TWI819578B - Multi-objective parameters optimization system, method and computer program product thereof - Google Patents

Abstract

A multi-objective parameters optimization system for providing a plurality of optimal machining parameters of a wire electrical discharge machine is provided. The system includes a plurality of post-training neural networks and a multi-objective parameters optimization module. Each post-training neural network outputs an objective prediction value according to a machining parameter combination, respectively. The multi-objective parameters optimization module executes a genetic algorithm that using the objective prediction values outputted from the post-training neural networks according to a plurality of value combinations of the machining parameter combination to find at least one optimal value combination of the machining parameter combination.

Description

Multi-objective parameter optimization systems, methods and computer program products

The invention belongs to the field of artificial intelligence, especially the field of artificial intelligence related to neural networks.

Wire-cut electric discharge machines are commonly used machines in the manufacturing industry. They can produce different processing qualities based on different combinations of processing parameters. Generally speaking, in order to achieve the processing quality required by users, senior operating technicians must manually adjust the required processing parameter combinations based on accumulated experience in the past. However, this process will consume a lot of time, cost and manpower, and may not be Can adjust the optimal combination of processing parameters.

It can be seen that the current technology still has room for improvement. In this regard, the present invention provides a multi-objective parameter optimization system, method and computer program product, which can effectively solve the above problems.

According to one aspect of the present invention, a multi-objective parameter optimization system for providing multi-objective optimized processing parameters of a wire-cut electric discharge machine is proposed. The system includes complex-trained neural networks and parameter optimization modules. Each trained neural network has its own output according to the combination of processing parameters. Output target prediction value. The parameter optimization module executes a genetic algorithm to find the optimal numerical combination of the processing parameter combination using the target predicted values output by the trained neural network based on the various numerical combinations of the processing parameter combination. .

According to another aspect of the present invention, a multi-objective parameter optimization method for providing multi-objective optimized processing parameters of a wire-cut electric discharge machine is provided, and the method is executed by a multi-objective parameter optimization system. The multi-objective parameter optimization system includes complex trained neural networks and parameter optimization modules, and each trained neural network outputs a target prediction value according to a combination of processing parameters. The method includes the steps of: executing a genetic algorithm through a parameter optimization module to use the trained neural network to find out the processing target prediction values based on the multiple numerical combinations of the processing parameter combinations. The optimal numerical combination of parameter combinations.

According to yet another aspect of the present invention, a computer program product stored in a non-transitory computer-readable medium is provided to enable multi-objective parameter optimization system operation to provide multi-objective control of wire-cut electric discharge machines. Optimize processing parameters. The multi-objective parameter optimization system has multiple trained neural networks and parameter optimization modules, and each trained neural network outputs a target prediction value based on a combination of processing parameters. The computer program product includes: instructions to cause the parameter optimization module to execute a genetic algorithm to use the trained neural network to find the target predicted values output based on the multiple numerical combinations of the processing parameter combinations. Optimized numerical combination of processing parameter combinations.

1: Multi-objective parameter optimization system

2: Neural network group after training

3: Parameter optimization module

4: Transfer learning module

10: Wire-cut electrical discharge processing machine

21: Type 1 Neural Networks

22: Type II Neural Network

23: The third type of neural network

24: Type IV Neural Networks

25: Category 5 Neural Networks

26: Category 6 Neural Networks

211, 221, 231: input layer

212, 222, 232: hidden layer

213, 223, 233: Loss function layer

214, 224, 234: Neuron

S61~S69: steps

OF1, OF2: target prediction value

Figure 1 is a schematic diagram of a multi-objective parameter optimization system according to an embodiment of the present invention.

FIG. 2 is a schematic diagram of the operation process of the trained neural network group according to an embodiment of the present invention.

FIG. 3A is a schematic diagram of the operation process of the parameter optimization module according to an embodiment of the present invention.

FIG. 3B is a schematic diagram of the calculation results of the genetic algorithm according to an embodiment of the present invention.

Figure 4 is a schematic diagram of the operation process of the transfer learning module according to an embodiment of the present invention.

FIG. 5A is a schematic diagram of the training model of the first type of neural network according to an embodiment of the present invention.

FIG. 5B is a schematic diagram of the training model of the second type of neural network according to an embodiment of the present invention.

FIG. 5C is a schematic diagram of the training model of the third type of neural network according to an embodiment of the present invention.

FIG. 6 is a flow chart of a multi-objective parameter optimization method according to an embodiment of the present invention.

