Method and system for predicting the mold filling process of foam mixtures

Abstract

A method for predicting the mold filling process of a foam mixture is disclosed, during which the foam mixture expands and several properties of the foam mixture change. The method of the present disclosure includes receiving first experimental data, which includes first measurements of at least one of several properties, where at least some of the first measurements are preferably measured at different time points during the foaming process of the foam mixture in a cavity; receiving first model, which describes the time-related behavior of at least one of several properties during the expansion of the foam mixture, and which includes one or more parameters; determining first set of parameters based on the first experimental data and the first model; and second measurement, which includes second measurements representing the propagation of the foam mixture expanding on a given surface. The invention includes receiving experimental data, wherein a given surface is formed by an inclined surface such that a foam mixture flows over the surface due to the influence of gravity during the foaming process; receiving a second model describing the foam propagation of the foam mixture during the foaming process on the given inclined surface, the second model comprising one or more parameters; determining a second set of parameters based on the second experimental data and the second model; and outputting at least one of a first set of parameters, a second set of parameters, and a combination of the first and second sets of parameters for use in predicting the mold filling process of the foam mixture. Furthermore, a corresponding system, a computer program product, and a computer-readable storage medium are disclosed.

Description

Technical Field

[0001] The present disclosure relates to a method and system for predicting the mold filling process of a foam mixture, during which the foam mixture expands and some properties of the foam mixture change.

Background Art

[0002] Foam mixtures are widely used. For example, some conceivable applications include use in insulation, soundproofing, and / or reinforcement of structures, and further or alternatively as adhesives and / or filling materials, and may fulfill these simultaneously. An important representative example of such a foam mixture is polyurethane foam. The foam mixture may contain several components or may be a combination of two or more different types of foams. The components of the foam mixture react with each other during the foaming process, thus causing the expansion of the foam mixture. During the foaming process, some properties of the foam mixture, such as temperature, volume, chemical composition, etc., change.

[0003] Often, the foam mixture is used to fill a mold. For example, such a mold may be an automobile door or a vehicle dashboard to be filled with the foam mixture for insulation and soundproofing. When filling the mold, it is desirable that the mold is fully filled with the expanded and cured foam. For this purpose, it is necessary to control the properties of the foaming process, such as temperature, injection pressure, (one or more) injection points, etc. However, these properties are based on the foam mixture used in the mold filling process, the mold to be filled, and various other constraints. This means that in the case of a foam mixture with unknown expansion behavior, several molds have to be filled with mold filling processes of several different specifications until the desired mold filling result is found. This causes a great deal of experimental effort. Changing the mold may result in a different mold filling behavior of the foam mixture, and as a result, new mold filling experiments have to be carried out every time the mold is changed.

[0004] Even after numerous experiments, if a molding process specification that results in sufficient mold filling is found, it remains unclear whether the results are close to optimal mold filling behavior. Similar results may be achievable with smaller amounts of foam mixture by improving the quality of the resulting foam or by making the foam distribution within the mold more uniform. This means that mold filling experiments may not necessarily lead to optimal results.

[0005] Several methods are known in the art that can be used to estimate at least some aspects of the behavior of expanding polymer foam mixtures, such as viscosity. For example, International Publication No. 2021 / 249897 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 the polymer melt. A solvable generalized Newtonian fluid model describes the same at least one rheological property and includes at least one empirical coefficient. The difference between the measured value and the result of the generalized Newtonian fluid model is minimized by iteratively adjusting the empirical coefficient. The empirical coefficient thus determined is input into a solvable Navier-Stokes model. Based on the results of the Navier-Stokes model and the measured value, the empirical coefficient is iteratively further adjusted. Viscosity is an important property of foam mixtures, but it is not sufficient to predict mold filling behavior.

[0006] U.S. Patent No. 5,740,074 relates to a method for filling compartment cavities, such as refrigerator cavities, with a foam produced by the expansion and solidification of a foam mixture consisting of predetermined chemicals. For this purpose, a known amount of the foam mixture is generated in a test chamber cavity, pre-selected parameters (such as free surface height and pressure) are measured, and the average density and apparent viscosity of the foam as a function of time are determined. 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 pre-selected amount of pre-selected foam mixture generated at a pre-selected location in an enclosure cavity having the same geometric shape as the compartment cavity. The method in U.S. Patent No. 5,740,047 characterizes the foaming process by estimating the viscosity of the mixture using only the attributes of a freely rising expanding foam. However, to accurately describe the spatial evolution of the foam front, it is essential to consider the flow behavior in the initial stages of the foaming process. Therefore, using a "semi-empirical approach" to estimate the viscosity of the foam mixture and failing to incorporate essential rheological dynamics limits the possibility of accurately describing the mold filling process that expands the polyurethane foam.

