METHOD FOR ADAPTING A COMPONENT DESCRIPTION OF A WORKPIECE TO BE PRODUCED WITH AMORPHOUS PROPERTIES

Abstract

Amorphous metals are a new class of materials in which advantageous physical properties can be achieved. Amorphous metals require rapid cooling in the injection-molding process, which is not achieved in the case of a large number of geometries. The invention relates to a method for adapting a component description of a workpiece to be produced with amorphous properties, which method comprises: —defining a cooling behaviour of at least a part of a workpiece to be produced, taking account of a component description of the workpiece; —adapting at least a part of the component description, taking account of the defined cooling behaviour of the workpiece.

Claims

1. A method for adapting a component description of a workpiece to be produced with amorphous properties, which method comprises: determining a cooling behavior of at least a part of a workpiece to be produced, taking account of a component description of the workpiece; and, adapting at least a part of the component description, taking account of the determined cooling behavior of the workpiece.

2. The method according to claim 1, wherein the adaptation comprises inserting at least one coolant description into the component description, wherein the coolant description indicates a coolant.

3. The method according to claim 1, wherein the adaptation comprises determining a material to be used, wherein the adaptation is performed taking account of the material.

4. The method according to claim 1, wherein optimizing the component description, in particular using a finite element and/or finite volume simulation of the workpiece.

5. The method according to claim 4, wherein the optimization of the component description comprises identifying at least one local geometry of the workpiece in the component description, wherein the local geometry indicates a region of the workpiece in which material can be saved.

6. The method according to claim 5, wherein the determination of the cooling behavior comprises simulating a cooling behavior for the workpiece, wherein the cooling behavior indicates a cooling rate.

7. The method according to claim 4, wherein the component description indicates a plurality of volume elements, wherein the cooling behavior indicates a volume cooling rate for at least one volume element, in particular for each of the plurality of volume elements.

8. The method according to claim 1, wherein comparing a cooling rate to a critical cooling rate, wherein the adaptation is performed taking account of the comparison.

9. The method according to claim 1, wherein the adaptation is performed only when the comparison shows that the cooling rate is below the critical cooling rate for at least one volume element.

10. The method according to claim 1, wherein classification, in particular for at least one, preferably each, volume element, of a component description, particularly preferably using a classifier, as to whether the cooling rate is below the critical cooling rate for at least a part of the workpiece.

11. A control method, comprising providing a component description that indicates a workpiece to be produced; adapting the component description, in particular in accordance with the method according to claim 1; and, controlling a production plant using the adapted component description to produce a workpiece.

12. A computer readable storage medium containing instructions that cause at least one processor to implement a method according to claim 1 when the method is executed by the at least one processor.

13. A device for adapting a component description of a workpiece to be produced with amorphous properties, which device comprises: at least one storage unit for storing at least one component description; at least one cooling determination unit which is designed to determine a cooling behavior of at least a part of a workpiece to be produced, taking account of the at least one component description; and, an adaptation unit which is designed to adapt at least a part of the component description taking account of the determined cooling behavior.

14. The device according to claim 13, wherein the adaptation unit is further designed to determine a material to be used, wherein the adaptation unit is further designed to perform the adaptation of the component description taking account of the material.

15. The device according to claim 13, wherein an optimization unit is further designed to identify at least one local geometry of the workpiece in the component description, wherein the local geometry indicates a region of the workpiece in which material can be saved.

16. The device according to claim 13, wherein a comparison unit that is designed to compare a cooling rate to a critical cooling rate, wherein the adaptation unit is designed to perform the adaptation taking account of the comparison.

17. A system for producing a workpiece, which system comprises: a device for adapting a component description, in particular according to claim 13; and, an injection-molding device which is designed to produce a workpiece using a component description, in particular using an adapted component description.

18. The system according to claim 17, wherein a chamber of the injection-molding device for receiving liquid material; and, a punch which is designed to introduce liquid material into the chamber at a punch speed, wherein the punch speed is selected taking account of the component description.