When read in conjunction with the accompanying drawings, the following embodiments are used to clearly demonstrate the above and other technical contents, features and/or effects of the present invention. Through the elaboration of specific embodiments, people will further understand the technical means and effects adopted by the present invention to achieve the above objectives. In addition, since the contents disclosed in the present invention should be easily understood and implemented by those skilled in the art, all equivalent substitutions or modifications that do not depart from the concept of the present invention should be included in the claims.

It should be noted that in this document, unless otherwise specified, "a" element is not limited to a single element, but may also refer to one or more elements.

In addition, ordinal numbers such as "first" or "second" in the description and claims merely describe the claimed elements, and do not represent or indicate that the claimed elements have any sequential ordinal number, and are not the claimed elements and A sequence between another claimed element or between steps of a manufacturing method. These ordinal numbers are used only to distinguish one request element with a specific name from another request element with the same name.

In addition, the word "adjacent" in the description and claims is used to describe mutual proximity and does not necessarily mean that they are in contact with each other.

In addition, descriptions such as "when..." or "when" in the present invention mean "at the moment, before or after" and other aspects, and are not limited to situations that occur at the same time, which will be explained here. In the present invention, "disposed on" and other similar descriptions indicate the corresponding positional relationship between the two components, and do not limit whether there is contact between the two components, unless there are special limitations, which are explained here. Furthermore, when the present invention describes multiple effects, if the word "or" is used between the effects, it means that the effects can exist independently, but it does not exclude that multiple effects can exist at the same time.

In addition, words such as "connected" or "coupled" in the description and the claims not only refer to a direct connection with another component, but also refer to an indirect connection or electrical connection with another component. In addition, electrical connection includes direct connection, indirect connection, or communication between two components through radio signals.

In addition, in the description and claims, the terms "about", "approximately", "substantially" and "substantially" usually mean that the difference between a value and a given value is within 10% of the given value, or Within 5%, or within 3%, or within 2%, or within 1%, or within the range of 0.5%. The quantities given here are approximate quantities, that is, in the absence of specific instructions for "about", "approximately", "substantially", and "approximately", "about", "approximately", "approximately", The meaning of "substantially" and "substantially". In addition, the terms "range is a first value to a second value" and "range is between a first value and a second value" mean that the range includes the first value, the second value and other values therebetween.

In addition, in this article, the terms "system", "device", "device", "module", or "unit" refer to a digital circuit that includes an electronic component or is composed of a plurality of electronic components. A type of circuit, or other circuits of a broader nature, and unless otherwise specified, they are not necessarily There is a rank or hierarchical relationship. In addition, each component may be implemented as a single circuit or an integrated circuit in a suitable manner, and may include one or more active components, such as transistors or logic gates, or one or more passive components, such as resistors or capacitors. , or inductor, but not limited to this. The components can be connected to each other in a suitable manner, for example, using one or more lines to form a series or parallel connection in conjunction with the input signal and the output signal respectively. In addition, each component can allow input and output signals to enter and exit sequentially or in parallel. The above configurations are all based on actual applications.

In addition, as long as it is reasonable, the technical features of different embodiments disclosed in the present invention may be combined to form another embodiment.

In addition, in this document, “at least one element” includes the form of one or more elements. For example, the description of “at least one element in a range” includes the form of a single element in the range, multiple elements in the range. The appearance of an element and the appearance of all elements in the range. In this article, "and/or" includes single, any multiple and all aspects. For example, the description of "component a, component b and/or component c" includes aspects of any one of the three, and the three The appearance of any two of them and the appearance of any three of them.

Figure 1 is a schematic diagram of a multi-objective parameter optimization system 1 (hereinafter referred to as system 1) according to an embodiment of the present invention. The system 1 can be used to provide multiple optimized processing parameters of the linear cutting electric discharge machine 10 . As shown in Figure 1, the system 1 may include a trained neural network group 2 and a parameter optimization module 3. In addition, the system 1 may also include a transfer learning module 4.

Among them, the trained neural network group 2 may include a plurality of trained neural network groups (also called proxy models). Each trained neural network can output a target prediction value according to a combination of processing parameters, that is, the user can input a combination of processing parameters to the trained neural network, and the trained neural network can output the The target predicted value corresponding to the processing parameter combination. The parameter optimization module 3 can execute a genetic algorithm to find the optimization using the target prediction values of the trained neural network combination of processing parameters. In addition, the transfer learning module 4 can perform a transfer learning on the trained neural networks respectively, so that the trained neural networks are converted into a plurality of transfer learning-like neural networks.