[0007] The research paper "Numerical Simulation of Mold Filling Processes with Polyurethane Foams" (Chem.Eng.Technol.2009,32,No.9,p.1438-1447) takes a similar approach, for example, by using the time evolution of the foaming mixture to characterize the free rise behavior of the expanding foam in a beaker. Additional equations for chemical reaction rates are solved, but the influence of the chemical properties of polyurethane on the flow behavior of the expanding mixture is not explicitly shown. The applied foaming viscosity model is purely theoretical, as no experiments for verifying the model are disclosed. Furthermore, verification in a beaker is often insufficient to fully capture the flow behavior of the expanding foam. Extensive experiments and tests are essential to achieve optimal mold design and the filling behavior of the relevant foaming mixture in the mold. However, it would be beneficial if such extensive trials could be minimized. [Prior art documents] [Patent Documents]

[0008] [Patent Document 1] International Publication No. 2021 / 249897 [Patent Document 2] U.S. Patent No. 5,740,074 [Non-patent literature]

[0009] [Non-Patent Document 1] Numerical Simulation of Mold Filling Processes with Polyurethane Foams(Chem.Eng.Technol.2009,32,No.9,p.1438-1447) [Overview of the Initiative] [Problems that the invention aims to solve]

[0010] Therefore, an object of this disclosure is to provide a method and system that enables the prediction of the mold filling behavior of a foam mixture. [Means for solving the problem]

[0011] This disclosure proposes a method for predicting the mold filling process of a foamed mixture, during which the foamed mixture expands and some properties of the foamed mixture change. Receiving first experimental data including a first measurement of at least one of several characteristics, wherein at least some of the first measurements are preferably measured at different points in time during the foaming process of the foaming mixture in a cavity, A first model that describes the time-related behavior of at least one of several properties of a foaming mixture during expansion, the first model comprising one or more parameters, Based on the first experimental data and the first model, determine the first set of parameters, Receiving the second experimental data, which includes a second measurement representing the propagation of a foaming mixture expanding on a predetermined surface, wherein the predetermined surface is formed by an inclined surface such that the foaming mixture flows on the surface due to the influence of gravity during the foaming process, A second model describing foam propagation during the foaming process of a foaming mixture on a predetermined inclined surface, receiving a second model that includes one or more parameters, Based on the second set of experimental data and the second model, determine the second set of parameters. To be used for predicting the mold filling process of foam mixtures, output at least one of the following: a first set of parameters, a second set of parameters, and a combination of the first and second sets of parameters. Includes.

[0012] With respect to the first and second experimental data and the first and second models, it should be noted that “first” and “second” do not indicate a specific order of data acquisition, a specific order of data and / or models, or a specific order of relevant steps of the method relating to this disclosure. These adjectives merely identify the experimental data and models.

[0013] Furthermore, it should be noted that the use of "and / or" between the 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 both the first and second features.

[0014] It is recognized that the mold-filling behavior of foam mixtures can be derived from basic tests conducted outside the mold. Generally, the mold imposes constraints on 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 variation in the surface structure of the mold may affect the flow behavior of the foam mixture. However, apart from these constraints, the foam mixture behaves similarly both outside and inside the mold. Therefore, values ​​representing the foaming behavior of the foam mixture outside the mold also represent the foaming behavior of the foam mixture inside the mold. In other words, the behavior of the foam mixture inside the mold can be derived based on basic tests conducted outside the mold.

[0015] For this purpose, this disclosure uses first and second experimental data obtained from measurements in the first and second tests. Both tests analyze the behavior of the foaming mixture during the foaming process.

[0016] The first test may relate to the free upward expansion of a foaming mixture. To facilitate measurement, the expansion may be restricted to certain directions. In one embodiment, the expansion is carried out, for example, in a cavity formed by a beaker. Such a test can determine the chemical rheological and / or dynamical properties of the foaming process. A measuring device can be used in this first test. This measuring device may comprise a base plate, heating (e.g., incorporated into the base plate), and various sensors. The beaker can be placed on the base plate, and changes in the properties of the foaming mixture can be monitored by the sensors. The sensors may comprise dielectric sensors, pressure sensors, temperature sensors, and / or height sensors (e.g., laser or ultrasonic sensors placed above the beaker). The acquired data may be further used by a control unit of the measuring device. One possible commercially available measuring device is distributed as FOAMAT® by Format Messtechnik GmbH, Karlsruhe, Germany.

[0017] Such a first test or another first test generates a first measurement, which is part of the first experimental data. The first measurement generally includes several values, which are measured by different sensors and / or may include values ​​measured at different points in time during the expansion of the foam mixture. The first experimental data can be input into the method according to the disclosure, and / or into the first experimental data interface of the system according to the disclosure.

[0018] The second test may relate to the flow behavior of the foam mixture during the foaming process. During the second test, the foam mixture can be expanded on a given surface, and the propagation of the foam mixture along the surface can be measured. The expansion can occur freely, i.e., only under the influence of the given surface in a given manner. Using knowledge of the given surface, the measured foam propagation provides information about the flow behavior of the foam during the foaming process. The given surface can be embodied in various ways, insofar as it influences the expanding foam mixture in a predetermined deterministic manner. According to the present invention, the given surface is formed by an inclined surface such that the foam mixture flows on the surface under the influence of gravity during the foaming process. This test has the advantage that the results focus on the rheological properties of the foam mixture rather than the expansion properties of the foam mixture. The inclination must be large enough to influence the flow of the foam. However, the inclination must be small enough so that the foam does not flow too fast and measurements can be taken during the foaming process. In one embodiment, the inclination is 1° or more with respect to the horizontal direction; in another embodiment, the inclination is 5° or more; and in yet another embodiment, the inclination is 10° or more. In one embodiment, the inclination is 50° or less with respect to the horizontal direction; in another embodiment, the inclination is 30° or less; and in yet another embodiment, the inclination is 20° or less.

