Method for process design for a casting device and method for controlling a casting device

Abstract

A method for quickly finding robust operating points of a casting process is disclosed. Metamodels and extrapolatable models contribute to reducing the experimental effort both in simulation and for practical experiments, and these models are subsequently used for autonomous control of the casting process.

Claims

1. A process design method for a casting device, comprising: carrying out a test point calculation, wherein process parameters of a casting process for a casting component are assigned to a respective test point, wherein a number of process parameters define an n-dimensional test space, and wherein the test points are calculated sequentially filling the test space within the test space and are transferred to a casting process simulation; carrying out the casting process simulation, wherein production of the casting component is simulated sequentially for the test points transferred from the test point calculation using the process parameters assigned to the respective test point, wherein sequential production of two or more casting components is simulated for each test point in a virtual mold until a temperature of the virtual mold has reached a steady state, and wherein an output parameter or a plurality of output parameters are evaluated for each casting component of a test point; carrying out an optimization, wherein at least one metamodel of the casting process simulation is created for at least part of the n-dimensional test space using the process parameters and assigned output parameters, and wherein a robust, steady optimum with its assigned process parameters is determined for one of the output parameters or for a plurality of the output parameters; and carrying out a casting process, wherein at least one casting component is produced by a casting device, wherein the process parameters assigned to the robust, steady optimum are used as process parameters of the casting device, and wherein an evaluation of one output parameter or a plurality of output parameters for the casting component is carried out.

2. The method according to claim 1, wherein the test space is limited by one or more physical previously known input constraints and/or one or more machine-specific previously known input constraints and/or one or more component-specific previously known input constraints.

3. The method according to claim 1, wherein the test space is limited by one or more physical output constraints and/or one or more machine-specific output constraints and/or one or more component-specific output constraints, and the output constraints are determined based on the output parameters of the casting process simulation and/or the casting process.

4. The method according to claim 3, wherein an extrapolatable model is calculated for at least one output constraint, wherein compliance with the output constraint is checked for each test point before it is transferred to the casting process simulation by extrapolation based on the extrapolatable model, wherein, in case of compliance with the output constraint, the test point is transferred to the casting process simulation, and wherein, in case of non-compliance with the output constraint, the test point is discarded and a new test point is calculated using a distance criterion within the test space and this new test point is again checked for compliance with the output constraint, or the test point is discarded and a new test point is determined using the extrapolatable model on a boundary of the output constraint, or the test point is shifted using the extrapolatable model while maintaining a safety distance from the boundary of the output constraint.

5. The method according to claim 4, wherein the extrapolatable model is a linear regression, or wherein the extrapolatable model is an AI model.

6. The method according to claim 4, wherein a robust test point of a similar component is specified as a first test point, or wherein a central test point within the test space is selected as a first test point, the process parameters of which have a specified minimum distance to previously known input constraints.

7. The method according to claim 6, wherein starting from the first test point, test points are first defined within the test space that do not exceed a specified distance from the first test point until the extrapolatable model can be formed, which is recalculated based on a currently specified test point and nearest neighbors specific to the test point.

8. The method according to claim 1, wherein the test points are calculated sequentially filling the test space based on a distance criterion within the test space, wherein the distance criterion defines a distance of a next test point to be calculated from one or more previous test points within the test space.

9. The method according to claim 1, wherein one or more process parameters are specified for each test point, which are selected from: melting temperature, pressure curve, pressure holding time, setting time, mold opening time, cooling parameters, on/off times.

10. The method according to claim 9, wherein for one or more of the process parameters, previously known input constraints limit the test space.

11. The method according to claim 1, wherein an output parameter of the casting process simulation is a cast component defect of the casting component, and/or wherein an output parameter of the casting process simulation is a machine parameter.

12. The method according to claim 1, wherein the steady state within the casting process simulation is considered to have been reached if a change in the temperature of the virtual mold for successive cast components falls below a specified threshold value.

13. The method according to claim 1, wherein a metamodel is created for at least one casting defect, and/or wherein a metamodel is created for at least one machine parameter.

14. The method according to claim 13, wherein the metamodel is an AI model that is trained and validated based on the process parameters of the test points and the output parameters of the casting process simulation.