In an embodiment, the trained neural network group 2, the parameter optimization module 3 or the transfer learning module 4 can be disposed on the same or different electronic devices, wherein the electronic devices can be equipped with a processor. Types of electronic devices may include desktop computers, notebook computers, tablet computers, industrial computers, servers, cloud servers, smartphones, or other electronic products with processors, but are not limited thereto. In one embodiment, the trained neural network group 2, parameter optimization module 3 or transfer learning module 4 can be implemented through a computer program product. For example, the computer program product can have a plurality of instructions. , these instructions can cause the processor of the electronic device to perform special operations, thereby enabling the processor to implement various functions of the trained neural network group 2, parameter optimization module 3 or transfer learning module 4, but are not limited to this. In addition, in one embodiment, the computer program product may be stored in a non-transitory computer-readable medium, such as, but not limited to, the memory or storage of an electronic device. In addition, the computer program product can be stored in a network server (such as the memory of the network server or other storage device) for other users to download. For example, the computer program product can be made into an application software (application, APP). The form is sold and used on the Internet, but is not limited to this.

Next, each component will be described.

Figure 2 is a schematic diagram of the operation process of the trained neural network group 2 according to an embodiment of the present invention, and please refer to Figure 1 at the same time.

As shown in FIG. 2 , in one embodiment, the trained neural network group 2 may include a first type neural network 21 , a second type neural network 22 and a third type neural network 23 . The first type neural network 21, a second type neural network 22 and a third type neural network 23 can obtain the same type of processing parameter combination, and each output different target prediction values.

In one embodiment, the processing parameters in the processing parameter combination may include discharge pulse off time (off time, OFF), arc discharge pulse off time (arc off time, AFF), short circuit discharge pulse off time (short off time, SFF). ), discharge pulse duration (on time, ON), arc discharge pulse duration (arc on time, AN) or short circuit discharge pulse duration (short on time, SN), or any combination of the above processing parameters, and is not limited to this. In other embodiments, the types of processing parameters may also include standard voltage (SV), output voltage (OV), feedrate override (FR), and/or water flow (water flow, WL), without limitation.

In one embodiment, the first type of neural network 21 can be used to output a prediction value of machining speed or feed rate. For example, after a set of processing parameter combinations is input to the first type of neural network 21, the A type of neural network 21 can perform feature analysis on the set of processing parameter combinations and predict the processing feed speed corresponding to the set of processing parameter combinations. The “processing feed speed” here refers to the feed speed of the wire-cut electric discharge machine 10 at each time during the cutting process of the workpiece (that is, the object to be cut).

In one embodiment, the second type neural network 22 can be used to output a surface roughness prediction value. For example, after a set of processing parameter combinations is input to the second type neural network 22, the second type neural network Road 22 can perform feature analysis on this set of processing parameter combinations and predict the surface roughness corresponding to this set of processing parameter combinations. "Surface roughness" here refers to the surface roughness of the workpiece after cutting.

In one embodiment, the third type neural network 23 can be used to output a prediction value of machining accuracy. For example, after a set of machining parameter combinations is input to the third type neural network 23, the third type neural network 23 The characteristics of this set of processing parameter combinations can be analyzed, and the corresponding processing accuracy of this set of processing parameter combinations can be predicted. "Machining accuracy" here refers to the degree to which the three geometric parameters such as the actual size, shape and position of the cut surface of the workpiece conform to the expected ideal geometric parameters.

In one embodiment, the first type of neural network 21 , the second type of neural network 22 and the third type of neural network 23 are respectively trained through deep learning, thereby obtaining the ability to generate predicted target values based on a combination of processing parameters. . The training process of the first type of neural network 21, the second type of neural network 22 and the third type of neural network 23 will be explained in subsequent paragraphs (eg, Figure 5A to Figure 5C).

In one embodiment, the first type of neural network 21 , the second type of neural network 22 and the third type of neural network 23 can be developed using the Keras open source neural network library, and the version used is 2.6.0, and is not limited to this.

Next, the parameter optimization module 3 will be described. FIG. 3A is a schematic diagram of the operation process of the parameter optimization module 3 according to an embodiment of the present invention. Please refer to FIG. 1 and FIG. 2 at the same time.

As shown in Figure 3A, after the training of the first type neural network 21, the second type neural network 22 and the third type neural network 23 is completed, the parameter optimization module 3 can combine the processing parameters (such as OFF, AFF, SFF, ON, AN and SN) are input to the first type neural network 21, the second type neural network 22 and the third type neural network 23, and the genetic algorithm is executed, from the first type Find the best target prediction value among multiple target prediction values output by the neural network 21, the second type neural network 22 and the third type neural network 23 respectively, and deduce the best target prediction value from the best target prediction value. One or more processing parameter combinations corresponding to the target predicted value, and the processing parameter combination corresponding to the best target predicted value is set as the optimized value combination.