[0019] This second test, or a similar second test, generates second measurements, which are part of the second experimental data. The second measurements generally include several values. The second measurements may refer to different properties of the foam mixture and / or may be measured at different points in time during the expansion of the foam mixture. In addition to information regarding the propagation of the foam mixture during the second test, the second measurements may refer to further properties such as temperature, volume of the foam mixture, width of the foam mixture, etc. The second experimental data can be input into the method according to the disclosure, and / or into a second experimental data interface of the system according to the disclosure.

[0020] According to an advantageous embodiment, the second measurement value is generated by capturing a time series image showing the advancement of the front surface of the foam in the flow situation on a predetermined surface. By implementing an image recognition algorithm, the advancement of the front surface of the foam can be analyzed to extract dimensional information and rheological behavior of the flowing foam mixture. For example, it is possible to design the predetermined surface such that the tracking of the advancing front surface of the foam by the image recognition algorithm can be achieved relatively easily.

[0021] The image recognition methods and algorithms applicable here are known in the art. For example, 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 describes several use cases of various domains and technologies for solving computer vision-related tasks. 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 architectures specifically tailored to 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 methods for solving image segmentation problems. Since the field of computer vision overlaps with engineering, the underlying hardware configuration for image analysis is crucial for the successful implementation of relevant image recognition algorithms. Therefore, this paper provides a specific hardware configuration for new challenges in image analysis.

[0022] The first model describes the time-related behavior of at least one of several properties of the foaming mixture that change during the foaming process. The first model includes one or several parameters, the values of which are related to the changing properties of the foaming mixture. The second model describes the foam propagation during the expansion of the foaming mixture. The second model includes one or several parameters, the values of which are related to the propagation of the foaming mixture. The term "propagation" refers to the movement of the foaming mixture parallel to a given surface. This can represent how far the outer boundary of the foaming mixture has moved in a given direction within a certain time.

[0023] Regarding the "first model" and the "second model", it should be noted that these models generally describe the specific behavior of the foaming mixture under specific conditions. This means that the models describe different aspects of the behavior of the foaming mixture. This can be achieved in various ways. The first and second models may be completely independent of each other, i.e., the models are based on different assumptions or modeling methods. However, the first and second models may also be based on the same modeling method, i.e., the models may originate from the same way of thinking. In this regard, both models may originate from a more general way of thinking, and the first model may be derived as a first subset or a first generalization or a first simplification, and the second model may be derived as a second subset or a second generalization or a second simplification. However, the first model and the second model may also be derived from each other, for example, one of those models is a more general version of the other model.

[0024] The first and / or second models may be based on the modeling schemes disclosed in Clement Raimbault et al: “Foaming parameter identification of polyurethane using FOAMAT® device”, Polym Eng Sci. 2021;61:1243-1265 or Joe Wang: “Combination of PU System with CAE Simulation for Accurate PU Foaming Prediction”, 2021, https: / / www.moldex3d.com / combination-of-pu-foamat-system-with-cae-simulation-for-accurate-pu-foaming-prediction / or R. Rao et al: “Density predictions using a finite element / level set model of polyurethane foam expansion and polymerization”, Computers and Fluids (2018), 175:20-35.

[0025] The first and second models may be represented in various ways. They can be represented by solvable formulas that provide relationships between several variables and / or coefficients. One or more parameters may be one of these variables and / or coefficients, or terms that influence this relationship. If the number of variables and / or coefficients in the model is small, they can also be represented by a reference table. However, as the number of variables and / or coefficients increases, many relative dependencies can be considered, and this method may reach its limits as the reference table can become high-dimensional. The first and second models may incorporate artificial intelligence techniques. The models may include, for example, neural networks trained on measurements from various foaming processes of similar foaming mixtures.

[0026] It should be noted that one or more parameters of the first and second models can be formed by various things. The parameters should relate to the foaming mixture and / or its behavior during the foaming process. The parameters may be constants and can relate to the properties of the foaming mixture (which change or remain constant throughout the foaming process), or to physical and / or chemical quantities during the foaming process. These examples are provided merely for clarification and should not be understood as limitations. It is possible that the parameters of the first model are also parameters of the second model, and vice versa. Since the parameters relate to the foaming mixture and / or its foaming behavior, it is common for parameters to be shared and can support the quality of prediction of mold filling behavior.

