) data. The mold model may additionally include information about the walls of the mold, such as their roughness, their temperature, their flexibility, etc. The mold simulation system may simulate the mold filling process based on the determined parameters. In this way, it can be estimated whether the properties of the mold, the foam mixture and / or the injection should be adapted to obtain better mold filling results.

[0049] In one or more embodiments, the method additionally includes inputting at least one of the first parameter set and the combination of the first parameter set and the second parameter set into the injection molding system, and performing injection molding by the injection molding system using at least one of the first parameter set, the second parameter set, and the combination of the first parameter set and the second parameter set. In this way, the results of parameter determination and / or optimization can be tested at a real object.

[0050] According to another aspect of the invention, the present disclosure also proposes a corresponding system for predicting a mold filling process of a foam mixture, the corresponding system preferably being configured to perform the method as described above, during which the foam mixture expands and several properties of the foam mixture change, the system comprising:

[0051] a first experimental data interface configured to receive first experimental data, the first experimental data comprising first measured values ​​of at least one of the several properties, wherein at least some of the first measured values ​​are measured at different points in time during expansion of the foam mixture, preferably in the cavity,

[0052] a first model interface configured to receive a first model describing a time-dependent behavior of at least one of a number of properties during expansion of the foam mixture, wherein the first model comprises one or several parameters,

[0053] A first parameter determiner is configured to determine a first parameter set based on first experimental data and a first model,

[0054] a second experimental data interface configured to receive second experimental data, the second experimental data comprising second measurements representing the spread of the foam mixture expanding on a predefined surface, wherein the predefined surface is formed by an inclined surface so that the foam mixture flows on the surface by the influence of gravity during the foaming process,

[0055] a second model interface configured to receive a second model describing foam propagation on a predefined inclined surface during expansion of the foam mixture, wherein the second model comprises one or several parameters,

[0056] A second parameter determiner configured to determine a second parameter set based on second experimental data and a second model, and

[0057] An output interface is configured to output at least one of the first parameter set, the second parameter set, and a combination of the first parameter set and the second parameter set to be used in predicting a mold filling process of the foam mixture.

[0058] The advantages of the method described above also apply to this system.

[0059] In one or several embodiments, the system additionally includes a mold simulation system configured to perform an injection molding simulation using at least one of the first parameter set, the second parameter set, and a combination of the first parameter set and the second parameter set.

[0060] According to another aspect of the present invention, the present disclosure also proposes a computer program product comprising instructions, and when the program is executed by a computer, the instructions cause the computer to execute the method as described above.

[0061] According to another aspect of the present invention, the present disclosure also proposes a computer-readable storage medium comprising instructions, and when executed by a computer, the instructions cause the computer to execute the method as described above.

[0062] Further features and embodiments are disclosed in relation to the attached drawings. It should be noted that the following description relates to specific embodiments of the present disclosure and should not be construed as limiting the subject matter claimed. The drawings show:

[0063] Figure 1 is a flow chart of an embodiment of a method according to the present disclosure, and

[0064] Figure 2 is a block diagram of a system according to the present disclosure.

[0065] Figure 1 A flow chart of an embodiment of a method according to the present disclosure is shown. The method starts with a box labeled "Start". In step 100, first experimental data are received. The first experimental data include first measured values ​​of at least one of several properties of the foam mixture, wherein at least some of the first measured values ​​are measured at different time points during a foaming process of the foam mixture. The measured expansion preferably occurs in a cavity, such as a beaker. In step 110, a first model is received. The first model describes the time-dependent behavior of at least one of the several properties of the foam mixture as it changes during the expansion of the foam mixture, wherein the first model includes one or several parameters. The first experimental data and the first model should relate to similar things, such as the free rise behavior of the foam mixture in a beaker.

[0066] In step 120, second experimental data are received. The second experimental data comprise second measured values ​​representing the spread of the foam mixture expanding on a predefined surface. The predefined surface may be formed by an inclined surface. In step 130, a second model is received. The second model describes the foam spread during the foaming process of the foam mixture, preferably on the predefined surface, wherein the second model comprises one or several parameters.

[0067] In step 140, a first set of parameters is determined based on the received first experimental data and the received first model. This may include executing an OD optimization algorithm to calculate optimal kinetic model parameters for the chemical kinetics of the expanding foam. In a general sense, this may be performed by determining one or several differences between the first measured values ​​and the results of the first model. The absolute value of the difference(s) should be less than or equal to a first threshold value, so that the first set of parameters may be considered "optimal". If the parameters are not optimal ("optimal: no"), at least one parameter of the first model is modified in step 150. Thereafter, step 140 is performed again. If the parameters are optimal ("optimal: yes"), the method continues with step 160.

