Method for generating and / or adapting a geometric meta-model for a moulded part to be produced in a moulding process, in particular for an injection-moulded part to be produced in an injection moulding process

Abstract

The invention relates to a method for generating and / or adapting a geometric meta-model for a moulded part, to be produced in a moulding process, for use as simulation geometry, on the basis of process data, wherein in a learning process step, an accepted part is produced, the accepted part process is characterised and, to determine a meta-model, in a geometry determination step at least one of the learned characteristics is iteratively approached with intermediate geometries, in simulations, until a sufficiently precise meta-model has been achieved, wherein a virtual shape is used, which is at least a shape following geometric rules or is put together from same.

Description

[0001] Method for generating and / or adapting a geometric meta-model for a molded part to be produced in a primary molding process, in particular for an injection molded part to be produced in an injection molding process

[0002] The invention relates to a method for generating and / or adapting a geometric meta-model for a molded part to be produced in a primary molding process, in particular for an injection-molded part to be produced in an injection-molding process, wherein first a learning process step is carried out and after the learning process step a determination of the geometric meta-model is carried out.

[0003] In the area of ​​networking injection molding simulation tools, or the results calculated from them, with injection molding machines, it is generally known to use simulation data to perform machine setup, for example, by setting target values ​​and / or parameters. It is also known to compare simulation data with real-life tests on the injection molding machine.

[0004] Furthermore, software products are known that are used to configure the injection molding process, i.e., the actual injection molding process, based on manually performed simulations. The basis for the state-of-the-art simulations is previously known spatial data, for example, CAD data, of the component to be manufactured and / or spatial data, for example, CAD data, of an injection mold cavity.

[0005] For example, EP 3 291 959 B1 describes the determination and display of process parameter values ​​in an injection molding process. This involves characterizing pressure curves from the simulation and a real process.

[0006] From EP 3 892 440 A1 a method for adjusting an injection molding machine is known, which is also based on the provision and / or selection of at least one 3D model of at least one geometry of at least one molded part to be molded and / or at least one geometry of at least one mold cavity.

[0007] DE 102021 109 851 A1 discloses a method and a computer program product for comparing a simulation with the actual process. For this purpose, at least one simulation curve of a characteristic variable for the process, in particular a simulated pressure curve, is calculated within the simulation. During the actual process, at least one measurement curve of the characteristic variable, in particular a measured pressure curve, is measured, and a correlation is established between the simulation process and the actual process based on characteristic points of the simulation curve and the measurement curve.

[0008] From AT 513.481 B1 a method for simulating an injection molding process is known, wherein in a machine simulation for simulating the injection molding machine, optionally without an injection molding tool, at least one first parameter is calculated in an injection unit of an injection molding machine to be simulated on the basis of a physical behavior model, and in a process simulation for simulating the injection molding material to be processed and / or an injection molding tool, at least one second parameter, preferably a pressure, is calculated in the injection molding material to be simulated on the basis of a physical model, wherein the at least one second parameter is communicated to the machine control and / or the at least one first parameter is communicated to the process simulation.

[0009] The above-mentioned prior art has one significant common disadvantage. Performing the simulation based on the CAD data of the component to be manufactured or on the basis of the CAD data of the mold cavity requires, firstly, that such data is known. Furthermore, such a simulation is associated with a high computational effort, since, particularly in the case of complex components or complex, for example, branched mold cavities, a considerable amount of computational effort is required to perform the simulation, e.g., the flow simulation. This computational effort is particularly time-consuming. In general, therefore, the simulation of the injection molding process based on the previously known CAD data of the component to be manufactured or on the basis of the previously known CAD data of the mold cavity is only used for the initial setup of a process.However, this also requires that the person who is supposed to carry out this process simulation must also have knowledge of the mentioned CAD data, since it is impossible to set up a meaningful simulation process without knowledge of this data.

[0010] This in turn results in the disadvantage that, due to the need for at least a realistic 3D model as a starting point, complicated and time-consuming, especially manual, data handling results.

[0011] Particularly in the case of more complex geometries or more complex simulation models, a high level of manual processing effort with regard to initial model optimization and the like is unavoidable.

[0012] The object of the invention is therefore to automate the creation of physical models from real process data in order to enable simulation without knowledge of the actual geometry of the component and / or the mold cavity. A further object is to significantly simplify the complexity of simulation-related calculations of primary forming processes, in particular injection molding processes, both qualitatively and / or quantitatively. Furthermore, the object of the invention is to enable the determination of machine setting data / process parameters without knowledge of the CAD data or the geometric three-dimensional shape of the molded part to be produced and / or the CAD data or the three-dimensional shape of the cavity in the mold. Furthermore, it is desirable to significantly reduce the manual effort required for simulation and data management.In particular, the manual meshing effort of a simulation geometry, as currently required in state-of-the-art injection molding simulations, is to be significantly reduced.

[0013] The above-mentioned objects are achieved according to the invention with a method having the features of claim 1. Advantageous embodiments are specified in the claims dependent on claim 1. The invention is based on the core idea of ​​utilizing findings from a learning process step to determine a geometric meta-model based on these findings, resulting in a substitute geometry in the form of a geometric meta-model that is greatly simplified compared to the real component or the real tool cavity. This geometric meta-model is created without knowledge of the real spatial shape or the CAD data of the real component and / or the tool cavity.Only on the basis of the at least one learning geometry variable recorded in the learning process step, at least one learning process variable recorded in the learning process step and the at least one learning design target variable recorded in the learning process step, a substitute geometry is determined from highly simplified geometries, namely from geometric rule structures, which makes it possible to simulate a behavior or a process behavior, in particular with regard to suitable features, in a sufficiently approximate manner as the real process or the real geometry would do in reality.

[0014] This makes it possible to create a geometric meta-model without knowledge of the actual geometry of the molded part to be produced or without knowledge of the actual geometry of the mold cavity. This geometric meta-model is significantly simplified compared to the real geometries or the simulation geometries in the learning process step, yet still enables a reliable, optimized process setup of the primary forming process, especially the injection molding process. The geometric meta-model is particularly suitable as a simulation geometry for conducting a process simulation of the primary forming process, especially the injection molding process.

[0015] For the understanding of the present invention, the terms listed below are defined as follows, if appropriate, in addition to or alternatively to a relevant technical term definition. If the present definition overlaps with or even contradicts a possibly existing technical term definition, the definition as it appears in this application shall prevail in case of doubt. Geometric rule:

[0016] According to the invention, a geometric rule structure is to be understood as a 2-dimensional or a 2.5-dimensional or a 3-dimensional non-free-form structure.

[0017] A 2-dimensional geometric rule structure is a 2-dimensional polytope or a plane projection of a 2.5-dimensional or a 3-dimensional geometric rule structure.

[0018] A 2.5-dimensional geometric rule structure is a 2-dimensional rule structure with at least partially constant die information in a third dimension.

[0019] A 3-dimensional geometric regular structure is understood to be a 3-dimensional non-free-form structure, whereby in particular, but not exclusively, the following basic geometric bodies are to be regarded as 3-dimensional regular structures:

[0020] 3-dimensional polytopes, e.g.

[0021] Cuboid

[0022] Dice

[0023] prism

[0024] pyramid

[0025] - 3- or multi-corner plate

[0026] - truncated pyramid and / or

[0027] 3-dimensional bodies with at least one curved boundary surface, e.g.

