METHOD AND DEVICE FOR REDUCING THE AMOUNT OF REWORKING REQUIRED ON MOLD CAVITIES PRIOR TO THEIR USE IN SERIES PRODUCTION

Abstract

A method for determining optimized shape data representing a shape of a molded workpiece formed from a molded material or/and a mold cavity of a molding tool, wherein the molded material hardens depending on at least one solidification parameter, the method including: a) providing shape data representing a shape of the workpiece or/and cavity, b) providing material data representing the molded material, c) providing molding process data representing the molding process, d) providing tool data representing the tool embodying the cavity, e) determining predictive shape data based on initial model data comprising the at least one solidification parameter and data provided in steps a), b), c), and d) simulating the molding process, f) generating optimized predictive shape data as the optimized shape data based on at least predictive shape data determined in step e) and based on first initial AI data comprising the at least one solidification parameter and data provided in steps a, b), c), and d), by means of an artificial neural simulation optimization network trained to optimize predictive shape data.

Claims

1-15. (canceled)

16. Method for determining optimized shape data which represent a shape of a molded workpiece or/and a shape of a mold cavity of a molding tool, wherein the molded workpiece is formed from a molded material which is introduced in a flowable manner into the mold cavity as part of the molding shaping process, wherein the molded material hardens as a function of at least one solidification parameter, wherein the method comprises: a) providing initial shape data representing an initial shape of a workpiece to be molded and/or of an initial cavity to be used for molding the workpiece, b) providing material data representing the molded material, c) providing molding process data representing the molding process, d) providing tool data representing information about the tool embodying the mold cavity beyond the initial shape of the mold cavity, e) determining predictive shape data based on initial model data comprising the at least one solidification parameter and initial shape data, material data, molding process data, and tool data provided in steps a), b), c), and d) by means of electronic data processing system by simulating the molding process, f) generating optimized predictive shape data as the optimized shape data based on at least predictive shape data determined in step e) and based on first initial AI data comprising the at least one solidification parameter and initial shape data, material data, molding process data, and tool data provided in steps a, b, c, and d), by means of an electronic data processing system, wherein the electronic data processing system is configured as an artificial neural simulation optimization network trained to optimize predictive shape data.

17. The method according to claim 16, wherein the method comprises the following further step: g) generating revised shape data as further optimized shape data, wherein the revised shape data represents a revised shape of the mold cavity of the molding tool, based on at least optimized predictive shape data determined in step f) and second initial AI data, which comprise the at least one solidification parameter and initial shape data, material data, molding process data and tool data provided in steps a), b), c) and d), by means of an electronic data processing system which is designed as an artificial neural shape optimization network trained for shape optimization.

18. The method according to claim 17, wherein at least a part of the second initial AI data is also first initial AI data.

19. The method according to claim 17, wherein a major part of the second initial AI data is also first initial AI data.

20. The method according to claim 17, wherein the artificial neural simulation optimization network is the artificial neural shape optimization network.

21. The method according to claim 16, wherein at least a part of the initial model data is also initial AI data.

22. The method according to claim 16, wherein a major part of the initial model data is also initial AI data, and wherein the electronic data processing system performing the simulation and the trained artificial neural simulation optimization network retrieve their respective initial data from initial model and AI data from the same data source.

23. The method according to claim 16, wherein the electronic data processing system designed for simulation of the molding process determines the prediction shape data by model-based simulation.

24. The method according to claim 16, wherein the electronic data processing system designed for simulation of the molding process determines the prediction shape data by model-based simulation using a numerical model including a numerical finite element model or/and a numerical finite volume model or/and a numerical finite difference model.

25. The method according to claim 16, wherein the initial shape data comprises nominal dimensions including length dimensions or/and angle dimensions or/and curvature parameters, of the workpiece or/and of the initial cavity.

26. The method according to claim 16, wherein the material data comprise at least one value of density, heat capacity, thermal conductivity, viscosity, thermal expansion coefficient, anisotropy coefficient and at least one characteristic material-dependent threshold value, including softening temperature, melting temperature, activation temperature or glass transition temperature, yield strength, breaking strength, of at least one component of the molded material and the like.