15. The method according to claim 14, wherein a validation of the AI model comprises determining a model quality of the AI model by comparing an output parameter of the casting process simulation for a validation test point with a prediction of the AI model for the output parameter of this validation test point, releasing the AI model if a specified model quality is achieved or repeating the validation for one or more further test points if the specified model quality is not achieved, and using the output parameters of the casting process simulation for the validation test point to train the AI model before renewed validation.

16. The method according to claim 1, wherein test points in a vicinity of a possible robust, steady optimum are calculated by the test point calculation, and wherein the test points in the vicinity of the possible robust, steady optimum are evaluated using the metamodel in order to confirm the possible robust, steady optimum as the robust, steady optimum.

17. The method according to claim 16, wherein the robust optimum is validated before the casting process is carried out by the casting process simulation, and wherein test points in the vicinity of the robust, steady optimum are validated using the casting process simulation.

18. The method according to claim 1, wherein the optimization is a robust multi-objective optimization.

19. The method according to claim 16, wherein starting from the robust, steady optimum, further test points in the vicinity of the robust, steady optimum are run using the casting process.

20. The method according to claim 19, wherein the order of the test points is sorted starting from the robust optimum with increasing distance in the test space and/or sorted against a background of energy and/or time efficiency.

21. The method according to claim 1, wherein sensor data from sensors of the casting device are used to validate and/or improve and/or recreate the metamodel or metamodels.

22. The method according to claim 4, wherein sensor data from sensors of the casting device are used to validate and/or improve and/or recreate at least one extrapolatable model.

23. A process design method for a casting device, comprising: carrying out a test point calculation, wherein process parameters of a casting process for a casting component are assigned to a respective test point, wherein a number of process parameters define an n-dimensional test space, and wherein the test points are calculated sequentially filling the test space within the test space and transferred to a casting process; carrying out the casting process, wherein production of the casting component is carried out sequentially for the test points transferred from the test point calculation using the process parameters assigned to the respective test point, wherein sequential production of two or more casting components is carried out for each test point in a mold of the casting device until a temperature of the mold has reached a steady state, and wherein an output parameter or a plurality of output parameters are evaluated for each casting component of a test point; carrying out an optimization, wherein at least one metamodel of the casting process is created for at least part of the n-dimensional test space using the process parameters and assigned output parameters, wherein a robust, steady optimum with its assigned process parameters is determined for one of the output parameters or for a plurality of the output parameters; further carrying out the casting process, wherein at least one casting component is produced by a casting device, wherein the process parameters assigned to the robust, steady optimum are used as process parameters of the casting device, and wherein an evaluation of output parameter or a plurality of output parameters for the casting component is carried out.

24. A process design method for a casting device, comprising: carrying out a test point calculation, wherein process parameters of a casting process for a casting component are assigned to a respective test point, wherein a number of process parameters define an n-dimensional test space and wherein the test points are calculated sequentially filling the test space within the test space and are transferred to a simulated casting process and/or a casting process; carrying out the simulated casting process and/or the casting process, wherein production of the casting component is simulated and/or carried out sequentially for the test points transferred by the test point calculation using the process parameters assigned to the respective test point, wherein sequential production of two or more casting components is carried out for each test point in a virtual casting mold of the casting device and/or in a casting mold of the casting device until a temperature of the virtual casting mold and/or the casting mold has reached a steady state, wherein an output parameter or a plurality of output parameters are evaluated for each casting component of a test point; carrying out an optimization, wherein at least one metamodel is created for at least a part of the n-dimensional test space using the process parameters and assigned output parameters, wherein a robust, steady optimum with its assigned process parameters is determined for one of the output parameters or for a plurality of the output parameters; and further carrying out the casting process, wherein at least one casting component is produced by a casting device, wherein the process parameters assigned to the robust, steady optimum are used as process parameters of the casting device and wherein an evaluation of one output parameter or a plurality of output parameters for the casting component is carried out.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0126] The invention is described in more detail below with reference to drawings illustrating exemplary embodiments.

[0127] FIG. 1A shows a casting device with a cast component;

[0128] FIG. 1B shows a virtual casting device;

[0129] FIG. 2 shows test points in a test space;

[0130] FIG. 3 shows a flow chart of a method according to the disclosure.

DETAILED DESCRIPTION

[0131] FIG. 1A shows a casting device 100. The casting device 100 is intended for the production of cast aluminum components 200.