[0028] - cylinder,

[0029] Bullet

[0030] cone

[0031] Cylindrical disc with a height h and a diameter d, where h <d gilt

[0032] Partial sphere, especially hemisphere

[0033] Truncated cone. Virtual object:

[0034] According to the invention, a virtual entity is understood to be the result of a Boolean addition or a Boolean subtraction of at least two geometric rule entities. In extreme cases, a virtual entity can also be formed from only a single geometric rule entity.

[0035] Injection phase:

[0036] The period from the start of the melt filling process to complete volumetric mold filling, at which the mold filling process is complete except for the necessary material transport to compensate for shrinkage. The start of the melt filling process approximately coincides with the closing of the non-return valve.

[0037] Reprint phase:

[0038] The period from the switchover point until the end of pressurization to compensate for material shrinkage. While material is transported into the mold for this purpose, this shrinkage compensation is no longer considered part of the injection phase.

[0039] Molding process:

[0040] The period during which melt enters the cavity. Typically, the molding process covers a period from the beginning of the injection phase to the end of the holding pressure phase.

[0041] In detail, the method according to the invention for generating and / or adapting a geometric meta-model for a molded part to be produced in a primary forming process for use as simulation geometry comprises the following steps: a) in a learning process step: a.1) producing a good part of the molded part in a real good part production process and / or carrying out a simulation of the good part production process, a.2) recording at least one learning process variable and at least one learning geometry variable and at least one

[0042] Learning design target value of the good part manufacturing process and / or the simulation of the good part manufacturing process b) in a step for determining the geometric meta-model: b.1) definition of at least one process MUST criterion, which corresponds to the learning process value, and definition of at least one geometry MUST criterion, which corresponds to the learning geometry value b.2) configuration of a simulation environment at least comprising: b.2.1) a geometry-based simulation component containing at least one basic geometry, b.2.2) a process-based simulation component, b.2.3) a design-based simulation component of a

[0043] Experimental design that includes at least one simulation optimization objective to be achieved

[0044] Simulation, where the

[0045] Simulation optimization goal from the

[0046] Learning design target is formed or the

[0047] Learning design target is; b.3) Carrying out the simulation with the simulation environment configured in step b.2. to obtain a simulation result; b.4) Evaluation of the simulation result by comparing the simulation result with the specified simulation optimization goal; b.5) If the simulation optimization goal is not achieved in step b.4), b.5.1 ) Iterative adaptation of the base geometry to obtain an intermediate geometry; b.5.2) Modification of the geometry-based

[0048] Simulation component from step b.2.1) by replacing the base geometry with the intermediate geometry; b.6) Repeating steps b.3) to b.5) using the intermediate geometry until the specified simulation optimization goal is achieved with the intermediate geometry; b.7) When the specified simulation optimization goal is achieved, defining the intermediate geometry as the geometric meta-model; b.8) wherein a virtual entity is used for the base geometry and the intermediate geometry, which is at least one geometric rule entity or which is formed from two or more geometric rule entities.

[0049] In a particular embodiment, the virtual structure is formed or composed of 2-dimensional, 2.5-dimensional and / or 3-dimensional rule structures.

[0050] These represent desirable basic alternatives regarding rule formation types.

[0051] Advantageously, at least one of the following 3-dimensional rule structures is selected from the group:

[0052] - 3-dimensional polytopes, e.g.

[0053] Cuboid

[0054] Dice

[0055] prism

[0056] pyramid

[0057] - 3- or multi-corner plate

[0058] - truncated pyramid and / or

[0059] - 3-dimensional bodies with at least one curved boundary surface, e.g.

[0060] - Cylinder, sphere, cone, cylindrical disc with a height h and a diameter d, where h <d gilt

[0061] Partial sphere, especially hemisphere truncated cone is used.

[0062] These relatively simple 3-dimensional geometric structures, which are mathematically representable, enable particularly low computational effort in simulation calculations based on geometric metamodels formed from these 3-dimensional geometric structures. In contrast to, for example, freeform surfaces or freeform bodies, which can only be determined numerically, such as bodies containing spherical surfaces or oscillating surfaces, the geometric metamodel formed from such structures significantly simplifies simulation calculations.

[0063] Advantageously, a 2-dimensional rule structure is understood to be a 2-dimensional polytope or a planar projection of a 3-dimensional rule structure.

[0064] The use of only 2-dimensional control structures also makes it possible to reduce the simulation computation effort and thus the time required to calculate a simulation based on the geometric meta-model created from it.

[0065] Furthermore, a 2.5-dimensional control structure is to be understood as a 2-dimensional control structure together with at least partially constant dieken information concerning a third dimension.

[0066] Such a 2.5-dimensional control structure can advantageously help to reduce the computational effort and thus the time required for a simulation, especially compared to the use of 3-dimensional control structures.

[0067] It is particularly expedient that the virtual structure and / or at least one of the geometric control structures is designed to be parameterizable in the direction of at least one spatial axis (X-axis and / or Y-axis and / or Z-axis), and in step b.5.1) at least one of the size values ​​of the spatial axes (X-axis and / or Y-axis and / or Z-axis) is changed for the iterative adaptation.

[0068] With the above measure, it is possible to optimize the basic geometry or the intermediate geometry towards the optimized geometric meta-model by only having to vary the spatial shape and / or the size of at least one control structure and thus to iteratively adapt the basic geometry or the intermediate geometry to the desired target geometry only in this way or in conjunction with further measures, ie to obtain the geometric meta-model.

[0069] According to a further embodiment of the method according to the invention, outer edges and / or inner edges of the virtual structure or transition edges between two standard structures of the virtual structure are rounded or have a chamfer.

[0070] By using fillets or chamfers, it is possible in a particularly simple way, for example, to adapt the flow behavior of a material melt within the geometric meta-model to the findings from the learning process step.

[0071] Advantageously, the primary mold manufacturing process is a casting process, e.g., an injection molding process or a die casting process.

[0072] The method according to the invention is particularly suitable for the above-mentioned primary forming processes.

[0073] According to a further embodiment of the invention, the geometric meta-model with respect to the geometry-based simulation component satisfies at least one geometry MUST criterion within predefined geometry MUST criterion limits and / or the geometric meta-model behaves with respect to the geometry MUST criterion within the predefined geometry MUST criterion limits in the same way as the real geometry of the good part or the simulation geometry of the molded part to be produced, ie like the learning geometry variable.

[0074] The properties of the geometric meta-model outlined above enable a particularly accurate and realistic determination of the geometric meta-model in comparison to corresponding properties of the real geometry or the simulation geometry.

[0075] In a further embodiment of the method according to the invention, the geometric meta-model with respect to the geometry-based simulation component fulfills at least one geometry CAN criterion within predefined geometry CAN criterion limits and / or the geometric meta-model behaves with respect to the geometry CAN criterion within the predefined geometry CAN criterion limits in the same way as the real geometry of the good part or the simulation geometry of the molded part to be produced, ie like the learning geometry variable.

[0076] With the properties of the geometric meta-model outlined above, a further, particularly precise determination of the behavior of the geometric meta-model in comparison to that of the real geometry or the simulation geometry is possible.

[0077] It has proven particularly useful that the geometry MUST criterion is a total volume to be filled (Vfiii.tot).

[0078] According to a particular embodiment, the geometry CAN criterion is a relation of dimensions of the rule structures of the geometric meta-model, e.g. L / D (length to diameter) for a cylinder.