[0027] One or more parameters of the first model are incorporated into the first set of parameters, and one or more parameters of the second model are incorporated into the second set of parameters. The combination of the first and second sets of parameters may include one or more parameters of the first model and / or one or more parameters of the second model. The combination of the first and second sets of parameters may further include parameters derived from the parameters of the first and / or second models. The first set of parameters, the second set of parameters, and / or the combination of the first and second sets of parameters can describe at least a portion of the behavior of the foam mixture during the foaming process so that the mold filling process can be predicted.

[0028] With regard to the determination of the first and second sets of parameters, the first test relates to the first model, and the second test relates to the second model. This may mean that the test measures something modeled by the corresponding model. For example, if the first model models the chemical dynamics and free expansion of the foaming mixture, then the first test should also relate to the chemical dynamics and / or free expansion. In one embodiment, the first test measures the height, temperature, and / or pressure of the foaming mixture during the foaming process. Thus, the first model may also relate to at least one, preferably all three, of these measurements. For example, if the second model models the viscosity of the foaming mixture, then the second test should also relate to viscosity. In one embodiment, the second test measures the propagation of the foaming mixture on an inclined surface. Thus, the second model may also relate to the propagation of the foaming mixture.

[0029] The determination of the first set of parameters and / or the determination of the second set of parameters can be carried out in various ways. The determination is based on experimental data and the respective models. This means that by determining the first set of parameters, a link is established between the first set of parameters, the first set of experimental data, and the first model, and by determining the second set of parameters, a link is established between the second set of parameters, the second set of experimental data, and the second model. How this link is established is not essential to this disclosure. The determination can be understood as a matching of the model with the corresponding experimental data, and the parameters are variables in this matching process. This can be done by heuristic algorithms, Monte Carlo methods, or Bayesian estimators. However, it can also be obtained through artificial intelligence, for example, using neural networks.

[0030] The “foaming process” refers to the expansion of the foaming mixture, i.e., the conversion of the foaming mixture into a (cured) foaming matrix by the chemical reaction of (one or more) initial liquid resin materials. The foaming process may begin with the initiation of a reaction between the components of the foaming mixture and, for example, other components of the foaming mixture and / or the surrounding air. The foaming process may end with the completion of the reaction and / or curing of the foam. For the purposes of this disclosure, the foaming process may be limited to a typical stage of foaming of the foaming mixture, e.g., a period during which the foam undergoes a significant volume increase.

[0031] The "foaming mixture" may contain one or more components, and may be a combination of two or more different types of foams. These components of the foaming mixture react with each other and / or with the surrounding air during the foaming process, thus resulting in the expansion of the foaming mixture. According to one embodiment, the foaming mixture includes a polyurethane foam.

[0032] The “properties” of a foam mixture that change during the foaming process can refer to a variety of things. These can refer to a variety of chemical and / or physical properties, such as volume, rise height, pressure, temperature, dielectric polarization, consistency, amount of available reactants, chemical composition, weight, weight loss, and fluidity. For the purposes of this disclosure, this term may be limited to properties that have an effect on the mold filling behavior of the foam mixture, such as volume, rise height, temperature, pressure, and fluidity.

[0033] The term "mold" can refer to various objects that can be filled with a foaming mixture and that limit the expansion of the foaming mixture to at least a certain extent. This means that a cavity into which the foaming mixture can be injected is defined within the mold. The mold may have one or more openings. A single opening may be formed by a foaming mixture receiving opening into which the foaming mixture can be injected. Other openings can be used to release gases produced during the foaming process. There may also be openings that are not directly used, such as openings to reduce the weight of the mold. The mold may be completely open on one side. In one embodiment, the mold is formed from a vehicle part. Such a vehicle part may be a door, dashboard, seating unit, etc. It should also be noted that the mold does not necessarily have to be made of a single material. The mold may be made of several materials and one or more sides may be bordered by another already cured foaming mixture.

[0034] The “mold filling process” refers to the process of filling a mold. This may include filling the mold with the foam mixture, i.e., before the start of the foaming process. 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 the injection of the foam mixture and the foaming process, as the foam mixture may expand as soon as it leaves the injection device.

[0035] The set of parameters determined by the method of this disclosure can be used in various ways in relation to a mold filling process. Since the set of parameters describes the behavior of the foam mixture during the foaming process, it can be used in any case where the flow of the foam mixture is relevant. They can be used to predict the mold filling process in a virtual mold filling simulation system. They can be used to tune the mold and / or the mold filling process in a way that can achieve the desired mold filling. In any case, the set of parameters enables the prediction of the mold filling behavior of the foam mixture so as to significantly reduce the amount of extensive experimental work required.

[0036] In one or more embodiments, in determining a first set of parameters, a first model is matched with first experimental data in such a way that a predetermined criterion is met. In this way, a criterion for ending the determination step can be provided. Such a predetermined criterion may be formed by a threshold that should be the maximum difference between the measured values ​​and the corresponding results of the first model. The predetermined criterion may also be the least mean squares between some measured values ​​and the parameterized first model.

[0037] In one or more embodiments, matching the first model with first experimental data involves determining the difference between the first experimental data and the time-related behavior determined by the first model, and iteratively adjusting one or more parameters of the first model to minimize this difference. In this way, the parameterized first model can describe time-related behavior that is close to the behavior measured in the first test.