27. The method according to claim 16, wherein the material data comprise at least one value of density, heat capacity, thermal conductivity, viscosity, thermal expansion coefficient, anisotropy coefficient and at least one characteristic material-dependent threshold value, including softening temperature, melting temperature, activation temperature or glass transition temperature, yield strength, breaking strength, of at least one component of the molded material and the like, wherein a value of the material data is a correlation of values of the respective physical quantity depending on amounts of at least one further physical quantity.

28. The method according to claim 16, wherein the molding process data comprises at least one value of molding duration, molding pressure, amount of material introduced into the cavity, material temperature of the molding material at the beginning of the molding, time interval between introduction of the material into the cavity and time of opening of the cavity, holding pressure, holding pressure duration, ambient temperature and the like, wherein preferably the value of the molding process data is a correlation of values of amounts of the relevant physical quantity depending on amounts of at least one further physical quantity.

29. The method according to claim 16, wherein the molding process data comprises at least one value of molding duration, molding pressure, amount of material introduced into the cavity, material temperature of the molding material at the beginning of the molding, time interval between introduction of the material into the cavity and time of opening of the cavity, holding pressure, holding pressure duration, ambient temperature and the like, wherein the value of the molding process data is a correlation of values of amounts of the relevant physical quantity depending on amounts of at least one further physical quantity.

30. The method according to claim 16, wherein the tool data comprises at least one value of density of a material of the tool, heat capacity of a material of the tool, thermal conductivity of a material of the tool, thermal expansion coefficient of a material of the tool, mass of at least one tool component, at least one dimension of at least one tool component, density of a coolant used in or on the tool, heat capacity of the coolant, inlet temperature of the coolant into the tool, outlet temperature of the coolant from the tool and the like.

31. The method according to claim 16, wherein the tool data comprises at least one value of density of a material of the tool, heat capacity of a material of the tool, thermal conductivity of a material of the tool, thermal expansion coefficient of a material of the tool, mass of at least one tool component, at least one dimension of at least one tool component, density of a coolant used in or on the tool, heat capacity of the coolant, inlet temperature of the coolant into the tool, outlet temperature of the coolant from the tool and the like, wherein a value of the tool data is a correlation of values of amounts of the respective physical quantity depending on values of at least one further physical quantity.

32. The method according to claim 17, wherein the method comprises the step of comparing prediction shape data optimized by the trained artificial neural simulation optimization network with initial shape data, wherein step g) is executed depending on the result of the comparing step.

33. The method according to claim 16, wherein the method further comprises training of the artificial neural simulation optimization network using a workpiece molded based on the initial model data and using prediction shape data determined in step e).

34. The method according to claim 17, wherein the method further comprises generating control data for controlling at least one processing machine for producing a mold cavity of the molding tool on the basis of the initial shape data or/and on the basis of the revised shape data.

35. The method according to claim 17, wherein the method further comprises generating control data for controlling at least one processing machine for producing a mold cavity of the molding tool on the basis of the initial shape data or/and on the basis of the revised shape data and/or tool data, and wherein the method comprises controlling the at least one processing machine on the basis of the generated control data.

36. An electronic data processing device comprising the data processing system configured to simulate the molding process and the electronic data processing system configured as the artificial neural simulation optimization network trained for simulation optimization, wherein the electronic data processing device is configured to perform the method according to claim 16.

37. A machine arrangement comprising at least one processing machine for shape-changing processing of a tool blank and an electronic data processing device according to claim 36, wherein the electronic data processing device is adapted for generating control data operation for controlling at least one processing machine for producing a mold cavity of the molding tool on the basis of the initial shape data or/and on the basis of the revised shape data and/or tool data, wherein the processing machine is adapted to carry out a processing operation based on control data generated by the electronic data processing device.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The invention may take physical form in certain parts and arrangement of parts, a preferred embodiment of which will be described in detail and illustrated in the accompanying drawings which forms a part hereof and wherein:

[0063] FIG. 1 shows a rough schematic representation of an embodiment of an optimization system according to the invention, showing an embodiment of the machine arrangement according to the invention, in which an embodiment of the method for determining optimized shape data according to the invention is carried out.