Description

[0114] The invention is explained in more detail below with reference to exemplary embodiments. In the drawings:

[0115] FIG. 1 shows a schematic illustration of an injection-molding machine;

[0116] FIG. 2 shows a schematic illustration of a tool;

[0117] FIG. 3 shows a cross-section of a workpiece with a coolant;

[0118] FIG. 4 shows a temperature profile for a workpiece;

[0119] FIG. 5 shows an assignment of temperature profiles to volume elements of a component description;

[0120] FIG. 6 schematically shows the process of optimization and adaptation of a component description;

[0121] FIG. 7 shows the mode of operation of an artificial neural network;

[0122] FIG. 8 shows a first exemplary embodiment of a device for adapting a component description;

[0123] FIG. 9 shows a second exemplary embodiment of a device for adapting a component description;

[0124] FIG. 10 shows a third exemplary embodiment of a device for adapting a component description;

[0125] FIG. 11 shows a fourth exemplary embodiment of a device for adapting a component description.

[0126] In the following, the same reference numbers are used for identical parts or parts having the same effect.

[0127] FIG. 1 shows a schematic illustration of an amorphous metal (AMM) injection-molding system 1. The injection-molding system 1 comprises a mold in the tool 2 and a melting chamber 3. The melting chamber 3 is supplied with a solid alloy segment of an amorphously solidifying alloy (blank) 4 by a robot and is placed centrally in an induction coil 5. The blank 4 is heated within the melting chamber 3 by means of a heating element, in particular an induction field which is generated by the induction coil 5. The blank 4 is a solid alloy segment of an amorphously solidifying alloy. The alloy segment 4 comprises, for example, a certain amount of palladium, platinum, zirconium, titanium, copper, aluminum, magnesium, niobium, silicon and/or yttrium.

[0128] The blank 4 is melted by the heating element or the induction coil 5, so that it is present in molten form. Preferably, the blank 4 is heated to a temperature of 1050° C. The molten material is injected into the tool 2 by a piston 6.

[0129] FIG. 2 shows the schematic structure of an injection-molding tool. The molding chamber 11 is filled with a melt by means of one or a plurality of openings 10 leading into a molding chamber 11 of a tool 2. The molding chamber 11 is designed as a counterpart of the workpiece 8 to be produced. In the exemplary embodiment of FIG. 2, it is provided that an opening 10 can be used to guide liquid material into the molding chamber 11. It can be advantageous to use a plurality of sprues for filling the molding chamber 11 in order to achieve a uniform temperature distribution and to reduce turbulence of the melt. A uniform temperature distribution and a small number of turbulences lead to a better cooling operation, to homogeneous cooling and thus to uniform amorphous material properties.

[0130] The liquid material must rapidly cool down within the molding chamber 11 in order to prevent crystallization. The cooling of the liquid material depends greatly on the geometry of the component or workpiece 8 to be produced.

[0131] FIG. 3 shows an example component 20 which is a cylinder 20. The cooling process within the cylinder 20 takes more or less time depending on the size of the cylinder 20, i.e., on its height and diameter. At a critical size of the cylinder 20, cooling cannot be carried out rapidly enough to prevent crystallization of the material. This results in crystalline structures and not in the desired amorphous structures.

[0132] In order to obtain the desired amorphous structures, i.e., to prevent crystallization, the cooling rate in the inner region of the cylinder 20 must be high enough. This means that the cooling rate inside the cylinder 20 will be greater than a critical cooling rate. To accelerate cooling, coolants 21 can be arranged within a component 20 or a workpiece 8. For example, it is conceivable that molded parts 21 be arranged in the component 20 to be produced. In the example of FIG. 3, for example, a metal rod 21 is arranged and fastened within the cylinder 20. Due to the metal rod 21, melting heat can now be released both to the metal rod 21 and into the tool 2; the metal rod 21 thus ensures that a higher cooling rate can be achieved. Due to the possibility of injection-molding molded bodies 20 with high heat conduction, it is possible to also produce larger amorphous two-component components. The amorphous functional surfaces are designed such that they meet the mechanical, physical or chemical requirements. It is thus possible to prevent crystallization despite the size of the component 20.

[0133] FIG. 4 shows an example temperature profile 22. The exemplary embodiment of FIG. 4 relates to the inner part of the workpiece 20 of FIG. 3. As shown, the temperature falls from an initial temperature C1 at a time t1 to a temperature C2 reached at a time t2.

