Method and device for reducing the amount of reworking required on mold cavities prior to their use in series production

Patent metadata

Field	Value
Publication number	US-2023241826-A1
Application number	US-202318102529-A
Country	US
Kind code	A1
Filing date	Jan 27, 2023
Priority date	Feb 2, 2022
Publication date	Aug 3, 2023
Grant date	—

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.

First claim

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 amount

Assignees

• Roechling Automotive Se & Co Kg

Inventors

• Tsigkopoulos Thomas

Classifications

• B29C37/00 Primary

  Component parts, details, accessories or auxiliary operations, not covered by group B29C33/00 or B29C35/00 · CPC title

• B29C37/005

  Compensating volume or shape change during moulding, in general · CPC title

• G06F30/27

  using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model · CPC title

• G06F30/17

  Mechanical parametric or variational design · CPC title

• G06N3/08

  Learning methods · CPC title