[0079] By specifying such a geometry-CAN criterion, melt flow velocities in a geometric meta-model underlying a simulation can, for example, be approximated at least in some areas to actual flow velocities during the good part process or to flow velocities resulting from the simulation of the good part process. Such approximations, at least in some areas, can, in particular, accurately simulate or adapt average flow front velocities of the melt in the meta-model.

[0080] A further embodiment of the invention is characterized in that the geometric meta-model, with respect to the process-based simulation component, fulfills at least one process MUST criterion within predetermined process MUST criterion limits and / or that a process simulated with the meta-model behaves with respect to the process MUST criterion within the predetermined process MUST criterion limits like the real good part manufacturing process or like the simulation of the good part manufacturing process, ie like the learning process variable.

[0081] With the properties of the geometric meta-model outlined above, a particularly accurate and realistic determination of the geometric meta-model with regard to its process variables in comparison to corresponding process variables of the good part manufacturing process or the simulation of the good part manufacturing process is possible.

[0082] A further embodiment of the invention is additionally characterized in that the geometric meta-model, with respect to the process-based simulation component, fulfills at least one process CAN criterion within predetermined process CAN criterion limits and / or that a process simulated with the geometric meta-model behaves with respect to the process CAN criterion within the predetermined process CAN criterion limits like the real good part manufacturing process or like the simulation of the good part manufacturing process, ie like the learning process variable.

[0083] If, in addition to the process MUST criterion or the process MUST criteria, at least one further process CAN criterion is met, or if the geometric meta-model in a simulation process behaves in the same way as the actual good part manufacturing process or the simulation of the good part manufacturing process with regard to the process CAN criterion, the quality and meaningfulness of the geometric meta-model can be further improved as a simplified substitute geometry. In a specific embodiment, it has proven useful for the process MUST criterion to be at least one or more selected from the group:

[0084] - plasticizing screw stroke curve of the injection phase (Sinj) and / or values ​​calculated therefrom;

[0085] - injection pressure curve (ie a course of injection pressure values ​​pinj over a time, e.g. injection time, or a distance, e.g. the screw stroke) or singular injection pressure values ​​(pinj) and / or values ​​calculated therefrom;

[0086] - Physical material data, e.g. in the form of material models such as pvT and cross-WLF models;

[0087] - Temperature value (Tinj), which represents a temperature of the material melt, especially near the nozzle or is / are a hot runner temperature.

[0088] The above-mentioned process MIL SS criteria, of which at least one must be fulfilled, create a basis for setting up and / or optimizing a simulated process on the basis of the geometric meta-model in terms of essential process data in accordance with the real good part process or with the simulation of a real good part process, in particular within specified process MUST criteria limit values.

[0089] In further embodiments of the invention, the process CAN criterion is one or more selected from the group:

[0090] - Nozzle pressure curve (pnoz) and / or values ​​calculated from it;

[0091] - Volume flow curve of the injection phase (iZinj) and / or values ​​calculated from it;

[0092] - cavity pressure curve (pcav) and / or values ​​calculated from it;

[0093] - Closing force curve (Fci P )and / or values ​​calculated therefrom;

[0094] - Current curve (lDrv) and / or values ​​calculated from it.

[0095] Using such process CAN criteria, it is possible to meaningfully select a geometric meta-model within a simulation with regard to realistic process properties of the good part manufacturing process or the process properties of the simulation of the good part manufacturing process. The latter current curve, in particular, can provide easily measurable information about pressure conditions within the screw antechamber and / or within the mold cavity. The current curve is, for example, the temporal progression of the electrical current consumption of a drive motor for a hydraulic unit that feeds the plasticizing screw within a plastics injection molding machine. Similar statements are also possible based on knowledge of the temporal progression of the clamping force required to keep a mold closed.

[0096] Alternatively or additionally, it may be expedient for the geometric meta-model to fulfill at least one design MUST criterion within predefined design MUST criterion limits with respect to the design-based simulation component and / or for a process simulated with the meta-model to behave like the real good part manufacturing process or like the simulation of the good part manufacturing process, ie like the learning design target variable, with respect to the design MUST criterion within the predefined design MUST criterion limits.

[0097] A design must-have criterion is a process criterion toward which the simulation process is optimized using the base geometry / intermediate geometry. It is therefore a criterion of a test plan design that should be within the narrowest possible design must-have criterion limits.

[0098] An example of the design MUST criterion is the mold filling time (tun), which corresponds, for example, to the duration of the injection phase, under a boundary condition of the total volume to be filled (Vfiii.tot).

[0099] The above design MUST criterion specifies an easily measurable and achievable optimization target under the boundary condition of the total volume to be filled (Vfiii.tot), which is component-specific and / or cavity-specific. In addition to a design MUST criterion, the geometric meta-model can expediently fulfill at least one design CAN criterion with respect to the design-based simulation component within specified design CAN criterion limits, and / or the geometric meta-model can be characterized in that a process simulated with the meta-model behaves like the real good part manufacturing process or like the simulation of the good part manufacturing process, i.e., like the learning design target variable, with respect to the design CAN criterion within the specified design CAN limit values.

[0100] As a design CAN criterion, at least one or more has been selected from the group:

[0101] solidification time (tsoiid);

[0102] Volume flow curve and values ​​derived from it, e.g. - maximum volume flow (7 max), average volume flow (Y avg) proven.

[0103] With regard to the iterative optimization to achieve the geometric meta-model, it has proven useful that the iterative adaptation of the base geometry or the intermediate geometry is carried out by changing a geometric extension of one or more of the geometric rule structures in one or more of the spatial axes (x-axis; y-axis; z-axis).

[0104] With such a parameterization of the spatial extents of at least one, preferably several of the rule structures from which the basic geometry, the intermediate geometry or the geometric meta-model is formed, preferably with the aid of Boolean addition and / or Boolean subtraction, adaptations of the basic geometry and / or the intermediate geometry to achieve the geometric meta-model are possible in a particularly simple manner.

[0105] Alternatively or cumulatively to the above-mentioned parameterization of individual rule structures, it may be particularly useful to achieve the optimization goal for the iterative adaptation of the base geometry or the intermediate geometry to be carried out by Boolean addition of another rule structure to the existing base geometry or to the existing intermediate geometry.

[0106] This measure makes it possible to achieve an improved approximation of the geometric meta-model to the corresponding properties of the good part in a particularly simple manner and, above all, without unduly increasing the complexity of the geometric meta-model. Characteristic process variables of the good part manufacturing process or the simulation of the good part manufacturing process can also be easily adapted by adding rule structures to an existing base geometry or an existing intermediate geometry.

[0107] Alternatively or cumulatively to the above-mentioned parameterization of individual rule structures or the above-mentioned Boolean addition of rule structures, it may be particularly useful to achieve the optimization goal for the iterative adaptation of the base geometry or the intermediate geometry to be carried out by Boolean subtraction of another rule structure from the existing base geometry or from the existing intermediate geometry.

[0108] This measure makes it possible to achieve an improved approximation of the geometric meta-model to the corresponding properties of the good part in a particularly simple manner and, above all, without unduly increasing the complexity of the geometric meta-model. Characteristic process variables of the good part manufacturing process or the simulation of the good part manufacturing process can also be easily adapted by removing standard structures from an existing base geometry or an existing intermediate geometry.

[0109] It may also be appropriate for the iterative adaptation of the base geometry or the intermediate geometry to be performed by replacing a rule structure of the existing base geometry or the existing intermediate geometry with another rule structure. This represents another cumulative or alternative possibility for the meaningful adaptation or generation of the geometric meta-model.