[0134] It is therefore possible to determine a cooling rate that indicates the drop in temperature, i.e., the temperature difference C1−C2, in the interval from t1 to t2. Furthermore, it is possible to determine whether the cooling rate is high enough to prevent crystallization. The cooling rate at which crystallization is prevented can be referred to as the critical cooling rate. In order to determine whether a component or workpiece to be produced will have amorphous properties, it is therefore possible to determine whether the cooling rate at each point of the workpiece is greater than the critical cooling rate.

[0135] Workpieces can be digitally described by a component description, for example by a CAD file. FIG. 5 shows, for example, a component description 30 of a cuboid, which component description is composed of a plurality of volume elements 31, 32. The component description 30 can therefore be, for example, a CAD model which is subdivided into individual volume elements 31, 32 by means of simulation software. The temperature behavior can now be simulated or predicted for each volume element 31, 32 of the component description 30.

[0136] FIG. 5 shows, for example, that a first temperature profile 33 is assigned to a first volume element 31. A second temperature profile 34 is assigned to a second volume element 32. The temperature profiles 33, 34 show the drop in temperature from an initial temperature C1 to a limit temperature C2. In an example of a zirconium-based amorphously solidifying alloy, the initial temperature is approximately 850° C. and the limit temperature is 410° C. The material 8 of the workpiece indicated by the component description 30 has approximately a temperature of 850° C. when it was injected into the mold 2. The glass transition temperature for the selected alloy is approximately 410° C. This means that no crystalline structure is adopted when a critical cooling rate, i.e., a rate above the critical cooling rate, is exceeded. Thus, if the material 8 is cooled down rapidly enough to the critical temperature, amorphous structures will be obtained. If the cooling rate is below the critical cooling rate, the melt will solidify in the crystalline state and not in the amorphous state.

[0137] As can be seen from FIG. 5, the limit temperature C2 is reached in the case of the first temperature profile 33 at a time t2. In the second temperature profile 34, the limit temperature C2 is reached at a time t3. As can be seen from the first and second temperature profiles 33, 34, time t3 is before time t2. This means that the temperature in the temperature profile 33, which is assigned to the first volume element 31, falls more slowly than in the second temperature profile 34, which is assigned to the second volume element 32. The cooling rate in the second temperature profile 34 is therefore greater than in the first temperature profile 33. Assuming that the cooling rate in the first temperature profile 33 is less than the critical cooling rate required for obtaining amorphous structures, the component description 30 will need to be adapted to convey the heat more quickly out of the workpiece 8, so that the cooling rate at the location of the first volume element 31 is greater than the critical cooling rate.

[0138] The temperature diagrams 33 and 34 can be generated using a simulation unit. This means that a simulation of the temperature behavior is carried out for each volume element 31, 32. It is thus possible to determine the temperature diagrams 33 and 34 very precisely. The results of the simulation unit can be provided digitally as cooling behavior, for example as an object in an object-oriented programming language. However, it is also possible for the cooling behavior to be provided as a text file or in any other format.

[0139] FIG. 6 shows an exemplary embodiment in which a component description 30 is optimized and in which coolants 21, 21′ are introduced into the component description 30 of a workpiece, so that the cooling rate is increased. The coolant 21, 21′ is indicated by a coolant description which can be a digital representation of the coolant 21, 21′, for example a CAD file.

[0140] In the exemplary embodiment of FIG. 6, a three-dimensional cuboid is described by the component description 30, wherein FIG. 6 is a lateral sectional view. A force vector F is plotted on the right-hand side of the workpiece or the associated component description 30 at a force application point 2. Using a material optimization method, it is now determined at which locations of the workpiece described by the component description 30 material can be saved. In this case, the material optimization is carried out taking account of the loading condition defined by the force vector F at the force application point 2.

[0141] As can further be seen from FIG. 6, the result of material optimization is an optimized component description 30′ for an optimized workpiece, which comprises empty spaces 36, 36′. Empty spaces 36, 36′ define a local geometry on which no material is necessary, so that the optimized workpiece produced with the component description 30 withstands the defined loading condition.

[0142] It is now provided that coolants 21, 21′ be arranged in the empty spaces 36, 36′ in order to improve the heat transport out of the workpiece. For example, the coolants 21, 21′ can be molded parts made of copper, which have good thermal conductivity. However, it is also possible to use other materials. In the exemplary embodiment shown, the empty spaces 36, 36′ are completely filled. It is also possible for at least the outer contour of the empty spaces 36, 36′ to be filled with a material which has sufficient thermal conductivity, such as copper.