[0038] In one or more embodiments, in determining a second set of parameters, a second model is compared with second experimental data in such a way that a predetermined criterion is met. This provides a criterion for ending the determination step. Such a predetermined criterion may be formed by a threshold that represents the maximum difference between the measured values ​​and the corresponding results of the second model. The predetermined criterion may also be the least mean squares between some measured values ​​and the parameterized first model.

[0039] In one or more embodiments, the matching of the second model with the second experimental data involves determining the difference between the foam propagation determined by the second model and the second experimental data, and iteratively adjusting one or more parameters of the second model to minimize this difference. In this way, the parameterized second model can describe spatial and temporal behavior that is close to the behavior measured in the second test.

[0040] In one or more embodiments, one or more parameters of the first model include at least one of pressure, pressure difference, length difference, volumetric flow rate, extension rate, reaction rate, melt viscosity, temperature, weight, and density. Pressure can determine how the expanding foam mixture is pushed forward during the foaming process. Pressure difference can determine how the pressure on the outer surface of the expanding foam differs from the internal pressure at various points in the expanding foam mixture. Volumetric flow rate can indicate how quickly the foam mixture is expanding in all directions. Extension rate can indicate how quickly the foam mixture is extending in general. Reaction rate can indicate how quickly or slowly the chemical reactions within the foam mixture are progressing. Melt viscosity can represent how well the foam mixture flows during the foaming process. Temperature can represent the temperature of the foam mixture at the start of the foaming process and / or how the temperature progresses during the foaming process. Weight can indicate the amount of foam mixture present in the mold that can react.

[0041] 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, volumetric flow rate, extension rate, melt viscosity, temperature, weight, and density. The shear rate can determine how quickly the foam mixture is sheared or deformed during flow. The pressure can determine how the expanding foam mixture is pushed forward during the foaming process. The pressure difference can determine how the pressure on the outer surface of the expanding foam differs from the internal pressure at various points in the expanding foam mixture. The volumetric flow rate can indicate how quickly the foam mixture is expanding in all directions. The extension rate can indicate how quickly the foam mixture is extending in general. The melt viscosity can represent how well the foam mixture flows during the foaming process. The temperature can represent the temperature of the foam mixture at the start of the foaming process and / or how the temperature progresses during the foaming process. The weight can represent the amount of foam mixture present in the mold and capable of reacting.

[0042] In one or more embodiments, at least one parameter from the first set of parameters is used in determining the second set of parameters. In this way, determining the first set of parameters can directly assist in determining the second set of parameters. In this case, the order of the determination steps may be relevant.

[0043] In one or more embodiments, the determination of a first set of parameters and / or a second set of parameters is performed iteratively, and the initial values ​​of one or more parameters are preferably based on empirical parameters. The iterative determination can be easily carried out in a processor or the like. The empirical parameters can be selected from previous measurements using similar foaming mixtures.

[0044] In one or more embodiments, the method further includes inputting a first set of parameters and at least one of the first and second sets of parameter combinations into a mold simulation system, and having the mold simulation system perform an injection molding simulation using the inputs of the first set of parameters and at least one of the first and second sets of parameter combinations. The mold simulation system may have a mold model. The mold model can be provided by a three-dimensional representation of the mold, for example, via CAD (computer-aided design) data. The mold model may further include information about the mold walls, such as roughness, temperature, and flexibility. The mold simulation system can simulate the mold filling process based on the determined parameters. In this way, it is possible to estimate whether the characteristics of the mold, foam mixture, and / or injection should be adjusted to obtain better mold filling results.

[0045] In one or more embodiments, the method further includes inputting a first set of parameters and at least one combination of the first and second sets of parameters into an injection molding system, and having the injection molding system perform injection molding using the first set of parameters, the second set of parameters, and at least one combination of the first and second sets of parameters. In this way, the results of parameter determination and / or optimization can be tested on actual objects.

[0046] According to another aspect of the present invention, the present disclosure also proposes a corresponding system for predicting the mold filling process of a foam mixture, preferably configured to perform the method described above, during the foaming process the foam mixture expands and some properties of the foam mixture change, and the system A first experimental data interface is configured to receive first experimental data, the first experimental data comprising first measurements of at least one of several properties, and at least some of the first measurements preferably measured at different points in time during the expansion of the foaming mixture in the cavity. A first model interface is configured to receive a first model, the first model describing the time-relevant behavior of at least one of several properties during the expansion of a foaming mixture, and the first model includes one or more parameters. A first parameter determination system configured to determine a first set of parameters based on first experimental data and a first model, A second experimental data interface is configured to receive second experimental data, the second experimental data including second measurements representing the propagation of a foaming mixture expanding on a given surface, the given surface being formed by an inclined surface such that the foaming mixture flows on the surface due to the influence of gravity during the foaming process, A second model interface is configured to receive a second model, the second model describing foam propagation during the expansion of a foam mixture on a given inclined surface, and the second model includes one or more parameters. A second parameter determination system configured to determine a second set of parameters based on second experimental data and a second model, An output interface configured to output at least one of a first set of parameters, a second set of parameters, and a combination of the first and second sets of parameters, for use in predicting the mold filling process of a foam mixture. It is equipped with.