[0064] FIG. 2 shows a rough schematic representation of initial shape data, prediction shape data, and optimized prediction shape data, and

[0065] FIG. 3 shows a rough schematic representation of revised shape data.

DESCRIPTION OF PREFERRED EMBODIMENTS

[0066] Referring now to the drawings wherein the showings are for the purpose of illustrating preferred and alternative embodiments of the invention only and not for the purpose of limiting the same, in FIG. 1, an embodiment of an optimization system according to the invention, as explained above in the introduction to the description, is generally designated with 10.

[0067] At one or more CAD workstations 12, equipped with data processing systems with CAD program products, in the course of the design of an injection-molded component triggered by a customer order, shape data 14 are developed over a period of time dependent on the complexity of the injection-molded component, namely on the one hand component shape data 14a of the injection-molded component itself and on the other hand cavity shape data 14b of the injection-mold cavity for the manufacture of the injection-molded component with the component shape data 14a.

[0068] These shape data 14 form initial shape data for the further method.

[0069] The design of the injection molded component comprises the selection of the injection molded material(s) used to produce the component, if the injection molded component is produced using a multi-component injection molding process. At least one selected injection molded material may be an injection molded material filled with fibers or/and particles to achieve increased component strength. The injection molded material itself, in pure form or as a matrix material to accommodate fibers or/and particles as filler, is preferably a thermoplastic. This can be a thermoplastic synthetic material or/and a thermoplastic elastomer. However, thermosetting plastics or/and elastomers can also be processed in molding processes of the present invention.

[0070] With the selection of the at least one injection molded material, which is referred to more generally as the molded material in the description introduction, material data 16 representing the at least one injection molded material is available. These data may include density, thermal conductivity tensor, viscosity tensor, softening temperature, melting temperature, glass transition temperature, heat capacity, surface tension, and the like. Usually, the material data will be dependent on other physical quantities, in particular temperature, which plays a special role in injection molding as a solidification parameter.

[0071] Likewise, during the design process, the molding process data 18 is preliminarily determined, such as injection speed, compression speed, volumetric flow rate, injection pressure, injection duration, injection amount, insert arrangement in the case of layers and different types of inserts and injection temperature of the injection molded material, duration and amount of holding pressure after injection of injection molded material into the cavity, closing time of the mold, time interval between the end of injection of injection molded material into the cavity and opening of the cavity, ambient temperature, cooling conditions of the mold, such as coolant flow rate, temperature of the coolant as it enters the mold, temperature of the coolant as it leaves the mold, heat transfer conditions between the mold and the coolant, and the like.

[0072] Likewise, the injection molding tool is designed together with the injection molded component and the injection mold cavity, so that tool data also accrue in the course of the design activity, such as size and mass of the molding tool, density, thermal conductivity and heat capacity of the at least one material used to manufacture the tool, number, shape and local position of sprues and tempering channels, position and shape of the mold parting surfaces and the like. In particular, material data of materials used on the molding tool may in turn be provided as a function of further physical variables, especially temperature as the decisive solidification parameter of an injection molding process.

[0073] The shape data 14, the material data 16, the molding process data 18, and the tool data 20 form initial data for a simulation program product 22 that is operatively installed and set up in a first data processing system 24. The simulation program product 22 preferably uses a numerical model to predict the flow of the flowable injection molded material in the cavity occurring during injection molding, the processes of heat transfer associated with the flow and the resulting solidification, and the subsequent cooling of the injection molded component with the thermally induced changes in dimensions that occur.

[0074] One result of the simulation of the injection molding process is predictive shape data 26 of the injection molded component, as it might be present after demolding and incomplete cooling and hardening, if applicable, given the information input into the simulation model originating from shape data 14, material data 16, molding process data 18, and tool data 20.