[0110] Advantageously, it is also an aim of the method according to the invention that a characteristic tool system response of the geometric meta-model generates the same tool system response within predetermined tool system response limits as the real good part manufacturing process or as the simulation of the good part manufacturing process.

[0111] This approach assumes that in a simulation using the geometric meta-model, a characteristic tool-system response in response to predetermined process sequences, such as an injection pressure curve, is a particularly suitable characteristic, which leads to a calculated process simulation based on the geometric meta-model corresponding to a high degree to the good part manufacturing process or the simulation of the good part manufacturing process despite significantly lower and therefore less time-consuming calculation effort, and thus a suitable replacement geometry for further simulation calculations is found in the form of the resulting geometric meta-model.

[0112] Furthermore, it may be advantageous that a total volume of the geometric meta-model corresponds to a total volume of the good part, in particular including ancillary structures or the volume used in the simulation of the good part process.

[0113] Such an approach simplifies the determination of the total filling volume of a cavity without having to separately know the total volume of the actual molded component or the ancillary structures.

[0114] It is particularly expedient that, from a plurality of intermediate geometries which satisfy both the geometry MUST criterion and the process MUST criterion as well as the design MUST criterion, that intermediate geometry is selected as the meta-model which, with respect to at least one process CAN criterion, comes closest to the learning process variable and / or with respect to at least one geometry CAN criterion to the learning geometry variable and / or with respect to at least one design CAN criterion.

[0115] This measure can ensure that a simulation based on the determined geometric meta-model achieves a particularly high level of accuracy.

[0116] Furthermore, it may be advantageous that the total volume to be filled (Vfiii.tot) is determined taking into account a melt compression and / or taking into account an elasticity of a plasticizing screw and / or taking into account a closing behavior and / or a leakage behavior of a non-return valve.

[0117] Such a measure also increases the accuracy of the simulation calculation based on the geometric meta-model thus determined as a substitute geometry, since material-specific idealized assumptions as well as machine-side idealized assumptions are replaced by corresponding real material behavior and / or real machine behavior. Regarding the implementation of these measures, reference is made to the technical teachings of European patents EP3359365B1, EP3870422B1, and EP3055116B1, merely as examples.

[0118] It is particularly easy to carry out if the tool system response is a pressure curve p(s) which results from an identification speed signal v(s) of the plasticising screw with which the tool is filled.

[0119] This allows the measurement effort, both in terms of equipment and computational effort, i.e. the computational processing of measurement data, to be significantly reduced.

[0120] As an alternative to the mold system response defined above, e.g., a pressure curve, the mold system response can also be a velocity curve of the plasticizing screw v(s), which results from an identification pressure signal p(s) with which the mold is filled. This can also significantly reduce the measurement effort, both in terms of equipment and computational effort.

[0121] It is particularly useful that the test plan design includes, in addition to the simulation optimization objective, at least information on an iteration step size for the iterative adaptation in step b.5.1 ) and / or upper and lower limit values ​​for the iterative adaptation in step b.5.1 ).

[0122] By appropriately adjusting the iteration step size within the experimental design, the computational and / or iterative effort required to obtain a geometric meta-model within the required limits can be reduced and / or even optimized. This is achieved, in particular, by reducing the number of required iteration steps.

[0123] It is advisable that a mesh is assigned to the base geometry, the intermediate geometry and the geometric meta-model within the framework of a meshing and / or that an inlet is assigned to the base geometry, the intermediate geometry and the geometric meta-model.

[0124] To make both the base geometry and intermediate geometries, as well as the geometric meta-model, available for simulation calculations, it is advisable to perform meshing and define an inlet that will simulate an injection point. These are common preparatory measures for making a geometric structure accessible to simulation calculations, i.e., creating a simulation-capable geometry.

[0125] To minimize the computational effort required to obtain a simulation-capable base geometry, a simulation-capable intermediate geometry, or a simulation-capable geometric meta-model, the inlet remains unchanged, at least with regard to its geometry, during the iterative adaptation from the base geometry through the intermediate geometries to the geometric meta-model. The shape and / or dimensions of the inlet can be appropriately adapted to the control structure downstream in the melt flow direction.

[0126] The invention is explained in more detail below using the drawings as examples. They show:

[0127] Fig. 1 : schematically on the left side of the figure a view of a

[0128] Real component, namely a holder for wrenches, which can be replaced by a highly simplified replacement geometry in the form of a meta-model (right side of the figure) which was obtained using the method according to the invention;

[0129] Figure 2: schematically a flow diagram of the inventive

[0130] procedure;

[0131] Figure 3: a detailed representation of the process step “Configuration of the

[0132] simulation environment” from Figure 2;

[0133] Figure 4 shows a schematic flow diagram for the parameter definition, for example of radius and length of a three-dimensional control structure, for example a cylinder;

[0134] Figure 5: schematically a flow diagram of a sub-process for

[0135] Determination of the cylinder radius from Figure 4;

[0136] Figure 6: schematically a flow diagram of a sub-process for

[0137] Determination of the cylinder length from Figure 4;

[0138] Figure 7: schematically a flow chart for obtaining a

[0139] Design of Experiments (DoE).

[0140] Figure 1 shows a comparison of a good part 1 with the real geometry of a component produced in a primary forming process. Knowing the real geometry of the good part 1, the prior art performs very time-consuming and complex data processing, such as meshing, to create a model that is accessible for further process simulation. Creating a model based on the real geometry in this way is complex and computationally intensive, and the invention aims to avoid this, or at least greatly simplify it. For this purpose, the invention specifies how a substitute geometry, such as a geometric meta-model 2, can be determined. This substitute geometry, such as a geometric meta-model 2, can be determined. This substitute geometry is geometrically and mathematically much simpler and less complex than the good part 1, yet nevertheless offers a good simulation geometry for an almost realistic tool response in process simulations based on the geometric meta-model 2 determined according to the invention.Due to the significantly simplified design of the geometric meta-model 2, simulations to be carried out or calculated with it are significantly less time-consuming and cause considerably less computational effort.

[0141] The present invention therefore enables the performance of flow simulations for a primary forming process, in particular for an injection molding process, without knowing the real three-dimensional component geometry or cavity geometry usually required for such a simulation. On the basis of process and setting data of a machine that, for example, carries out a good part manufacturing process, a geometric substitute model, i.e. a geometric meta-model, is derived which can be used in a flow simulation instead of the more complex original geometry. If such a geometric substitute model fulfills certain minimum process requirements described in more detail below, this geometric substitute model is referred to in the context of this invention as geometric meta-model 2.

[0142] Calculation results based on such a geometric meta-model 2 are comparable, particularly with regard to mold filling times, injection pressures and volume flows, to those that would have been calculated using the original geometry. The core idea of ​​the invention is therefore to design a geometric meta-model 2 based on, among other things, process data which, despite considerably reduced geometric complexity compared to the good part 1 or the real geometry, i.e. despite greatly reduced computing and data handling effort, can provide reliable information within the framework of a simulation with which a manufacturing process of the good part or the real geometry can be simulated with sufficient approximation. These basic ideas are fundamentally applicable to all primary forming processes, but in particular to an injection molding process and / or a die casting process, such as an aluminum die casting process.The method according to the invention for obtaining such a replacement geometry in the form of a geometric meta-model 2 is explained with regard to the process flow in Figure 2. The method according to the invention is essentially divided into two process steps, namely a learning process step 3 and a step 4 for determining the geometric meta-model.