In one embodiment, each processing parameter in the processing parameter combination can be preset with a better value range, and the parameter optimization module 3 can generate a variety of processing parameter combinations based on the best value range of each processing parameter. Numeric combination. In one embodiment, the optimal value range of each processing parameter can be preset by the user and can be adjusted at any time. In one embodiment, the preferred numerical range may be predicted and provided by a person skilled in the art, but is not limited thereto.

In one embodiment, the genetic algorithm may be, for example, a second generation non-dominated sorting genetic algorithm II (NSGA-II), but is not limited thereto. Through NSGA-II, the parameter optimization module 3 can find multiple targets output by at least two of the first type neural network 21, the second type neural network 22, and the third type neural network 23. One or more Pareto fronts (Pareto front) among the predicted values, and the numerical combination of the processing parameter combination corresponding to the target predicted value belonging to the Pareto front is set as the optimal numerical combination. In one embodiment, the optimal value combination may be, for example, the target prediction value is a value combination that can produce a faster processing feed speed, the target prediction value is a value combination that can produce a lower surface roughness, and the target prediction value is a value combination that can produce a lower surface roughness. Produce a numerical combination with more accurate processing accuracy, or any combination of the above, and is not limited to this.

The details of the Pareto front are then explained in more detail. FIG. 3B is a schematic diagram of the calculation results of the genetic algorithm according to an embodiment of the present invention, and please refer to FIG. 3A at the same time. Among them, the example in Figure 3B is used to illustrate the process of obtaining the Pareto front corresponding to at least two target prediction values.

As shown in Figure 3B, after executing NSGA-II, the parameter optimization module 3 can generate a graph similar to that in which the horizontal axis of the graph is the first target prediction value OF1, such as the surface roughness prediction value, The vertical axis of the coordinate chart is the second target prediction value OF2, such as the processing feed speed prediction value, and the numerical combination of each group of processing parameter combinations can be calculated according to its corresponding first target prediction value and second target prediction value. are marked on the coordinate diagram. After that, the parameter optimization module 3 can find one or more numerical combinations from the numerical combinations in which at least one of the first target predicted value or the second target predicted value is better than other numerical combinations, and The found one or more value combinations are set as the first set of Pareto front combinations F1. When the first set of Pareto front combinations F1 is found, the parameter optimization module 3 then finds at least one of the first target prediction value or the second target prediction value from the remaining numerical combinations. one or more value combinations in other value combinations, and set the found one or more value combinations as the second set of Pare Support front edge combination F2. And so on until the preset number of Pareto fronts is found. Afterwards, the parameter optimization module 3 can set these found Pareto fronts as optimized value combinations corresponding to the processing parameter combinations of the first target predicted value and the second target predicted value. For example, the values of each processing parameter (such as OFF, AFF, SFF, ON, AN and SN) corresponding to the first set of Pareto front combinations F1 (such as a value of OFF, a value of AFF, SFF A value of , a value of ON, a value of AN and a value of SN) is one of the optimal value combinations. Each processing parameter corresponding to the second set of Pareto front combination F2 (such as OFF, AFF, SFF, ON, AN, and SN) (such as another value of OFF, another value of AFF, another value of SFF, another value of ON, another value of AN, and another value of SN) That is another optimized numerical combination. Those skilled in the art can similarly deduce the situation when Pareto fronts corresponding to more types of target prediction values are obtained.

In this way, the optimal combination of processing parameters can be found.

Next, transfer learning module 4 will be described. FIG. 4 is a schematic diagram of the operation process of the transfer learning module 4 according to an embodiment of the present invention. Please refer to FIG. 1 and FIG. 2 at the same time.

As shown in Figure 4, the transfer learning module 4 can perform transfer learning on the trained first type neural network 21, the second type neural network 22 and the third type neural network 23 respectively, so that the first type neural network The network 21, the second type neural network 22 and the third type neural network 23 can be respectively converted into a fourth type neural network 24 suitable for wire cutting electric discharge machining machines of other specifications or suitable for predicting other target prediction values. , a type 5 neural network 25 and a type 6 neural network 26 .

In one embodiment, the transfer learning module 4 performs transfer learning on the first type of neural network 21 , the second type of neural network 22 and the third type of neural network 23 in a weight-freezing manner, but is not limited to this. The details of transfer learning will be explained in subsequent paragraphs.

The foregoing paragraphs have explained the details of the trained neural network group 2, parameter optimization module 3 and transfer learning module 4. Next, the first type neural network 21, the second type neural network 22 and the second type will be described. The training process of three types of neural networks 23.