[0047] The advantages of the method described above also apply to this system.

[0048] In one or more embodiments, the system further comprises a mold simulation system configured to perform an injection molding simulation using at least one of a first set of parameters, a second set of parameters, and a combination of the first and second sets of parameters.

[0049] According to another aspect of the present invention, the present disclosure further proposes a computer program product comprising instructions, the instructions causing the computer to perform the above-described method when the program is executed by the computer.

[0050] According to another aspect of the present invention, the present disclosure further proposes a computer-readable storage medium containing instructions, which, when executed by the computer, cause the computer to perform the method described above.

[0051] Further features and embodiments are disclosed in connection with the accompanying drawings. It should be noted that the following description refers to specific embodiments of this disclosure and should not be understood as an limitation of the claimed subject matter. [Brief explanation of the drawing]

[0052] [Figure 1] A flowchart of one embodiment of the method according to this disclosure is shown. [Figure 2] This disclosure shows a block diagram of the system. [Modes for carrying out the invention]

[0053] Figure 1 shows a flowchart of one embodiment of the method according to the present disclosure. The method begins with a block labeled “Start”. In step 100, first experimental data is received. The first experimental data includes a first measurement of at least one of several properties of the foaming mixture, at least some of the first measurements being taken at different points in time during the foaming process of the foaming mixture. The expansion to be measured is preferably carried out in a cavity, such as a beaker. In step 110, a first model is received. The first model describes the time-related behavior of at least one of several properties of the foaming mixture that changes during the expansion of the foaming mixture, and the first model includes one or more parameters. The first experimental data and the first model should refer to similar things, for example, the free-rising behavior of the foaming mixture in a beaker.

[0054] In step 120, second experimental data is received. The second experimental data includes second measurements representing the propagation of a foaming mixture expanding on a predetermined surface. The predetermined surface can be formed by an inclined surface. In step 130, a second model is received. The second model preferably describes the propagation of foam during the foaming process of the foaming mixture on the predetermined surface, and the second model includes one or more parameters.

[0055] In step 140, a first set of parameters is determined based on the first experimental data received and the first model received. This may involve running an OD optimization algorithm to calculate the optimal dynamics model parameters for the reaction kinetics of the expanding foam. In general terms, this may be done by determining one or more differences between the first measurements and the results of the first model. The absolute value of the (one or more) differences should be less than or equal to a first threshold for the first set of parameters to be considered "optimal". If the parameters are not optimal ("optimal: no"), in step 150, at least one parameter of the first model is modified. Then, step 140 is performed again. If the parameters are optimal ("optimal: yes"), the method proceeds to step 160.

[0056] In step 160, a second set of parameters is determined based on second experimental data and a second model. The first set of parameters can also be used. This step may include running a 3D optimization algorithm for viscosity model parameters optimized using inclined surface experiments. The determination of the second set of parameters may be performed by determining one or more differences between second measurements and the results of the second model. The absolute value of the (one or more) differences should be less than or equal to a second threshold for the second set of parameters to be considered "optimal". The second threshold may be the same as the first threshold, and therefore the first and second threshold values ​​may be identified as threshold values. If the parameters are not optimal ("optimal: no"), in step 170, at least one parameter of the second model is modified. Then, step 160 is performed again. If the parameters are optimal ("optimal: yes"), the method proceeds to step 180.

[0057] In step 180, the determined parameters are used to predict the mold filling process. The determined parameters may include a first set of parameters and / or a second set of parameters and / or a combination of the first and second sets of parameters. One way to "use" the determined parameters is to output them for use in predicting the mold filling process of the foam mixture. In Figure 1, the determined parameters are used in step 180 for a 3D CFD simulation, i.e., a three-dimensional computational fluid dynamics simulation. In this step, based on the determined parameters, the foam expansion of the foam mixture in the mold (generally a complex mold) and / or on the plane in a real-world application is predicted. However, it should be noted that the determined parameters can also be used in other ways, for example, in tuning existing molds to control the injection molding process and / or optimize the mold filling behavior.

[0058] Within the scope of the embodiment in Figure 1, if the first experimental data is received in step 100 and the first model is received in step 110, the order of the steps in this method may be changed by skipping steps 120 and 130, as shown by the dashed line 110a, thereby determining a first set of parameters based on the received first experimental data and the received first model. After the optimal parameters are determined in step 140, the method proceeds to steps 120 and 130, as shown by the dashed line 140a. With the second experimental data received in step 120 and the second model received in step 130, a second set of parameters based on the second experimental data and the second model can be determined in step 160 (dashed line 130a). Of course, since the experiments are independent, it is also possible to perform steps 100 and 120 before proceeding to steps 110, 140, and 150, and the subsequent steps 130, 160, 170, and 180.