[0143] Learning process step 3 can fundamentally be performed alternatively or cumulatively in two different ways. Firstly, it is possible to perform a good part manufacturing process on a machine 5 to produce a good part and to determine process parameters or process signals 6 from this good part manufacturing process.

[0144] Alternatively or additionally, a simulation 7 of the good part manufacturing process can also be performed based on the real geometry. Such a simulated good part manufacturing process results in so-called simulated process signals 8 or simulated process parameters, which are determined by simulating the good part manufacturing process. At the end of the learning process step 3, real good part manufacturing process data or simulated good part manufacturing process data are thus available, on the basis of which, as will be described below, the geometrically highly simplified meta-model 2 according to the invention is determined.

[0145] In learning process step 3, therefore, at least one learning process variable and at least one learning geometry variable as well as at least one learning design target variable of the good part manufacturing process and / or the simulation thereof are recorded or determined.

[0146] These, particularly high-resolution, process and setting data are recorded and stored via a data acquisition system and made available for further processing.

[0147] The data supplied, in particular the scope of the data supplied, includes, for example, measured variables that are recorded directly on the machine during the good part manufacturing process or that are calculated in the good part simulation. Alternatively or cumulatively, modeling can run on the machine, in the cloud, or on a local computer alongside the data acquisition. In addition to this data, data acquired using so-called soft sensors can be used, for example, if the soft sensors are of sufficient quality and process-specific requirements regarding resolution, accuracy, etc. are met.

[0148] At the beginning of step 4 for determining the geometric meta-model 2, i.e., in the second essential phase of the method according to the invention, at least one process MUST criterion corresponding to the learning process variable and at least one geometry MUST criterion corresponding to the learning geometry variable are defined. The learning process variable and the learning geometry variable are known from learning process step 3. These two MUST criteria must be met by the newly created replacement geometry, namely ultimately by the geometric meta-model 2, within reasonable MUST criterion limits, so that a simulation based on the geometric meta-model can be meaningful for a real or simulated process of the good part or the real geometry.

[0149] Geometry must-have criteria / geometry can-have criteria and boundary conditions (examples)

[0150] At the beginning of step 4, data preprocessing (step S105) also takes place to determine the geometric meta-model 2. Data from the data preprocessing (step S105) is either fed directly to a configuration step (S106), or virtual sensor signals can be determined and / or calculated from the data preprocessing in a calculation step S107. The virtual sensor signals thus determined are assigned to a corresponding virtual sensor, which need not be physically present but is derived from measured values. This step is referred to as step S108 in Figure 2.

[0151] In this simulation configuration step, settings for the geometry to be simulated, the process to be simulated, and the scope of a simulated test plan are specified. Details will be explained below in the discussion of Figure 3. The specification of the above settings is also referred to below as a geometry-based step, a process-based step, and a design-based step.

[0152] In the geometry-based work step, the geometry to be used in the simulation is defined and, if necessary, parameterized, meshed, and provided with an inlet I that matches its dimensions, shape, and position on the geometry. The geometry obtained in the geometry-based work step is a substitute geometry for the good part 1 and represents the beginning of an iteration loop; therefore, this substitute geometry is referred to as the base geometry. The process-based settings depend on the process to be simulated. For example, the process-based settings required for an injection molding simulation differ from those required for a die casting simulation.

[0153] Process MUST criteria, process CAN criteria and boundary conditions (examples)

[0154] This data is used, for example, to configure the simulation environment.

[0155] The final configuration step of the simulation environment involves design-based configuration. This configuration of a design contains variables that specify the optimization goal, such as the learning design target variable determined in learning process step 3, as well as the variables to be varied to achieve the optimization goal. These variables are design-specific variables, and their changes support an approximation of the optimization goal. Furthermore, boundary conditions to be considered can be defined to filter the simulation results or define the limits of an optimization problem.

[0156] Following the configuration of the simulation environment, as performed in step S106, the simulation of the test plan is carried out (step S109). Results from the simulation of the test plan, i.e. results from step S109, are assessed in an evaluation step S110 with regard to the achievement of the desired optimization goal, i.e. with regard to the achievement of the desired learning design target. Whether the desired learning design target has been achieved, or at least achieved within the framework of technically reasonable tolerances, is decided in a decision step S111. If the first test plan did not generate a satisfactory result, the geometry-based configuration step is repeated with iterative adaptation of the base geometry (step S112), resulting in an intermediate geometry. The previous or a new test plan is simulated using the intermediate geometry, i.e. step S109 is repeated.

[0157] Design MUST criteria, design CAN criteria and boundary conditions (examples)

[0158] The scope of the geometric adaptation or geometric iteration is defined by a separate algorithm and can add new geometric rule bodies to the base geometry or the intermediate geometry as well as subtract them from an existing base geometry or an existing intermediate geometry. Furthermore, it is possible to adapt the degrees of freedom present in the geometry while the shape of the base geometry or the intermediate geometry remains fundamentally unchanged. As an example, the shape of a cuboid is generally retained, but the cuboid can be varied in its length, width, or height. The term “parameterization” is used below for such an approach. The geometry used in the simulation, which depends on the simulation environment used, can be parameterized in all three spatial dimensions if necessary in order to achieve the desired optimization goal, i.e.The learning design target variable is to be achieved. After the optimization goal has been achieved, the final parameters are determined in addition to the simulation calculation results, with which the geometry used in the simulation can be uniquely described. Thus, if an iteratively obtained geometry achieves certain tolerances, it can be considered a suitable geometry for the simulation and is thus referred to within the scope of the invention as a geometric meta-model, which is the goal of the inventive method (end: S113).

[0159] In the following, the configuration step S106 for configuring the simulation geometry is explained in more detail with reference to Figure 3.

[0160] Within the framework of the geometry-based settings (S320), a base geometry is first generated or selected (S321). According to the invention, this base geometry is at least one geometrically controlled structure, i.e., a basic body that is geometrically and mathematically easily graspable and easily describable, which in particular does not contain any free-form surfaces, such as, for example, oscillating surfaces or contour surfaces.

[0161] Freeform surfaces are, for example, surfaces that can only be described numerically and / or consist of point clouds, since they cannot be described by a simple, formulaic relationship, such as a sphere or a cuboid or a cube or the like.

[0162] Such freeform surfaces are, for example, scanned strain surfaces or osculation surfaces on CAD models and / or real geometries.

[0163] Subsequently, dependencies on geometry parameters are configured (S322). Such dependencies can be, for example, but not exclusively, a length-to-diameter (L-D) ratio of a cylinder, a length-to-width ratio of a cuboid, a width-to-height ratio of a cuboid, or a length-to-height ratio of a cuboid. Furthermore, the geometric control bodies can be parameterized, for example, based on individual or multiple degrees of freedom of the geometric control bodies, for example, lengths, widths, heights, angles, diameters, radii, or the like.

[0164] In a final step S323, the geometry thus obtained is meshed and provided with the inlet I to be usable for a simulation calculation. Thus, a base geometry, which is the first substitute geometry, or an intermediate geometry, which is a substitute geometry with at least one iteration stage, for example, steps S109, S110, S111, and S112, is meshed and provided with the inlet I.

[0165] Within the scope of the process-based settings (S330), essential process settings of the primary forming process, in particular the injection molding process, are configured in a step S331.