FIG. 5A is a schematic diagram of the training model 210 of the first type neural network 21 (that is, the first type neural network 21 before training) according to an embodiment of the present invention. Please refer to FIG. 1 and FIG. 2 at the same time. As shown in Figure 5A, the training model 210 of the first type of neural network 21 is a deep neural network model, which may include an input layer 211, a plurality of hidden layers 212 and a loss function layer 213, wherein each hidden layer 212 may include a plurality of neurons 214.

In one embodiment, the input layer 211 can be used to input six static features, such as OFF, AFF, SFF, ON, AN, SN, etc., but is not limited thereto. In one embodiment, the training model 210 may include 18 hidden layers 212, and each hidden layer may include 13 neurons 214, but is not limited thereto. In one embodiment, the loss function of the loss function layer 213 may be mean-square error (MSE), but is not limited thereto. In one embodiment, the activation function of the training model 210 may be a rectified linear unit function (Rectified Linear Unit, ReLU), but is not limited thereto. In one embodiment, the training module 210 may include an optimizer, where the optimizer may use the Nadam optimizer, but is not limited thereto. In one embodiment, the learning rate of the training model 210 may be set to 0.00032, but is not limited thereto. In one embodiment, the batch size of the training model 210 may be set to 8, but is not limited thereto. In one embodiment, the training number (Epoch) of the training model 210 can be set to 1000, and the endurance number (patience) can be set to 20 using early stopping method, but is not limited thereto. It should be noted that the above-mentioned various setting parameters for training the model 210 are examples and are not limiting.

In one embodiment, when starting training, multiple value combinations of processing parameter combinations (such as multiple value combinations of processing parameters OFF, AFF, SFF, ON, AN and SN) and each value combination The corresponding target value information (such as the actual processing feed speed corresponding to each numerical combination of the processing parameters OFF, AFF, SFF, ON, AN and SN) can be input into the training model 210, and some of the numerical combinations It can be used as training data, and part of the numerical combination can be used as test data. In one embodiment, the number of value combinations is greater than or equal to 50 and is routed thereto. In one embodiment, the number of numerical combinations is greater than or equal to 75, and is not limited thereto. In one embodiment, the number of numerical combinations may be between 80 and 100 (80≦number≦100), and is not limited thereto. In one embodiment, the value of OFF in the processing parameter combination can be between 5 microseconds (μs) and 15 μs (that is, 5 μs≦OFF≦15 μs), and is not limited thereto. In one embodiment, the value of AFF in the processing parameter combination can be between 5 μs and 15 μs (that is, 5 μs≦AFF≦15 μs), and is not limited thereto. In one embodiment, the value of SFF in the processing parameter combination can be between 5 μs and 15 μs (that is, 5 μs≦SFF≦15 μs), and is not limited thereto. In one embodiment, the value of ON in the processing parameter combination can be between 0.5 μs and 1 μs (that is, 0.5 μs≦ON≦1 μs), and is not limited thereto. In one embodiment, the value of AN in the processing parameter combination can be between 0.5 μs and 1 μs (that is, 0.5 μs≦AN≦1 μs), and is not limited thereto. In one embodiment, the value of SN in the processing parameter combination can be between 0.1 μs and 0.5 μs (that is, 0.1 μs≦SN≦0.5 μs), and is not limited thereto.

After the training model 210 obtains the training data and the target value information corresponding to each training data, the feature analysis path can be constructed through the hidden layer 212 and the loss function layer 213 (that is, the neurons 214 of each hidden layer 212 can be completed). weight setting), and then complete the training. The trained training model 210 can form the trained first type neural network 21. In one embodiment, the MSE of the first type of neural network 21 after training is approximately 0.0115 (mm/min) ² , and the mean absolute percentage error (MAPE) between the predicted results and the actual measurement results can be It is about 1.87%, which has good accuracy.

From this, the training process of the first type neural network 21 can be understood.

FIG. 5B is a schematic diagram of the training model 220 of the second type neural network 22 (that is, the second type neural network 22 before training) according to an embodiment of the present invention. Please refer to FIG. 1 and FIG. 2 at the same time. As shown in FIG. 5B , the training model 220 of the second type of neural network 22 is a deep neural network model, which may include an input layer 221 , a plurality of hidden layers 222 and a loss function layer 223 , where each hidden layer 222 may include a plurality of neurons 224.