[0059] Figure 2 shows a data processing system 1000 configured to predict the mold filling process of a foam mixture. In some embodiments, the system 1000 may include one or more servers 1010. One or more servers 1010 may be configured to communicate with one or more client computing platforms 1200 according to a client / server architecture and / or other architecture. One or more client computing platforms 1200 may be configured to communicate with other client computing platforms via one or more servers 1010 and / or according to a peer-to-peer architecture and / or other architecture. Users can access the system 1000 via one or more client computing platforms 1200.

[0060] A server 1010 (one or more) may consist of machine-readable instructions 1040. The machine-readable instructions 1040 may include one or more instruction modules. An instruction module may include a computer program module. An instruction module 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.

[0061] A first experimental data interface 1050 may be configured to receive first experimental data, which includes a first measurement of at least one of several properties, at least some of which are preferably measured at different points in time during the expansion of the foam mixture in a cavity. A first model interface 1060 may be configured to receive a first model, which describes the time-related behavior of at least one of several properties during the expansion of the foam mixture, and which includes one or more parameters. A first parameter determiner 1070 may be configured to determine a first set of parameters based on the first experimental data and the first model. A second experimental data interface 1080 may be configured to receive second experimental data, which includes a second measurement representing the propagation of the foam mixture expanding on a given surface. A second model interface 1090 may be configured to receive a second model, which preferably describes the propagation of foam during the expansion of a foam mixture on a given surface, and which includes one or more parameters. A second parameter determiner 1100 may be configured to determine a second set of parameters based on second experimental data and the second model. An output interface 1110 may be configured to output at least one of a first set of parameters, a second set of parameters, and a combination of the first and second sets of parameters, for use in predicting the mold filling process of the foam mixture. A mold simulation system 1120 may be configured to perform an injection molding simulation using at least one of the first set of parameters, a second set of parameters, and a combination of the first and second sets of parameters.

[0062] In some embodiments, the (one or more) servers 1010, the (one or more) client computing platforms 1200, and / or external resources 1210 may be operationally 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 understood that this is not intended to limit the scope of the disclosure, and that the scope of the present disclosure may include embodiments in which the (one or more) servers 1010, the (one or more) client computing platforms 1200, and / or external resources 1210 may be operationally linked via some other medium of communication.

[0063] A given client computing platform 1200 may include one or more processors configured to run computer program modules. These computer program modules may enable experts or users associated with the given client computing platform 1200 to interact with system 1000 and / or external resources 1210, and further / or provide other functions attributed to the client computing platform 1200 (one or more) as described herein. In non-limiting examples, a given client computing platform 1200 may include one or more desktop computers, laptop computers, handheld computers, tablet computing platforms, netbooks, smartphones, and / or other computing platforms.

[0064] External resource 1210 may include external information sources, external entities involved with system 1000, and / or other resources. In some embodiments, some or all of the functions attributed to external resource 1210 herein may be provided by resources included in system 1000.

[0065] The (one or more) server 1010 may include an electronic storage device 1020, one or more processors 1030, and / or other components. The (one or more) server 1010 may include communication lines or ports that enable the exchange of information with a network and / or other computing platform. The (one or more) server 1010 examples in Figure 2 are not intended to be limiting. The (one or more) server 1010 may include multiple hardware, software, and / or firmware components that work together to provide the functionality attributed to the (one or more) server 1010 as described herein. For example, the (one or more) server 1010 may be implemented by a cloud of computing platforms collaborating as the (one or more) server 1010.

[0066] The electronic storage device 1020 may include a non-temporary storage medium for electronically storing information. The electronic storage medium of the electronic storage device 1020 may include one or both of the following: a system storage device integrated with (i.e., substantially inremovable) the (one or more) servers 1010, and / or a removable storage device that can be detachably connected to the (one or more) servers 1010 via, for example, a port (e.g., a USB port, a FireWire port, etc.) or a drive (e.g., a disk drive, etc.). The electronic storage device 1020 may include one or more of the following: an optically readable storage medium (e.g., an optical disc, etc.), a magnetically readable storage medium (e.g., a magnetic tape, a magnetic hard drive, a floppy drive, etc.), an charge-based storage medium (e.g., an EEPROM, RAM, etc.), a solid-state storage medium (e.g., a flash drive, etc.), and / or other electronically readable storage media. The electronic storage device 1020 may include one or more virtual memory resources (e.g., cloud storage, a virtual private network, and / or other virtual memory resources). The electronic storage device 1020 can store software algorithms, information determined by (one or more) processors 1030, information received from (one or more) servers 1010, information received from (one or more) client computing platforms 1200, and / or other information that enables (one or more) servers 1010 to function as described herein.

[0067] The (one or more) processor 1030 may be configured to provide information processing capabilities in the (one or more) server 1010. Therefore, the (one or more) processor 1030 may include one or more of the following: 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. Although the (one or more) processor 1030 is shown as a single entity in Figure 2, this is for illustrative purposes only. In some embodiments, the (one or more) processor 1030 may include multiple processing units. These processing units may be physically located within the same device, or the (one or more) processor 1030 may represent the processing capabilities of multiple devices working together. One or more processors 1030 may be configured to run modules 1050, 1060, 1070, 1080, 1090, 1100, 1110, and / or 1120, and / or other modules, by software, hardware, firmware, any combination of software, hardware, and / or firmware, and / or other mechanisms for configuring the processing power on one or more processors 1030. As used herein, the term “module” can refer to any component or set of components that perform the function attributed to the module. This may include one or more physical processors executing processor-readable instructions, processor-readable instructions, circuitry, hardware, storage media, or any other components.