[0068] In step 160, a second parameter set is determined based on the second experimental data and the second model. Additionally, the first parameter set can be used. This step may include using a tilted surface experiment to perform a 3D optimization algorithm for optimizing viscosity model parameters. The determination of the second parameter set may be performed by determining one or more differences between the results of the second measurement and the second model. The absolute value of the (one or more) differences should be less than or equal to a second threshold value, so that the second parameter set can be considered "optimal". The second threshold value may be the same as the first threshold value, so that the first threshold value and the second threshold value may also be identified as threshold values. If the parameter is not optimal ("optimal: no"), at least one parameter of the second model is modified in step 170. Thereafter, step 160 is performed again. If the parameter is optimal ("optimal: yes"), the method continues at step 180.

[0069] In step 180, the determined parameters are used to predict the mold filling process. The determined parameters may include the first parameter set and / or the second parameter set and / or a combination of the first parameter set and the second parameter set. One way to "use" the determined parameters may be to output the determined parameters for use in predicting the mold filling process of the foam mixture. Figure 1 In the step 180, the determined parameters are used for a 3D CFD simulation, i.e. a three-dimensional computational fluid dynamics simulation. In this step, the foam expansion of the foam mixture in a mold (generally a complex mold) and / or on a plane surface in a practical application is predicted based on the determined parameters. However, it should be noted that the determined parameters can also be used in other ways, for example for controlling the injection molding process and / or for adapting an existing mold for optimizing the mold filling behavior.

[0070] exist Figure 1Within the scope of an embodiment of the invention, a modified order of the steps in the method can be implemented, wherein first experimental data are received in step 100 and a first model is received in step 110, whereby a first parameter set is determined based on the received first experimental data and the received first model by skipping steps 120, 130 as indicated by dashed line 110a. After determining the optimal parameters in step 140, the method continues with steps 120 and 130 as indicated by dashed line 140a. With the second experimental data as received in step 120 and the second model as received in step 130, a second parameter set based on the second experimental data and the second model can then be determined in step 160 (dashed line 130a). Of course, since the experiments are independent, it is possible to perform steps 100 and 120 before carrying out the following steps: steps 110, 140 and 150, followed by steps 130, 160, 170, and then 180.

[0071] Figure 2 A data processing device system 1000 configured for predicting a mold filling process of a foam mixture is illustrated. In some implementations, the system 1000 may include one or more servers 1010. The server(s) 1010 may be configured to communicate with one or more client computing platforms 1200 according to a client / server architecture and / or other architectures. The client computing platform(s) 1200 may be configured to communicate with other client computing platforms via the server(s) 1010 and / or according to a peer-to-peer architecture and / or other architectures. A user may access the system 1000 via the client computing platform(s) 1200.

[0072] The server(s) 1010 may be configured via machine readable instructions 1040. The machine readable instructions 1040 may include one or more instruction modules. The instruction modules may include computer program modules. The instruction modules may include one or more of the following: a first experimental data interface 1050, a first model interface 1060, a first parameter determiner 1070, a second experimental data interface 1080, a second model interface 1090, a second parameter determiner 1100, an output interface 1110, a mold simulation system 1120, and / or other instruction modules and / or systems.

[0073] The first experimental data interface 1050 may be configured to receive first experimental data, the first experimental data comprising first measured values ​​of at least one of several characteristics, wherein at least some of the first measured values ​​are measured at different time points during the expansion of the foam mixture, preferably in the cavity. The first model interface 1060 may be configured to receive a first model, the first model describing the time-dependent behavior of at least one of several characteristics during the expansion of the foam mixture, wherein the first model comprises one or several parameters. The first parameter determiner 1070 may be configured to determine a first parameter set based on the first experimental data and the first model. The second experimental data interface 1080 may be configured to receive second experimental data, the second experimental data comprising second measured values ​​representing the propagation of the foam mixture expanding on a predefined surface. The second model interface 1090 may be configured to receive a second model, the second model describing the foam propagation during the expansion of the foam mixture, preferably on a predefined surface, wherein the second model comprises one or several parameters. The second parameter determiner 1100 may be configured to determine a second parameter set based on the second experimental data and the second model. The output interface 1110 may be configured to output at least one of the first parameter set, the second parameter set, and a combination of the first parameter set and the second parameter set to be used to predict a mold filling process of the foam mixture. The mold simulation system 1120 may be configured to perform an injection molding simulation using at least one of the first parameter set, the second parameter set, and a combination of the first parameter set and the second parameter set.