[0075] In practice, it has so far been shown that the accuracy of such prediction shape data 26 based only on simulation is not sufficiently accurate, due to the complexity of the processes to be simulated and of the material behavior, and due to inherent inaccuracies in the numerical modeling and the numerous calculation steps resulting therefrom, to accurately design an injection mold cavity or an injection mold tool with such an injection mold cavity on the basis of the shape data 14 originating from the design, in a fail-safe way, such that the manufactured injection tool delivers sufficiently viable injection molded components at the first attempt or with only a short run-in time. The accuracy of the predictive shape data 26 decreases significantly as the complexity of the shape of the injection molded component increases.

[0076] The consequence of these inaccuracies is a considerable amount of reworking on the injection molding tool, for example in order to provide the injection mold cavity with a shape that is pre-distorted with respect to a mere negative image of the desired injection molded component, so that the injection molded component is initially demolded with a distorted shape from the pre-distorted injection mold cavity, whereby after demolding this distorted shape is equalized during further cooling and optionally hardening by thermally and optionally thermo-mechanically or/and thermochemically induced dimensional changes and at the end of the cooling process and optional hardening process, the component's shape is sufficiently close to the desired and/or designed component shape. Unless a mere change in the process control of the injection molding process brings about a sufficient improvement, this is currently done by trial and error and requires expensive processes relative to application and removal of molded material to and from the cavity. Even changing the control of the injection molding process represents undesirable expense, since during such a “run-in” of the designed injection mold, only scrap is produced over a longer period of time.

[0077] In order to reduce this effort and to shorten the time between the design of the component and the tool, according to the method presented here, the prediction shape data 26 are input to an artificial neural simulation optimization network 28, which is specially trained for this purpose and is implemented in a second electronic data processing system 30. The simulation optimization network 28 also receives material data 16, molding process data 18 and tool data 20 to the required extent in order to generate optimized prediction shape data 32 as optimized shape data on the basis of these data.

[0078] As discussed above in the description introduction, the use of the simulation program product 22 in the first data processing system 24 to generate the predictive shape data 26 may be omitted. With quantitatively and qualitatively sufficient amounts of data available, the shape data 14, the material data 16, the molding process data 18 and the tool data 20 can be input directly into the network 28 of the second electronic data processing system 30, if the latter is appropriately trained, in order to generate the optimized shape data 32 directly from this initial data as well as from the at least one solidification parameter. Because of the omission of the processing of data obtained by simulation, the network 28 would then be a shape data optimization network 28, no longer a simulation optimization network. This is, however, only a question of most appropriate designation. Of course, it would remain a trained artificial neural network.

[0079] The situation after generating the optimized prediction shape data 32 using the prediction shape data 26 is shown schematically in FIG. 2.

[0080] FIG. 2 shows the initial shape data 14 graphically represented as a rough schematic virtual injection molded component 60 represented by its component shape data 14a. The injection mold cavity 62 designed with the designed injection molded component 60 is represented by its cavity shape data 14b. The injection mold cavity 62 is shown dash-lined because it is located inside an injection molding tool 64, which is represented by its tool data 20.

[0081] For clarity, the designed injection molded component 60 is shown in FIG. 2 in a stand-alone position to the right of the injection molding tool 64.

[0082] In FIG. 2, to the left of the injection mold 64, the designed virtual injection molded component 60 is again shown with a solid line as represented by its component initial shape data 14a. Superimposed on the injection molded component 60 is shown with dash-dotted line the virtual injection molded component 60′ predicted by the simulation, as represented by its prediction shape data 26. Further overlaid with dashed line is the virtual injection molded component 60″ predicted by the trained artificial neural simulation optimization network 28, as represented by the optimized prediction shape data 32. Due to shrinkage after demolding, the expected injection molded component differs from the desired designed shape, wherein the prediction accuracy of the optimized prediction shape data 32 is much higher than that of the prediction shape data 26. The shape deviations in FIG. 2 are intended to be understood qualitatively and symbolically only. They are only for illustration purposes and do not represent real shape deviations of a real existing component.