[0068] In Figure 2, machine-readable instructions 1040 implementing modules 1050, 1060, 1070, 1080, 1090, 1100, 1110, and / or 1120 are shown as being executed within a single processing unit. However, in embodiments where the (one or more) processor 1030 includes multiple processing units, it should be understood that one or more of modules 1050, 1060, 1070, 1080, 1090, 1100, 1110, and / or 1120 may be implemented remotely from the other modules. The descriptions of the functions provided by the various modules 1050, 1060, 1070, 1080, 1090, 1100, 1110, and / or 1120 are illustrative and not intended to be limiting, and any of modules 1050, 1060, 1070, 1080, 1090, 1100, 1110, and / or 1120 may provide more or fewer functions than those described.

Claims

1. A method for predicting the mold filling process of a foaming mixture, wherein during the foaming process, the foaming mixture expands and some properties of the foaming mixture change, and the method is Receiving first experimental data including a first measurement of at least one of the aforementioned characteristics, wherein at least some of the first measurements are preferably measured in a cavity at different points in time during the foaming process of the foaming mixture, A first model for describing the time-related behavior of at least one of several properties during the expansion of the foam mixture, the first model comprising one or more parameters, Based on the first experimental data and the first model, a first set of parameters is determined, Receiving the second experimental data, which includes a second measurement representing the propagation of the foam mixture expanding on a predetermined surface, wherein the predetermined surface is formed by an inclined surface such that the foam mixture flows on the surface due to the influence of gravity during the foaming process, A second model for describing the foam propagation during the foaming process of the foam mixture on the predetermined inclined surface, the second model comprising one or more parameters, Based on the second experimental data and the second model, a second set of parameters is determined, Output at least one of the first set of parameters, the second set of parameters, and combinations of the first and second sets of parameters, so as to be used to predict the mold filling process of the foam mixture. Methods that include...

2. The inclination of the surface is 1° or more, particularly 5° or more, preferably 10° or more, with respect to the horizontal direction, and / or The method according to claim 1, wherein the inclination of the surface is 50° or less, particularly 30° or less, and preferably 20° or less, with respect to the horizontal direction.

3. The method according to claim 1 or 2, wherein, in determining the first set of parameters, the first model is compared with the first experimental data in such a manner that predetermined criteria are met.

4. The method according to claim 3, wherein the matching of the first model with the first experimental data determines the difference between the first experimental data and the time-related behavior determined by the first model, and one or more parameters of the first model are iteratively adapted to minimize the difference.

5. The method according to any one of claims 1 to 4, wherein, in determining the second set of parameters, the second model is compared with the second experimental data in such a manner that predetermined criteria are met.

6. The method of claim 5, wherein the comparison of the second model with the second experimental data determines the difference between the foam propagation determined by the second model and the second experimental data, and one or more parameters of the second model are iteratively adapted to minimize the difference.

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

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

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

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

11. The method according to any one of claims 1 to 10, further comprising inputting at least one of the first set of parameters, the second set of parameters, and combinations of the first and second sets of parameters into an injection molding system, and having the injection molding system perform injection molding using the first set of parameters, the second set of parameters, and at least one combination of the first and second sets of parameters.

12. A system for predicting the mold filling process of a foam mixture, preferably configured to perform the method according to any one of claims 1 to 11, wherein during the foaming process, the foam mixture expands and several properties of the foam mixture change, and the system A first experimental data interface is configured to receive first experimental data, the first experimental data comprising first measurements of at least one of several characteristics, and at least some of the first measurements being taken preferably in a cavity at different points in time during the expansion of the foam mixture. A first model interface is configured to receive a first model, the first model describing the time-related behavior of at least one of several properties during the expansion of the foam mixture, and the first model includes one or more parameters. A first parameter determination system configured to determine a first set of parameters based on the first experimental data and the first model, A second experimental data interface is configured to receive second experimental data, the second experimental data including second measurements representing the propagation of the foam mixture expanding on a predetermined surface, the predetermined surface being formed by an inclined surface such that the foam mixture flows on the surface due to the influence of gravity during the foaming process, A second model interface is configured to receive a second model, the second model describing foam propagation during the expansion of the foam mixture on a predetermined inclined surface, and the second model includes one or more parameters. A second parameter determination system configured to determine a second set of parameters based on the second experimental data and the second model, An output interface configured to output at least one of the first set of parameters, the second set of parameters, and combinations of the first and second sets of parameters, for use in predicting the mold filling process of the foam mixture. A system equipped with these features.

13. The system according to claim 12, further comprising a mold simulation system configured to perform an injection molding simulation using the first set of parameters, the second set of parameters, and at least one of the combinations of the first and second sets of parameters.

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

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

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