[0074] In some implementations, server(s) 1010, client computing platform(s) 1200, and / or external resources 1210 may be operably linked via one or more electronic communication links. For example, such electronic communication links may be established at least in part via a network such as the Internet and / or other networks. It will be appreciated that this is not intended to be limiting, and the scope of the present disclosure includes implementations in which server(s) 1010, client computing platform(s) 1200, and / or external resources 1210 may be operably linked via some other communication medium.

[0075] A given client computing platform 1200 may include one or more processors configured to execute computer program modules. The computer program modules may be configured to enable an expert or user associated with a given client computing platform 1200 to interact with the system 1000 and / or external resources 1210, and / or to provide other functionality attributed herein to the client computing platform(s) 1200. By way of non-limiting example, a given client computing platform 1200 may include one or more of the following: a desktop computer, a laptop computer, a handheld computer, a tablet computing platform, a netbook, a smart phone, and / or other computing platforms.

[0076] External resources 1210 may include information sources external to system 1000, external entities participating in system 1000, and / or other resources. In some implementations, some or all of the functionality attributed herein to external resources 1210 may be provided by resources included in system 1000.

[0077] Server(s) 1010 may include electronic storage 1020, one or more processors 1030, and / or other components. Server(s) 1010 may include communications links, or ports to enable information to be exchanged with a network and / or other computing platforms. Figure 2 The illustration of the server(s) 1010 in FIG. 1 is not intended to be limiting. The server(s) 1010 may include a number of hardware, software, and / or firmware components that operate together to provide the functionality attributed herein to the server(s) 1010. For example, the server(s) 1010 may be implemented by a cloud as a computing platform on which the server(s) 1010 operate together.

[0078] Electronic storage 1020 may include non-transitory storage media that electronically stores information. The electronic storage media of electronic storage 1020 may include one or both of system storage and / or removable storage, the system storage being provided integrated (i.e., substantially non-removable) with the server(s) 1010, and the removable storage being removably connectable to the server(s) 1010 via, for example, a port (e.g., a USB port, a FireWire port, etc.) or a drive (e.g., a disk drive, etc.). Electronic storage 1020 may include one or more of the following: optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tapes, magnetic hard drives, floppy disk drives, etc.), charge-based storage media (e.g., EEPROM, RAM, etc.), solid-state storage media (e.g., flash drives, etc.), and / or other electronically readable storage media. Electronic storage 1020 may include one or more virtual storage resources (e.g., cloud storage, virtual private networks, and / or other virtual storage resources). Electronic storage 1020 may store software algorithms, information determined by processor(s) 1030, information received from server(s) 1010, information received from client computing platform(s) 1200, and / or other information that enables server(s) 1010 to function as described herein.

[0079] Processor(s) 1030 may be configured to provide information processing capabilities in server(s) 1010. As such, processor(s) 1030 may include one or more of: a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and / or other mechanisms for electronically processing information. Figure 21030 is shown as a single entity, but this is for illustration purposes only. In some implementations, (one or more) processors 1030 may include multiple processing units. These processing units may be physically located within the same device, or (one or more) processors 1030 may represent the processing functionality of multiple devices operating in coordination. (One or more) processors 1030 may be configured to execute modules 1050, 1060, 1070, 1080, 1090, 1100, 1110 and / or 1120 and / or other modules by: software; hardware; firmware; some combination of software, hardware and / or firmware; and / or other mechanisms for configuring the processing capabilities on (one or more) processors 1030. As used herein, the term "module" may refer to any component or set of components that perform the functionality attributed to a module. This may include one or more physical processors, processor-readable instructions, circuits, hardware, storage media, or any other components during the execution of processor-readable instructions.

[0080] It should be appreciated that although the machine readable instructions 1040 implementing modules 1050, 1060, 1070, 1080, 1090, 1100, 1110 and / or 1120 are Figure 2 1030 as being executed within a single processing unit, but in implementations where processor(s) 1030 include multiple processing units, one or more of modules 1050, 1060, 1070, 1080, 1090, 1100, 1110, and / or 1120 may be implemented remotely from the other modules. The description of functionality provided by different modules 1050, 1060, 1070, 1080, 1090, 1100, 1110, and / or 1120 is for purposes of illustration and is not intended to be limiting, as any of modules 1050, 1060, 1070, 1080, 1090, 1100, 1110, and / or 1120 may provide more or less functionality than described.