[0166] As part of the design-based settings (S340), variables of the experimental design are first configured in step S341. Subsequently, boundary conditions that must be met in all cases with regard to the experimental design are configured (step S342). Furthermore, the optimization goal toward which geometric meta-model 2 is to be optimized is configured in step S343.

[0167] After that, step S106 of configuring the simulation environment is completed.

[0168] The following explains the basic process that takes place in step S112, namely the geometry-based configuration or geometry-based adaptation of the substitute geometry (base geometry or intermediate geometry), based on Figure 4. This is done using the example of a base geometry / intermediate geometry in the form of a geometric rule structure of a cylinder with length I and radius r.

[0169] First, in step S400, it is decided whether a real radius n-eai is known. If this is the case, the known real radius rreai is used as the cylinder radius for the simulation r C yi,sim is set (S401 ). If the radius rreai is not known, the cylinder radius for the simulation is r C yi,sim to a cylinder radius r cyi set (step S402).

[0170] The procedure for the cylinder's length I is analogous to the cylinder's radius r. First, in step S403, a query is made as to whether a real length Ireai is known. If this is the case, the real length Ireai is set as the cylinder length for the simulation Icyi.sim (S404). If the real length Ireai is unknown, the cylinder length for the simulation Icyi.sim is set to a cylinder length l cyi set (S405).

[0171] Starting values, step sizes of an iteration as well as limit values ​​for the adaptations explained above, namely the radius adaptation and the length adaptation using the example of a cylinder, can basically be freely selected.

[0172] With the aid of Figure 5, step S402, namely setting / calculating the cylinder radius rcyi.sim for the simulation, is now described when the real radius Treal is not known.

[0173] After starting this process (S500), a query is first made as to whether the existing run is a first run (step S501).

[0174] If it is the first pass, for example, in a step S502 the cylinder radius r cyiset to 2 mm. Furthermore, in step S503, a bit limhigh, which indicates that the upper limit has been reached, and a corresponding bit limiow, which indicates that the lower limit has been reached, are set to 0. The first pass is thus completed. One can return to the start of this process S500.

[0175] In a further run, in step S506, it is first checked whether the upper limit of the cylinder radius r cyi is reached, ie whether lirrihigh= true is set. If this is the case, a query is made in step S507 as to whether the lower limit of the cylinder radius r cyi is reached, ie whether limiow= true is set. If this is also the case, the radius r cyiare retained and the subroutine is terminated. If limiow is not set to true, a check is first made in step S508 to determine whether a lower limit of the adaptation rmin has been reached. If this is not the case, the radius rcyi is reduced in step S508' by the increment hcyi.r of the cylinder radius adaptation.

[0176] If the lower limit of the adaptation rmin is reached, the bit limiow, which indicates that the lower limit has been reached, is set to 1 (=true) in a step S511. With this new value of the bit limiow =1, which indicates that the lower limit for r C yi, the query of step S507 is returned again.

[0177] If the upper limit of the cylinder radius r cyi is not reached, ie if limhigh is not set to true, a step S509 first queries whether an upper limit of the adaptation r max is reached. If this is not the case, the radius r C yi is increased in a step S510 by the step size hcyi.r of the cylinder radius adjustment.

[0178] If the upper limit of the adaptation r ma x is reached, the bit limhigh, which indicates that the upper limit has been reached, is set to 1 (=true) in a step S511. With this new value of the bit limhigh =1, which indicates that the upper limit for r C yi, the query of step S506 is returned again.

[0179] An analogous procedure is used to determine the length of the cylinder Icyi.sim when this length Icyi.sim is unknown. This will be explained below using Figure 6.

[0180] After starting this process (S600), a query is first made as to whether the existing run is a first run (step S601).

[0181] If it is the first pass, for example, in a step S602 the cylinder length l C yi is set to 50 mm. Furthermore, in step S603, a bit limhigh, which indicates that the upper limit has been reached, and a corresponding bit limiow, which indicates that the lower limit has been reached, are set to 0. The first pass is thus completed. One can return to the start of this process (S600).

[0182] In a further run, a query is first made in step S606 as to whether the upper limit of the cylinder length Icyi has been reached, i.e. whether limhigh = true. If this is the case, a query is made in step S607 as to whether the lower limit of the cylinder length Icyi has been reached, i.e. whether limiow = true. If this is also the case, the cylinder length Icyi can be retained and the subroutine is terminated. If limiow is not set to true, a query is first made in step S608 as to whether a lower limit of the adaptation Imin has been reached. If this is not the case, the cylinder length Icyi is reduced in step S608' by the increment hcyi.i of the cylinder length adaptation.

[0183] If the lower limit of the adaptation Imin is reached, the bit limiow, which indicates that the lower limit has been reached, is set to 1 (=true) in step S611. With this new value of the bit limiow = 1, which indicates that the lower limit for Icyi has been reached, the system returns to the query of step S607.

[0184] If the upper limit of the cylinder length Icyi is not reached, i.e., if limhigh is not set to true, a check is first made in step S609 to determine whether an upper limit of the adaptation Imax has been reached. If this is not the case, the cylinder length Icyi is increased by the cylinder length adaptation increment hcyi.i in step S610.

[0185] If the upper limit of the adaptation Imax is reached, the bit limhigh, which indicates that the upper limit has been reached, is set to 1 (=true) in step S611. With this new value of the bit limhigh = 1, which indicates that the upper limit for Icyi has been reached, the program returns to the query of step S606.

[0186] The following, using Figure 7, explains how a design of experiments (DoE) is carried out within the scope of the invention. Unless explicitly stated otherwise, the order of description of the subsequent process steps is not to be understood as restricting the timing of the process steps. Unless explicitly stated otherwise, the individual steps can take place or be performed in a different order or simultaneously.

[0187] In a material data supply step S701, for example, physical data on the material used, in particular the material melt, a process temperature Tprocess and / or a tool temperature Ttool are provided.

[0188] In a supply step S702 for high-resolution data, for example, high-resolution individual values ​​and / or high-resolution curves of an injection pressure pinj, a filling time tfiii, a total filling volume Vnii, tot and a filling volume flow V inj are provided.

[0189] A process configuration is determined in a step S703 from the data of step S701 and step S702.

[0190] Furthermore, in a step S704, a geometry data feed is carried out, wherein geometric data relating to the basic geometry or the intermediate geometry, geometric limit values ​​and geometric boundary conditions are provided.

[0191] The geometry data input is supplemented by a mesh configuration step S705 to prepare for the data processing required for performing a simulation. Parallel to this, before or after this, an injection point configuration (inlet definition) is performed in a step S706.

[0192] A test plan is created in step S707 from the process configuration obtained in step S703, as well as from the geometry data (S704), which includes a mesh configuration (S705) and an injection point configuration (S706). This then results in the desired test plan design (DoE) (step S708).

[0193] Information on a possible upper limit on the number and / or size of the control structures used:

[0194] Such an upper limit on the number of control entities used is essentially component-dependent and cannot be specified or determined across the board. The construction of the geometric meta-model 2 via the initial or intermediate stages of the base geometry and the intermediate geometries takes place by parameterizing existing control entities or by Boolean addition to the virtual entity or Boolean subtraction of control entities from the virtual entity until the desired optimization goal, i.e., the learning design target, is achieved within technically reasonable tolerances.