[0143] In addition to a simulation for determining the temperature profiles of a workpiece or the volume elements 31, 32 of a component description 30, it is also possible to carry out a classification. It is thus possible to classify each volume element 31, 32 as to whether a cooling rate assigned to the respective volume element 31, 32 is greater than a critical cooling rate. It is thus possible to dispense with a simulation so that more efficient processing is possible. Such a classification can be carried out using a classifier or a classification unit. For example, so-called nearest neighbor methods, artificial neural networks or support vector machines can be used. These classifiers are trained with training data in a training phase. For a plurality of component descriptions and the respective volume elements contained, the training data contain information on whether the respective cooling rates of the volume elements are greater than a critical cooling rate. The critical cooling rate can be determined taking account of the material provided for the workpiece.

[0144] In addition to a classification, it is also possible to predict a cooling rate value using a regressor or a regression unit. A complicated simulation can thus also be dispensed with. Artificial neural networks can also be considered as regression units.

[0145] FIG. 7 shows an artificial neural network, which in the case of FIG. 7 is designed as a so-called convolutional neural network 40 (CNN 40). The neural network 40 of FIG. 7 can be designed as a classifier or as a regressor.

[0146] Input data 41 for the neural network 40 may be a tensor, i.e., a three-dimensional matrix, which has a plurality of data elements. Each data element can correspond to a volume element. Each data element can be designed as a tuple which indicates whether material is present at the location of the corresponding volume element, what material is used, and/or what initial temperature prevails at the location of the corresponding volume element.

[0147] A CCN is defined by a plurality of parameters. A kernel sequentially scans the input data in a first step. The so-called stride of the kernel indicates by how many volume elements the kernel must be shifted during each scan. The size of the kernel can also be defined. The stride and the size of the kernel thus define the so-called feature detectors 43 which are generated by a first convolution 42. Each feature detector 43 detects a specific feature in the input data. For example, a feature detector 43 may indicate whether or not material is present at a particular location. Overall, a plurality of feature detectors 43 are generated which have not been previously defined manually.

[0148] According to the same principle, a new set of feature generators 45 is generated from the first feature detectors 43 in a second convolution 44, wherein during the second convolution the number of feature generators is reduced compared to the first convolution. Such a step is referred to as pooling or subsampling.

[0149] A third set of feature generators 47 is generated in a third convolution 46. In the last step, a class is assigned to each volume element by means of a so-called soft-max layer. This means that it can be seen from the output whether the cooled speed for a volume element is greater or less than the critical cooling rate.

[0150] Each layer of the CCN 40 consists of a large number of neurons, i.e., of activation functions to which weights are assigned. The output of the neuron is activated or not activated depending on the weight and an input value. Possible activation functions include, for example, logit, arc tan, Gaussian functions. Training of the CCN 40 is performed using the back-propagation algorithm, wherein the values of the weights are determined.

[0151] There are a number of different models for CNN, such as VGG-net, RES-net, general adversiral networks or Google LeNet. Any of these implementations can be used, or another implementation is possible. Training of the neural networks can be carried out efficiently, since a plurality of the operations can be carried out parallelized. The inference, i.e., the querying of values for certain component description, can be carried out very efficiently.

[0152] FIGS. 8-11 show different exemplary embodiments of devices which implement the method steps described above.

[0153] For example, FIG. 8 shows a device 50 which is part of a system 60 and which comprises a storage unit 51. The storage unit 51 is designed to store a component description 52 which describes a workpiece. The device 50 further comprises a cooling determination unit 53, which is designed to determine a cooling behavior 54 using the component description 52. This means that the cooling determination unit 53 determines for volume elements of the component description 52 whether the cooling rate at the volume elements is greater than or less than a critical cooling rate.

[0154] The cooling behavior 54 is read in by an adaptation unit 55 which is also part of the device 50. Furthermore, the adaptation unit 55 reads the component description 52 from the storage unit 51. The adaptation unit 55 is designed to determine, taking account of the component description 52 and the cooling behavior 54, how the component description 52 or the workpiece described by the component description has to be changed so that the cooling rate of all volume elements of the component description 52 is greater than the critical cooling rate.