[0083] If the method were executed with omission of step e) and substitution of step f) by the modified step f′) described above, the virtual injection molded component 60′ predicted by the simulation would be omitted. The designed virtual injection molded component 60 and the predicted virtual injection molded component 60″ generated by the trained artificial shape data optimization network 28 would remain.

[0084] The optimized prediction shape data 32 may be output by the second electronic data processing system 30 for further use or may be processed internally. For example, a comparison instance 34, which is arranged in the second electronic data processing system 30 only by way of example in FIG. 1, can compare the optimized prediction shape data 32 of the injection molded component with the initial shape data 14a of the injection molded component to determine whether or not the deviations of the optimized prediction shape data 32 from the initial shape data 14a are within a predetermined tolerance range.

[0085] If the expected injection molded component deviates from the initial shape data 14a by more than the acceptable predetermined tolerance range based on its optimized prediction shape data 32, then the optimized prediction shape data 32 may be fed to a trained artificial neural shape optimization network 36 in a third electronic data processing system 38. Alternatively, the artificial neural simulation optimization network 28 may be the shape optimization network 36. Likewise, the shape optimization network 36 may be implemented in the second electronic data processing system or in the first electronic data processing system 24, in a manner different from that shown in FIG. 1.

[0086] The artificial neural shape optimization network 36, which receives not only initial shape data 14, preferably all initial shape data 14, but also material data 16, molding process data 18 and tool data 20 as initial data, generates revised shape data 14b′ of the injection mold cavity on the basis of its learned structure, which as new initial shape data 14b′ forms the basis of a renewed run for generating optimized prediction shape data 32. The artificial neural shape optimization network 36 thereby generates revised shape data 14b′ of the injection mold cavity, which results in a virtual injection molded component 60′″, the dimensions of which are expected to be less different from the initial shape 14a of the designed injection molded component. Preferably, the dimensions of the injection molded component 60′″ produced with an injection mold cavity 62′ with the revised shape data 14b′ are within the specified tolerance range. This is checked with a new process run.

[0087] Compared to FIG. 2, FIG. 3 shows the result of a new process run based on the revised shape data 14b′ as the initial shape data of the injection mold cavity 62′. The desired, designed virtual injection molded component 60 is unchanged and continues to be the target of the process. The shape of the injection mold cavity 62′ in the thus revised injection molding tool 64′ is changed based on the revised shape data 14b′ compared to the previously considered injection mold cavity 62. The resulting expected virtual injection molded component 60′″, represented by the optimized prediction shape data 32 of the injection molded part 60′″ obtained during the new process run, still does not correspond exactly to the designed and thus idealized injection molded component 60. However, its deviation from the latter in terms of shape is only so low that it can be assumed to be a good part.

[0088] If the goal of obtaining an expected injection molded component 60′″ that reproduces the desired initial shape data 14a with sufficient accuracy is achieved, the result of the comparison of the respective current optimized prediction shape data 32 of the expected injection molded component with the initial shape data 14a, which is performed by the comparison instance 34, is the recognition that the optimized prediction shape data 32 of the expected injection molded component 60′″ is within the tolerance range, whereupon the cavity shape data 14b or 14b′ which have led to the optimized predicted shape data 32 can be fed to a CAD/CAM instance 40 which, based on the cavity shape data 14b or 14b′ generates control data for at least one processing machine 42, such as a milling machine. Based on the control data generated by the CAD/CAM instance 40, the at least one processing machine 42 generates a component embodying the injection mold cavity as a tool component.

[0089] In this way, the path from a desired injection-molded component to an injection-molding process providing the desired injection-molded component with a functioning injection molding tool, and the effort required for this purpose, can be significantly reduced.

[0090] While considerable emphasis has been placed on the preferred embodiments of the invention illustrated and described herein, it will be appreciated that other embodiments, and equivalences thereof, can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. Furthermore, the embodiments described above can be combined to form yet other embodiments of the invention of this application. Accordingly, it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the invention and not as a limitation.