Claims

1. A method for predicting a mold filling process of a foam mixture, during which the foam mixture expands and several properties of the foam mixture change, the method comprising: receiving first experimental data, the first experimental data comprising first measured values ​​of at least one of the several properties, wherein at least some of the first measured values ​​are measured at different points in time during a foaming process of the foam mixture, preferably in the cavity, receiving a first model describing a time-dependent behavior of at least one of several properties during expansion of the foam mixture, wherein the first model comprises one or several parameters, determining a first parameter set based on first experimental data and a first model, receiving second experimental data, the second experimental data comprising second measurements representing the spread of the foam mixture expanding on a predefined surface, wherein the predefined surface is formed by an inclined surface such that the foam mixture flows on the surface during the foaming process by the influence of gravity, receiving a second model, the second model describing foam propagation on a predefined inclined surface during a foaming process of the foam mixture, wherein the second model comprises one or several parameters, determining a second set of parameters based on the second experimental data and the second model, and At least one of the first parameter set, the second parameter set, and a combination of the first parameter set and the second parameter set is output for use in predicting a mold filling process of the foam mixture.

2. The method according to claim 1, wherein the inclination of the surface relative to the horizontal is greater than or equal to 1°, in particular greater than or equal to 5°, preferably greater than or equal to 10°, and / or The inclination of the surface relative to the horizontal is less than or equal to 50°, in particular less than or equal to 30°, and preferably less than or equal to 20°.

3. A method according to claim 1 or claim 2, wherein when determining the first parameter set, the first model is matched to the first experimental data in such a way that predefined criteria are met.

4. A method according to claim 3, wherein matching the first model to the first experimental data comprises determining a difference between the first experimental data and the time-dependent behavior determined by the first model, and wherein one or several parameters of the first model are iteratively adapted so that the difference is minimized.

5. A method according to any one of claims 1 to 4, wherein when determining the second set of parameters, the second model is matched to the second experimental data in such a way that predefined criteria are met.

6. A method according to claim 5, wherein matching the second model to the second experimental data comprises determining a difference between the foam propagation determined by the second model and the second experimental data, and wherein one or several parameters of the second model are iteratively adapted such that the difference is minimized.

7. The method according to any one of claims 1 to 6, wherein the one or more parameters of the first model include at least one of pressure, pressure difference, length difference, volume rate, extension rate, reaction rate, melt viscosity, temperature, weight and density, and / or The one or several parameters of the second model include at least one of shear rate, extension rate, volume rate, length difference, temperature, weight, density, pressure, pressure difference and melt viscosity.

8. The method according to any one of claims 1 to claim 7, wherein at least one parameter in the first parameter set is used to determine the second parameter set.

9. The method according to any one of claims 1 to 8, wherein determining the first set of parameters and / or determining the second set of parameters is performed iteratively, wherein initial values ​​of one or several parameters are preferably based on empirical parameters.

10. The method according to any one of claims 1 to 9, additionally comprising inputting at least one of the first parameter set and the combination of the first parameter set and the second parameter set into a mold simulation system, and the mold simulation system using the input first parameter set and at least one of the combination of the first parameter set and the second parameter set to perform an injection molding simulation.

11. The method according to any one of claims 1 to 10, additionally comprising inputting at least one of the first parameter set, the second parameter set, and the combination of the first parameter set and the second parameter set into an injection molding system, and performing injection molding by the injection molding system using at least one of the first parameter set, the second parameter set, and the combination of the first parameter set and the second parameter set.

12. A system for predicting a mold filling process of a foamed mixture, preferably configured to perform the method according to one of claims 1 to 11, during which the foamed mixture expands and several properties of the foamed mixture change, the system comprising: a first experimental data interface configured to receive first experimental data, the first experimental data comprising first measured values ​​of at least one of the several properties, wherein at least some of the first measured values ​​are measured at different points in time during expansion of the foam mixture, preferably in the cavity, a first model interface configured to receive a first model describing a time-dependent behavior of at least one of a number of properties during expansion of the foam mixture, wherein the first model comprises one or several parameters, A first parameter determiner is configured to determine a first parameter set based on first experimental data and a first model, a second experimental data interface configured to receive second experimental data, the second experimental data comprising second measurements representing the spread of the foam mixture expanding on a predefined surface, wherein the predefined surface is formed by an inclined surface so that the foam mixture flows on the surface by the influence of gravity during the foaming process, a second model interface configured to receive a second model describing foam propagation on a predefined inclined surface during expansion of the foam mixture, wherein the second model comprises one or several parameters, A second parameter determiner configured to determine a second parameter set based on second experimental data and a second model, and An output interface is configured to output at least one of the first parameter set, the second parameter set, and a combination of the first parameter set and the second parameter set for predicting a mold filling process of the foam mixture.

13. The system of claim 12, additionally comprising a mold simulation system configured to perform an injection molding simulation using at least one of the first parameter set, the second parameter set, and a combination of the first parameter set and the second parameter set.

14. A computer program product comprising instructions which, when the program is executed by a computer, cause the computer to perform the method according to one of claims 1 to 11.

15. A computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to perform the method according to any one of claims 1 to 11.

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