[0155] An adapted or optimized component description 56 is subsequently delivered to an injection-molding machine 57, which produces the workpiece or component according to the adapted component description 56. In particular, the component description 56 can also indicate information on the operation of the injection-molding machine 57. For example, the component description 56 can indicate an advance speed of the punch 6. It is also conceivable for the adapted component description 56 to indicate via how many inlet openings 10 the liquid material is to be inserted into a mold 2.

[0156] The exemplary embodiment of FIG. 9 corresponds substantially to the exemplary embodiment of FIG. 8. The exemplary embodiment of FIG. 9 shows a system 60′ comprising a device 50′, which also has a storage device 51, a cooling determination unit 53 and an adaptation unit 55. The system 60′ further comprises an injection molding machine 57. In addition, an optimization unit 58 is provided in the device 50′. The optimization unit 58 is designed to determine at least one local geometry on which material can be saved for a workpiece. Such a method is described, for example, in connection with FIG. 6.

[0157] The optimization unit 58 is designed to output an optimized component description 59 to the adaptation unit 55 and the cooling determination unit 53.

[0158] The exemplary embodiment of FIG. 10 corresponds substantially to the exemplary embodiments of FIGS. 8 and 9. FIG. 10 shows a system 60″ with a device 50″ which comprises a storage unit 51, an optimization unit 58 and an adaptation unit 55. The system 60″ further comprises an injection-molding machine 57.

[0159] In the exemplary embodiment of FIG. 10, it is provided that the optimized component description 59 generated by the optimization unit 58 is read in by a simulation unit 53′. The simulation unit 53′ is designed to simulate a cooling behavior or a cooling rate for each volume element of a component description 59. The result of the simulation is read in as cooling behavior 54 by the adaptation unit 55, which is designed to generate an adapted component description 56, taking account of the cooling behavior 54 and the optimized component description 59. The adapted component description 46 is read in by the injection molding machine 57 and used to produce a workpiece or component.

[0160] FIG. 11 shows a further exemplary embodiment which shows a system 60′″ with a device 50″. The device 50″ comprises a storage unit 51, an optimization unit 58 and an adaptation unit 55. The system 60″ further comprises an injection-molding machine 57. The device 50″ moreover comprises an AI system 53′″ which is designed to implement a classifier and/or a regressor, as have been described in connection with FIG. 7.

[0161] The AI system 53″ generates a cooling behavior 54 which can be used by the adaptation unit 55 together with the optimized component description 59 in order to generate an adapted component description 56 which is used by the injection molding machine 57 in order to produce a workpiece or component.

LIST OF REFERENCE SIGNS

[0162] 1, 57 Injection-molding machine [0163] 2 Mold [0164] 3 Melt cylinder [0165] 4, 4′ Heating element [0166] 5 Filler hopper [0167] 6 Screw [0168] 7 Punch [0169] 8 Liquid starting material [0170] 9 Conduit system [0171] 10, 10′, 10″, 10′″ Inlet opening [0172] 11 Molding chamber [0173] 20 Workpiece [0174] 21, 21′ Coolant/copper rod [0175] 22 Temperature profile [0176] 30 Component description/CAD model [0177] 30′ Optimized component description [0178] 31 First volume element [0179] 32 Second volume element [0180] 33 First temperature diagram [0181] 34 Second temperature diagram [0182] 35 Force application point [0183] 36, 36′ Empty space [0184] 40 Artificial neural network [0185] 41 Input data/tensor [0186] 42 First convolution [0187] 43 Feature detector [0188] 44 Subsampling [0189] 45 Second feature detectors [0190] 46 Second convolution [0191] 47 Third feature detectors [0192] 48 Feedforward layer [0193] 49 Output layer [0194] 50, 50′, 50″, 50′″ System [0195] 51 Storage unit [0196] 52 Component description/CAD model [0197] 53 Cooling determination unit [0198] 53′ Simulation unit [0199] 53″ AI system [0200] 54 Cooling behavior [0201] 55 Adaptation unit [0202] 56 Adapted component description [0203] 58 Optimization unit [0204] 59 Optimized component description [0205] 60, 60′, 60″, 60′″ Device [0206] C1 Initial temperature [0207] C2 Target temperature [0208] T1, T2 Time [0209] F Force