[0132] The casting device 100 has a melting crucible 110 in which molten aluminum 112 is stored. The casting device 100 has a casting mold 120 with mold cavities 130, wherein the molten aluminum 112 can be introduced into the mold cavities 130 via pipelines 140.

[0133] A cast aluminum component 200 produced using the casting device 100 has casting defects, i.e. pores 210 or defects 220. The number and characteristics of a porosity and the defect in a component determine the component quality.

[0134] If the cast aluminum component 200 is a new component for which stable process parameters for operating the casting device 100 are not yet known, there are two challenges in particular. On the one hand, a stable process should be set up as quickly as possible and with little effort, which enables reliable production of the cast aluminum component 200 in the specified quality. Secondly, the process parameters that are primarily responsible for the occurrence of defects or imperfections on the component are to be determined.

[0135] In order to reduce the number of real tests required to find stable process parameters, a virtual casting device 300 is used to simulate the casting process (FIG. 1B).

[0136] For this purpose, a casting process simulation 310 is supplied with input data 320 in order to simulate test points with specified process parameters. The input data includes, for example, a melt temperature, a pressure curve, a pressure holding time, a solidification, a mold opening time, cooling parameters and the like. A parameter set of a test point therefore comprises specified values for this input data, wherein virtual components are produced sequentially for a specified test point until a temperature of the casting mold or several temperatures of the casting mold, depending on the number of virtual or real thermocouples, has reached a steady state.

[0137] Output parameters 330 are determined for each virtually manufactured component, such as component defects or porosity or mold temperatures using virtual temperature sensors, as well as critical output variables for the safe operation of the system.

[0138] The data from the casting process simulation 310 is used to train a metamodel in the form of an AI model 340.

[0139] As soon as the AI model 340 reflects the results of the casting process simulation 310 in a sufficiently accurate manner, i.e. the training of the AI model 340 has been completed and the AI model 340 has been validated, process parameters for a steady state of the mold temperature can be determined with the aid of a multi-objective optimization, which ensure stable process control on the one hand and sufficient component quality on the otherinitially viewed purely virtually using the casting process simulation 310 and using the AI model 340. These process parameters therefore belong to a test point that can be described as a robust, steady optimum.

[0140] This virtually determined optimum is the basis for the first real casting test using the casting device 100. During the real casting test, temperature sensors are used to measure the temperature of the melt and the casting mold at various positions. These measured values and a real determined component quality are in turn used to further improve the AI model 340, i.e. to train and validate or recalculate it.

[0141] The provision of the AI model 340 is explained in more detail below with reference to FIGS. 2 and 3. Decisive for the quality of the AI model 340 and for the efficiency during the creation of the AI model is in particular the selection of the first test points or the first test point as well as the specification of the partially known test space.

[0142] The test space is multidimensional in this case, as a wide range of values is possible for all the input data mentioned above and each individual value of a relevant parameter can theoretically be combined with all conceivable combinations and variations of all individual values of all other parameters.

[0143] To simplify the following explanations, only test points for two input parameters are discussed in FIG. 2. However, it should be clear that the procedure described in FIG. 2 can be applied to multidimensional parameter sets.

[0144] FIG. 2 shows a schematic diagram (I) of a rectangular test chamber V1, with a melt temperature T being shown in ? Celsius on the abscissa and a mold opening time t in seconds on the ordinate.

[0145] In a casting process simulation, the entire test space shown in diagram (I) could in principle be simulated using test points, wherein for each test point, as already described at the beginning, a number of components could be manufactured virtually in sequence until the steady state for one or more temperatures of the casting mold is reached.

[0146] However, not all of the test points shown in diagram (I) can be moved at all in practice by means of the casting device 100, or in any case cannot be moved without damaging the casting device 100, the casting mold 120 or other components (input constraints and output constraints). The input constraints are represented schematically by a barrier S1, wherein all test points to the left of the barrier are not movable, while all test points to the right of the barrier are movabletaking into account the input constraints.

[0147] Furthermore, not every one of the test points shown enables the production of a cast component in the specified quality in practice (output constraints). The test space is therefore additionally restricted by output constraints, as shown schematically by the barrier S2. In FIG. 2, the diagram (II) shows the barrier S2 as a dashed line, wherein the test points located within this dashed line are test points that can be moved in reality and the test points located outside this line are test points that cannot be moved in reality.