[0195] The size of individual rule structures can also vary, both in the base geometry and in the intermediate geometries as well as in the finished geometric meta-model 2. For example, a specific component to be replicated can be created by using a finite number of rule structures, each with a volume of 0.1 cm 3 up to 1 cm 3or even larger volumes. The volumetric sizes of the individual regular structures within a base geometry or an intermediate geometry or the finished geometric meta-model 2 can be the same or different from each other, even for qualitatively similar regular structures such as cuboids, spheres, cones, or the like.

[0196] A scaling and a specific use of qualitatively different control structures of different basic forms is specified by the application case itself and the representable state variables of the geometric meta-model 2.

[0197] A possible upper limit for the number of control entities to be used can be used, for example, by setting an upper limit for the simulation time required to simulate such a geometric meta-model. It is, of course, desirable to accomplish the additive construction of a geometric meta-model with the simplest possible form and the minimum necessary number of control entities, so that the specified optimization goal, i.e., the learning design target variable, for example, the filling time, is achieved before the required simulation time corresponds to that of the original geometry. The aim of the invention is to reduce the simulation time and thus the computational effort compared to that of the original geometry.

[0198] Further embodiments of the invention:

[0199] Of course, it is possible that the process of geometric adaptation can be shortened and / or optimized through the use of artificial intelligence or expert knowledge.

[0200] Information on how to conduct the simulations:

[0201] The simulations to be performed within the scope of the invention on the basis of a base geometry, an intermediate geometry, or on the basis of the geometric meta-model 2 can be carried out, for example, within commercially available simulation applications. However, it should be emphasized that the inventive method for generating the geometric meta-model 2 can be applied independently of the simulation application used. One advantage of using a commercial simulation application is that important steps for a filling simulation, such as generating a mesh, defining the inlet I, and the numerical calculation methods with which the necessary differential equation systems can be solved, are already available and have been tested.In addition, commercial simulation applications typically include a material database to incorporate material-specific processes or data, such as viscosity properties, into the simulation. Regarding the topic of "interconnecting" the base geometry, the intermediate geometries, and the geometric meta-model, see 2:

[0202] The term "meshing" in the context of the invention means that a corresponding mesh is placed on the respective geometry in preparation for a simulation in the usual way, on the basis of which the simulation calculations are then carried out. Of course, an adapted mesh will also have to be determined for each geometry adaptation, since this represents the respectively valid geometry, i.e. the base geometry, one or more of the intermediate geometries or the finished geometric meta-model 2. However, due to the geometry of the geometric meta-model 2, which is greatly reduced in complexity according to the invention, the meshing effort as well as the meshing effort of all geometric intermediate stages (base geometry and intermediate geometry) is considerably reduced, which significantly simplifies the effort required to prepare a simulation and saves computing time.

[0203] On the topic of “setting an inlet” on the base geometry, the intermediate geometries and the finished geometric meta-model 2:

[0204] Within the scope of the invention, an injection point (inlet I) is also expediently defined on the respective existing geometry, i.e., the base geometry, the intermediate geometries, and the finished geometric meta-model 2, with regard to its size, shape, and position on the model. Of course, it may be necessary to reduce the inlet I, which was initially assumed to be of a certain size for a base geometry, if, for example, an end face of a standard structure, such as a cylinder, becomes smaller during adaptation than the area of ​​the originally applied inlet, or to enlarge it if necessary. In general, it is, of course, imperative to adapt the inlet I appropriately. List of reference symbols

[0205] 1 good part, real geometry

[0206] 2 geometric meta-model

[0207] 3 learning process step

[0208] 4 Step to determine the geometric meta-model

[0209] 5 Machine

[0210] 6 process signals

[0211] 7 Simulation

[0212] 8 simulated process signals

[0213] 10 virtual entities

[0214] 11 virtual rule structure

[0215] S105 to S113 steps

[0216] S320 to S323 steps

[0217] S330 to S331 steps

[0218] S340 to S343 steps

[0219] S400 to S405 steps

[0220] S500 to S512 steps

[0221] S600 to S612 steps

[0222] S701 to S708 steps

[0223] I Inlet

Claims

Claims 1. Method for generating and / or adapting a geometric meta-model (2) for a molded part to be produced in a primary forming process for use as simulation geometry, on the basis of process data, comprising the steps: a) in a learning process step (3): a.1) producing a good part (1) in a real good part production process and / or carrying out a simulation of the good part production process, a.2) recording at least one learning process variable and at least one learning geometry variable and at least one learning design target variable of the good part production process and / or the simulation of the good part production process b) in a step (4) for determining the geometric meta-model (2): b.1) defining at least one process MUST criterion which corresponds to the learning process variable, and defining at least one geometry MUST criterion which corresponds to the learning geometry variable b.2) Configuration of a simulation environment at least comprising: b.2.1) a geometry-based simulation component containing at least one basic geometry, b.2.2) a process-based simulation component, b.2.3) a design-based simulation component of a. Experimental design that includes at least one simulation optimization objective to be achieved Simulation, where the Simulation optimization goal from the Learning design target is formed or the Learning design target is; b.3) Carrying out the simulation with the model configured in step b.

2. Simulation environment for obtaining a simulation result; b.4) Evaluation of the simulation result by comparing the simulation result with the specified simulation optimization goal; b.5) If the simulation optimization goal is not achieved in step b.4), b.5.1 ) Iterative adaptation of the base geometry to obtain an intermediate geometry; b.5.2) Modification of the geometry-based Simulation component from step b.2.1) by replacing the base geometry with the intermediate geometry; b.6) Repeating steps b.3) to b.5) using the intermediate geometry until the specified simulation optimization goal is achieved with the intermediate geometry; b.7) When the specified simulation optimization goal is achieved, defining the intermediate geometry as the geometric meta-model (2); b.8) wherein a virtual structure (10) is used for the base geometry and the intermediate geometry, which is at least one geometric rule structure (11) or which is formed from two or more geometric rule structures (11).

2. Method according to claim 1, characterized in that the virtual structure (10) is formed or composed of 2-dimensional, 2.5-dimensional and / or 3-dimensional control structures (11).

3. Method according to claim 1 or 2, characterized in that as the 3-dimensional control structure (11) at least one is selected from the group: - 3-dimensional polytopes, e.g. Cuboid Dice prism pyramid - 3- or multi-corner plate - truncated pyramid and / or - 3-dimensional bodies with at least one curved boundary surface, e.g. - cylinder, Sphere Cone Cylindrical disc with a height h and a diameter d, where h <d gilt Partial sphere, especially hemisphere truncated cone is / are used.

4. Method according to claim 2, characterized in that a 2-dimensional polytope or a planar projection of a 3-dimensional rule structure is used as the 2-dimensional rule structure (11).

5. The method according to claim 4, characterized in that a 2-dimensional control structure (11) is used as the 2.5-dimensional control structure (11) together with at least sectionally constant thickness information relating to a third dimension.

6. Method according to one of the preceding claims, characterized in that the virtual structure (10) and / or at least one of the control structures (11) is designed to be parameterizable in the direction of at least one spatial axis (X-axis and / or Y-axis and / or Z-axis), and in step b.5.1) at least one of the size values ​​of the spatial axes (X-axis and / or Y-axis and / or Z-axis) is changed for the iterative adaptation.

7. Method according to one of the preceding claims, characterized in that outer edges and / or inner edges of the virtual structure (10) or transition edges between two standard structures (11) of the virtual structure (10) are rounded or have a chamfer.