In one embodiment, the input layer 221 can be used to input six static features, such as OFF, AFF, SFF, ON, AN, SN, etc., but is not limited thereto. In one embodiment, the training model 220 may include 20 hidden layers 222, and each hidden layer 222 may include 20 neurons 224, but is not limited thereto. In one embodiment, the loss function of the loss function layer 223 may be MSE, but is not limited thereto. In one embodiment, the activation function of the training model 220 may be ReLU, but is not limited thereto. In one embodiment, the optimizer of the training module 220 may be a Nadam optimizer, but is not limited thereto. In one embodiment, the learning rate of the training model 220 may be set to 0.003, but is not limited thereto. In one embodiment, the batch size of the training model 220 may be set to 5, but is not limited thereto. In one embodiment, the Epoch of the training model 220 can be set to 1000, and early stopping can be used to set the patience to 20, but is not limited thereto. It should be noted that the above-mentioned various setting parameters for training the model 220 are examples and are not limiting.

The training model 220 of the second type neural network 22 can adopt the same numerical combination of processing parameter combinations as the training model 210 of the first type neural network 21, so this part will not be described in detail.

After the training model 220 obtains the training data and the target value information corresponding to each training data, the feature analysis path can be constructed through the hidden layer 222 and the loss function layer 223 (that is, the neurons 224 of each hidden layer 222 can be completed). weight setting), and then complete the training. The trained training model 220 can form the trained second type neural network 22. In one embodiment, the MSE of the second type neural network 22 after training is about 0.0063 (mm/min) ² , and the MAPE between the predicted results and the actual measurement results is about 1.72%, which has good accuracy.

Through this, the training process of the second type neural network 22 can be understood.

FIG. 5C is a schematic diagram of the training model 230 of the third type neural network 23 (that is, the third type neural network 23 before training) according to an embodiment of the present invention. Please refer to FIG. 1 and FIG. 2 at the same time. As shown in Figure 5C, the training model 230 of the third type of neural network 23 is a deep neural network model, which may include an input layer 231, a plurality of hidden layers 232 and a loss function layer 233, where each hidden layer 232 may include a plurality of neurons 234.

In one embodiment, the input layer 231 can be used to input six static features, such as OFF, AFF, SFF, ON, AN, SN, etc., but is not limited thereto. In one embodiment, the training model 230 may include 26 hidden layers 232, and each hidden layer 232 may include 13 neurons, but is not limited thereto. In one embodiment, the loss function of the loss function layer 233 may be MSE, but is not limited thereto. In one embodiment, the activation function of the training model 230 may be ReLU, but is not limited thereto. In one embodiment, the optimizer of the training module 230 may be a Nadam optimizer, but is not limited thereto. In one embodiment, the learning rate of the training model 230 may be set to 0.00035, but is not limited thereto. In one embodiment, the batch size of the training model 230 may be set to 4, but is not limited thereto. In one embodiment, the Epoch of the training model 230 can be set to 1000, and early stopping can be used to set the patience to 20, but is not limited thereto. It should be noted that the above-mentioned various setting parameters for training the model 230 are examples and are not limiting.

The training model 230 of the third type of neural network 23 can adopt the same numerical combination of processing parameter combinations as the training model 210 of the first type of neural network 21, so this part will not be described in detail.

After the training model 230 obtains the training data and the target value information corresponding to each training data, it can construct a feature analysis path through the hidden layer 232 and the loss function layer 233 (that is, it can be completed The weight setting of the neuron 234 of each hidden layer 232), thereby completing the training. The trained training model 230 can form the trained third type neural network 23. In one embodiment, the mean absolute error (MAE) between the prediction results of the trained third type neural network 23 and the actual measurement results can be approximately 1.95%, which has good accuracy.

Through this, the training process of the third type neural network 23 can be understood.

After the training of the first type neural network 21 , the second type neural network 22 and the third type neural network 23 is completed, the processing feed speed prediction value and surface roughness can be output according to the input processing parameter combination. Predicted value and machining accuracy predicted value, and the parameter optimization module 3 can also use the first type of neural network 21, the second type of neural network 22 and the third type of neural network 23 and execute the genetic algorithm to find out Optimized processing parameter combination.

In one embodiment, the parameter optimization module 3 uses the genetic algorithm to find the processing parameter combination, and the average MAPE between the predicted processing feed speed value and the actual measured result is about 3.44, and the predicted surface roughness value and the actual measured result are about 3.44. The average MAPE of the results is about 4.46, and the average MAE of the predicted machining accuracy value and the measured results is 1.99, both of which have good accuracy.

In addition, after the training of the first type neural network 21, the second type neural network 22 and the third type neural network 23 is completed, the transfer learning module 4 can also train the first type neural network 21, the second type neural network 21 and the second type neural network 23 respectively. The class neural network 22 and the third class neural network 23 use weight freezing to perform transfer learning. Next, the process of transfer learning will be explained. Please refer to Figure 4 to Figure 5C at the same time.