[0148] The test points that can be moved in reality are therefore limited by various input constraints and output constraints. For example, a melt temperature that is too low leads to inadequate mold filling and faulty components. Furthermore, the cooling time must be shorter than the mold closing time in order to prevent damage to the casting device. Furthermore, the mold should not be opened before the melt has completely solidified. There are therefore a large number of limiting physical and mechanical boundary conditions that restrict the feasibility of certain test points.

[0149] These input constraints are taken into account in the test planning and before the first simulations are carried out in order to avoid time-consuming simulations being calculated for test points that cannot be represented in reality. The output constraints are not known in advance and are determined during the tests. The barrier S1 is therefore known before the simulation of the first test point, while the barrier S2 is determined dynamically during the filling of the test space.

[0150] A safe test point P1, which has proven to be a reliable or robust operating point for a casting device for a similar component, is preferably selected as the starting point for a first test point of a simulation.

[0151] Further test points are then simulated in the vicinity of this test point until linear regression is possible for the relevant output constraints, on the basis of which further test points can be planned and sequential space-filling test planning can be carried out.

[0152] Linear extrapolation can be used to estimate whether or not the test point can be represented in reality already before performing or simulating the test points, i.e. whether, for example, a machine stop or damage to the machine, mold or the like could occur during the execution of a real test using the parameters in question, or whether it is foreseeable that a good part cannot be produced with these parameters under any circumstances.

[0153] Diagrams (III) and (IV) show the step-by-step filling of the test space, wherein the test points marked with a cross represent the test points excluded from the test series and the test points filled in black represent test points that can be moved in reality.

[0154] In the following, a method is further described with reference to FIG. 3.

[0155] A method step (A) relates to the performance of a test point calculation, wherein process parameters of a casting process for a casting component are assigned to a respective test point, wherein a number of process parameters corresponds to a natural number n?2 and defines an n-dimensional test space and wherein the test points are calculated sequentially filling the test space within the test space and are transferred to a casting process simulation.

[0156] A method step (B) relates to the performance of the casting process simulation, wherein the production of the casting component is simulated sequentially for the test points transferred from the test point calculation, starting from the first test point P1, using the process parameters assigned to the respective test point, wherein the sequential production of two or more casting components is simulated for each test point in a virtual casting mold until the temperature of the virtual casting mold has reached a steady state, and wherein an output parameter or a plurality of output parameters are evaluated for each casting component of a test point.

[0157] A method step (C) relates to the performance of an optimization, wherein at least one metamodel of the casting process simulation is created for at least part of the n-dimensional test space using the process parameters and the assigned output parameters, wherein a robust, steady optimum with its assigned process parameters is determined for one of the output parameters or for several of the output parameters.

[0158] The metamodel is a trained and validated AI model in the form of a neural network. Step (D) describes the validation of the AI model with the following steps: Determining a model quality of the AI model by comparing an output parameter of the casting process simulation for a validation test point with a prediction of the AI model for the output parameter of this validation test point, wherein the AI model is released, if a specified model quality is achieved or the validation is repeated for one or more further test points if the specified model quality is not achieved, wherein the output parameters of the casting process simulation for the validation test point are used to train the AI model before the new validation.

[0159] In step (E), a casting process is carried out by means of the casting device 100, wherein at least one casting component is produced by means of the casting device 100, wherein the process parameters assigned to the robust, steady optimum are used as process parameters of the casting device 100 and wherein an output parameter or a plurality of output parameters for the casting component is evaluated.

[0160] During the practical trials, sensor data from temperature sensors of the casting device 100 for measuring temperatures within the casting mold are collected to further train and validate the AI model 340 and to re-check the robustness of the relevant test point. The method step (F) describes that the sensor data from the sensors of the casting device is used to validate and/or improve and/or recreate the metamodel or metamodels.

[0161] In this way, the AI model 340 can be extended to a hybrid model based on both simulation data and real tests.

[0162] The AI model 340 can be used as part of a model-based control system for regulating the casting device 100, wherein target process points are specified using the AI model 340, the process parameters of which represent a robust optimum.

[0163] In particular, the AI model 340 enables a robust operating point to be reached quickly and autonomous process regulation.