8. Method according to one of the preceding claims, characterized in that the primary mold manufacturing process is a casting process, e.g., an injection molding process or a die-casting process.

9. Method according to one of the preceding claims, characterized in that the geometric meta-model (2) with respect to the geometry-based simulation component fulfills at least one geometry MUST criterion within predetermined geometry MUST criterion limit values ​​and / or the geometric meta-model (2) behaves with respect to the geometry MUST criterion within the predetermined geometry MUST criterion limit values ​​in the same way as the real geometry of the good part (1) or the simulation geometry of the molded part to be produced, ie as the learning geometry size 10. Method according to one of the preceding claims, characterized in that the geometric meta-model (2) with respect to the geometry-based simulation component fulfills at least one geometry CAN criterion within predetermined geometry CAN criterion limit values ​​and / or the geometric meta-model (2) with respect to the geometry CAN criterion within the predetermined geometry CAN criterion limit values ​​behaves like the real geometry of the good part (1) or the simulation geometry of the molded part to be produced, ie like the learning geometry variable.

11. Method according to claim 9, characterized in that the geometry MUST criterion is a total volume to be filled (Vfiii.tot).

12. Method according to claim 10, characterized in that the geometry CAN criterion is a relation of dimensions of the control structures (11) of the geometric meta-model, e.g. L / D (length to diameter) for a cylinder.

13. Method according to one or more of the preceding claims, characterized in that the geometric meta-model (2) in relation on the process-based simulation component at least one process MUST criterion is fulfilled within predefined process MUST criterion limit values ​​and / or that a process simulated with the meta-model (2) behaves with regard to the process MUST criterion within the predefined process MUST criterion limit values ​​like the real good part manufacturing process or like the simulation of the good part manufacturing process, ie like the learning process variable.

14. Method according to one or more of the preceding claims, characterized in that the geometric meta-model (2) with respect to the process-based simulation component fulfills at least one process CAN criterion within predetermined process CAN criteria limit values ​​and / or that a process simulated with the geometric meta-model (2) behaves with respect to the process CAN criterion within the predetermined process CAN criteria limit values ​​like the real good part manufacturing process or like the simulation of the good part manufacturing process, ie like the learning process variable.

15. The method according to claim 13, characterized in that the process MUST criterion comprises at least one or more selected from the group: - plasticizing screw stroke curve of the injection phase (Sinj) and / or values ​​calculated therefrom; - injection pressure curve (pinj) or singular injection pressure values ​​(pinj) and / or values ​​calculated therefrom; - Physical material data, e.g. in the form of material models such as pvT and cross-WLF models; - Temperature value (Tinj), which represents a temperature of the material melt, especially near the nozzle or is a hot runner temperature.

16. The method according to claim 14, characterized in that the process CAN criterion one or more selected from the group: - Nozzle pressure curve (pnoz,) and / or values ​​calculated from it; - Volume flow curve of the injection phase (7 in j) and / or values ​​calculated therefrom; - cavity pressure curve (pcav) and / or values ​​calculated from it; - Closing force curve (Fci P )and / or values ​​calculated therefrom; - current curve (lDrv) and / or values ​​calculated from it.

17. Method according to one or more of the preceding claims, characterized in that the geometric meta-model (2) with respect to the design-based simulation component fulfills at least one design MUST criterion within predetermined design MUST criterion limits and / or that a process simulated with the geometric meta-model (2) behaves with respect to the design MUST criterion within the predetermined design MUST criterion limits like the real good part manufacturing process or like the simulation of the good part manufacturing process, ie like the learning design target variable.

18. Method according to claim 17, characterized in that the design MUST criterion is - mold component filling time (tfiii) under a boundary condition of the total volume to be filled (Vfiii,tot).

19. Method according to one or more of the preceding claims, characterized in that the geometric meta-model, with respect to the design-based simulation component, fulfills at least one design CAN criterion within predetermined design CAN criteria limits and / or that a process simulated with the geometric meta-model (2) behaves in the same way as the real good part with respect to the design CAN criterion within the predetermined design CAN limits. manufacturing process or how the simulation of the good part manufacturing process, ie as the learning design target.

20. Method according to claim 19, characterized in that the design CAN criterion comprises at least one or more selected from the group: solidification time (tsoiid); Volume flow curve and values ​​derived from it, e.g. maximum volume flow (7 max), average volume flow (7 av g)iSt.

21. Method according to one of the preceding claims, characterized in that the iterative adaptation of the basic geometry or the intermediate geometry is carried out by changing a geometric extension of one or more of the geometric control structures (11) in one or more of the spatial axes (x-axis; y-axis; z-axis).

22. Method according to one of the preceding claims, characterized in that the iterative adaptation of the basic geometry or the intermediate geometry is carried out by Boolean addition of a further rule structure (11) to the existing basic geometry or to the existing intermediate geometry.

23. Method according to one of the preceding claims, characterized in that the iterative adaptation of the base geometry or the intermediate geometry is carried out by Boolean subtraction of a further rule structure (11) from the existing base geometry or from the existing intermediate geometry.

24. Method according to one of the preceding claims, characterized in that the iterative adaptation of the basic geometry or the intermediate geometry is carried out by exchanging a control structure (11) of the existing basic geometry or the existing intermediate geometry with another control structure (11).

25. Method according to one of the preceding claims, characterized in that a characteristic tool system response of the geometric meta-model (2) produces, within predetermined tool system response limit values, the same tool system response as the real good part manufacturing process or as the simulation of the good part manufacturing process.

26. Method according to one of the preceding claims, characterized in that a total volume of the geometric meta-model (2) corresponds to a total volume of the good part, in particular including ancillary structures or the volume used in the simulation of the good part process.

27. Method according to one of the preceding claims, characterized in that from a plurality of intermediate geometries which satisfy both the geometry MUST criterion and the process MUST criterion and the design MUST criterion, that intermediate geometry is selected as the geometric meta-model (2) which comes closest to the learning process variable with respect to at least one process CAN criterion and / or to the learning geometry variable with respect to at least one geometry CAN criterion and / or to at least one design CAN criterion.

28. Method according to one of the preceding claims, characterized in that a determination of the total volume to be filled (Vfiii.tot) takes place taking into account a melt compression and / or taking into account an elasticity of a plasticizing screw and / or taking into account a closing behavior and / or a leakage behavior of a non-return valve.

29. Method according to one of the preceding claims, characterized in that the tool system response is a pressure curve p(s) which results from an identification speed signal v(s) of the Plasticizing screw, with which the tool is filled.

30. Method according to one of the preceding claims, characterized in that the tool system response is a speed curve of the plasticizing screw v(s), which results from an identification pressure signal p(s) with which the tool is filled.

31. Method according to one of the preceding claims, characterized in that the test plan design, in addition to the simulation optimization objective, also includes at least information on an iteration step size for the iterative adaptation in step b.5.1 ) and / or upper and lower limit values ​​for the iterative adaptation in step b.5.1 ).

32. Method according to one of the preceding claims, characterized in that a network is assigned to the basic geometry, the intermediate geometry and the geometric meta-model (2) as part of a networking and / or an inlet I defined with regard to size and / or shape and / or arrangement on the geometry is assigned to the basic geometry, the intermediate geometry and the geometric meta-model (2).

33. Method according to claim 32, characterized in that the inlet I remains unchanged during the iterative adaptation at least with regard to the arrangement of the inlet I on the base geometry, the intermediate geometry and the geometric metamodel.

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