In one embodiment, the transfer learning module 4 freezes the weights of the neurons 214 other than the neurons 214 of the last hidden layer 212 of the first type neural network 21 (for example, the neurons of the first 17 hidden layers 212 The weights of 214 are frozen) and retrained using the training data to adjust only the weights of neurons 214 of the last hidden layer 212. In one embodiment, when using about 40 sets of training data, During training, the MAPE between the target prediction value and the actual measurement result of the fourth type neural network 24 formed after the first type neural network 21 performs transfer learning can be approximately 1.97%, which has good accuracy.

In one embodiment, the transfer learning module 4 freezes the weights of neurons 224 other than the neurons 224 of the last hidden layer 222 of the second type neural network 22 (for example, the neurons of the first 19 hidden layers 222 The weights of 224 are frozen) and retrained using the training data to adjust only the weights of neurons 224 of the last hidden layer 222. In one embodiment, when about 40 sets of training data are used for training, the MAPE of the target predicted value of the fifth type neural network 25 formed after the second type neural network 22 performs transfer learning and the actual measurement result can be approximately is 2.58%, which has good accuracy.

In one embodiment, the transfer learning module 4 freezes the weights of the neurons 234 other than the neurons 234 of the last hidden layer 232 of the third type neural network 23 (for example, the neurons of the first 25 hidden layers 232 The weights of 234 are frozen) and retrained using the training data to adjust only the weights of neurons 234 of the last hidden layer 232. In one embodiment, when about 40 sets of training data are used for training, the MAE of the target prediction value of the sixth type neural network 26 formed after the third type neural network 23 performs transfer learning and the actual measurement result can be approximately is 2.17%, which has good accuracy.

In this way, a small amount of training data can be used to transfer the trained neural network group 2 between different machines to expand the applicable scope of the system 1.

Next, the operation process of the system 1 of the present invention is integrated. FIG. 6 is a step flow chart of a multi-objective parameter optimization method according to an embodiment of the present invention. The multi-objective parameter optimization method is executed through the system 1 .

First, step S61 is executed, and the architectures of the training model 210 of the first type neural network 21, the training model 220 of the second type neural network 22, and the training model 230 of the third type neural network 23 are set. That is, the structure as shown in Figures 5A~5C is formed. Then step S62 is executed, and the model for training 210~230 obtain training data respectively. Then step S63 is executed, and the training models 210 to 230 are trained using the training data to form a trained neural network respectively. Thereby, the training of the training model 210 of the first type neural network 21, the second type neural network 22 and the third type neural network 23 can be completed.

After step S63 is completed, step S64 can be executed. The parameter optimization module 3 inputs a plurality of better value combinations of the processing parameter combinations into the trained first type neural network 21 and the second type neural network respectively. 22 and the third type of neural network 23. After that, step S65 is executed, and the parameter optimization module 3 executes NSGA-II to find a prediction value from the target prediction values of the first type neural network 21, the second type neural network 22, and the third type neural network 23. or multiple Pareto fronts. Then step S66 is executed, and the parameter optimization module 3 sets the numerical combination corresponding to the Pareto fronts as the optimized numerical combination of the processing parameter combination. Thereby, the system 1 of the present invention can automatically find the optimal parameter combination for multiple objectives.

In addition, when step S63 is completed, step S67 may also be executed, and the transfer learning module 4 freezes the weights of the first type of neural network 21, the second type of neural network 22, and the third type of neural network 23 respectively. Then step S68 is executed, and the transfer learning module 4 retrains the first type of neural network 21 , the second type of neural network 22 and the third type of neural network 23 with frozen weights. Then step S69 is executed. The first type of neural network 21 forms a fourth type of neural network 24 that is suitable for different machines or has different prediction capabilities. The second type of neural network 22 forms a fourth type of neural network that is suitable for different machines or has different prediction capabilities. The fifth type of neural network 25 has different capabilities, and the third type of neural network 23 forms a sixth type of neural network 26 that is suitable for different machines or has different prediction capabilities. With this, transfer learning can be accomplished.

Accordingly, the trained neural network group 2 established by the multi-objective parameter optimization system 1 of the present invention can accurately predict multiple target prediction values of the wire-cut electric discharge machine 10 . In addition, the parameter optimization module 3 of the multi-objective parameter optimization system 1 can execute a genetic algorithm to obtain the optimal processing parameter combination using the trained neural network group 2, thereby saving a lot of money. time cost and labor cost Book. In addition, the trained neural network group 2 of the multi-objective parameter optimization system 1 of the present invention can be applied to other machines through transfer learning, which can improve the applicability and reduce redevelopment expenses and costs.

Although the present invention has been illustrated by the above embodiments, it is understood that many modifications and variations are possible according to the spirit of the present invention and the patentable scope claimed by the present invention.

1: Multi-objective parameter optimization system

2: Neural network group after training

21: Type 1 Neural Networks

22: Type II Neural Network

23: The third type of neural network

3: Parameter optimization module

Claims (9)

A multi-objective parameter optimization system used to provide multi-objective optimized processing parameters of a linear cutting electric discharge machining machine, including: a plurality of trained neural networks, wherein each trained neural network is based on a processing parameter combined to each output a target prediction value; and a parameter optimization module that executes a genetic algorithm to utilize the target predictions output by the trained neural network according to a plurality of numerical combinations of the processing parameter combination. values to find at least one optimized value combination of the processing parameter combination; wherein the genetic algorithm is to find out the target prediction values output by the trained neural network group based on the combination of values. At least one Pareto front (Pareto front), and the set of value combinations corresponding to the at least one Pareto front are set to the at least one optimized value combination of the processing parameter combination. The multi-objective parameter optimization system as described in claim 1, wherein the processing parameters include discharge pulse stop time, arc discharge pulse stop time, short circuit discharge pulse stop time, discharge pulse duration, arc discharge pulse duration or short circuit Discharge pulse duration, or any combination of the above. The multi-objective parameter optimization system as claimed in claim 1, wherein the trained neural network group includes a first trained neural network, a second trained neural network and/or a A third trained neural network, wherein the target predicted value of the first trained neural network is a processing feed speed predicted value, and the target predicted value of the second trained neural network is surface roughness. degree prediction value, and the target prediction value of the third trained neural network is the workpiece accuracy prediction value. The multi-objective parameter optimization system as described in claim 1 further includes a transfer learning module that performs transfer learning on each trained neural network in a weight-freezing manner. A multi-objective parameter optimization method, executed by a multi-objective parameter optimization system, is used to provide multi-objective optimized processing parameters of a line cutting electric discharge machine, wherein the multi-objective parameter optimization system includes a plurality of training A post-training neural network and a parameter optimization module, and each trained neural network outputs a target prediction value according to a combination of processing parameters, wherein the method includes the step of: optimizing the model by the parameter A group that executes a genetic algorithm to use the trained neural networks to find at least one optimized numerical combination of the processing parameter combination based on the target prediction values output by the multiple numerical combinations of the processing parameter combination. ; wherein the genetic algorithm is to find at least one Pareto front among the target prediction values output by the trained neural network group based on the combination of values, and add the at least one Pareto front The set of numerical combinations corresponding to the leading edge is set to the at least one optimized numerical combination of the processing parameter combination. The multi-objective parameter optimization method as described in claim 5, wherein the processing parameters include discharge pulse stop time, arc discharge pulse stop time, short circuit discharge pulse stop time, discharge pulse duration, arc discharge pulse duration or short circuit Discharge pulse duration, or any combination of the above. The multi-objective parameter optimization method as described in claim 5, wherein the trained neural network group includes a first trained neural network, a second trained neural network or a third trained neural network. The trained neural network, or any combination of the above, wherein the target predicted value of the first trained neural network is the processing feed speed predicted value, and the target predicted value of the second trained neural network is the surface roughness prediction value, and the target prediction value of the third trained neural network is the workpiece accuracy prediction value. The multi-objective parameter optimization method described in claim 5 further includes the step of freezing the weight of each trained neural network through a transfer learning module of the multi-objective parameter optimization system. Perform transfer learning. A computer program product, stored in a non-transitory computer-readable medium, used to operate a multi-objective parameter optimization system to provide multi-objective optimization processing parameters of a linear cutting electric discharge machining machine, wherein the The multi-objective parameter optimization system includes a plurality of trained neural networks and a parameter optimization module, and each trained neural network outputs a target prediction value according to a combination of processing parameters, wherein the computer The program product includes: an instruction that causes the parameter optimization module to execute a genetic algorithm to use the trained neural network to find the target predicted values output based on the multiple numerical combinations of the processing parameter combination. At least one optimized numerical combination of the processing parameter combination is obtained; wherein the genetic algorithm is to find at least one of the target prediction values output by the trained neural network group according to the numerical combination. Pareto front, and the set of value combinations corresponding to the at least one Pareto front is set to the at least one optimized value combination of the processing parameter combination.