A SLM process parameter analysis method based on machine learning

Abstract

The present invention relates to the field of parameter optimization, and in particular to a method for analyzing SLM process parameters based on machine learning. A method for analyzing SLM process parameters based on machine learning comprises: constructing a workpiece image feature vector; constructing a workpiece prediction image; and outputting an SLM process optimization parameter set. The present invention performs a temporal analysis of changes in SLM process control parameters and corresponding changes in workpiece images. While learning the impact of changes in SLM process control parameters on the workpiece, the present invention predicts changes in the workpiece under the premise that the SLM process control parameters remain unchanged. Based on the predicted changes in the workpiece, defects in the workpiece are analyzed, and based on the analyzed defects, subsequent SLM process control parameters are optimized to avoid the occurrence of defects, thereby improving the quality of the workpiece.

Description

Technical Field

[0001] The present invention relates to the field of parameter optimization, and in particular to a SLM process parameter analysis method based on machine learning. Background Art

[0002] Selective laser melting (SLM), as an advanced additive manufacturing technology, is widely used in aerospace, medical, automotive and other fields. However, the selection and control of process parameters in the SLM process are crucial and directly affect the quality and performance of the final product. Although existing technologies have conducted research on static parameter settings, in dynamic processes, SLM systems usually rely on fixed parameters set by humans and lack the ability to adaptively adjust. This fixed parameter setting method often cannot cope with the uncertainties brought about by factors such as material properties, environmental changes and equipment status, resulting in frequent defects in the production process, such as pores, unfused parts and warping. This not only increases the subsequent detection and repair costs, but also limits the application potential of SLM technology in the manufacture of complex workpieces. Summary of the Invention

[0003] The present invention performs a temporal analysis of the changes in SLM process control parameters and the corresponding workpiece image changes. While studying the impact of the changes in SLM process control parameters on the workpiece, it predicts the changes in the workpiece under the premise that the SLM process control parameters remain unchanged. Based on the predicted workpiece changes, the defects of the workpiece are analyzed, and the subsequent SLM process control parameters are optimized based on the analyzed defects to avoid the occurrence of defects and thereby improve the quality of the workpiece.

[0004] A method for analyzing SLM process parameters based on machine learning, comprising:

[0005] Obtain the SLM process parameter set and workpiece monitoring image at the current monitoring time point, send the workpiece monitoring image to the image feature extraction model for processing, and output the workpiece image feature vector;

[0006] The SLM process parameter set and the workpiece image feature vector at the current monitoring time point are concatenated to form the workpiece timing prediction data, and the workpiece timing prediction data at the current monitoring time point and the previous N-1 workpiece timing prediction data are combined to form a workpiece timing prediction data set; the workpiece timing prediction data set is fed into the workpiece image prediction model for processing, and a workpiece prediction image is output;

[0007] The workpiece prediction image and the SLM process parameter set at the current monitoring time point are fed into the parameter adjustment model for processing, and the SLM process optimization parameter set is output;

[0008] The parameter adjustment model includes a standard image construction layer, a difference image construction layer, a difference image enhancement layer, a difference feature extraction layer, a first feature splicing layer and an SLM process optimization parameter set output layer, wherein the standard image construction layer is used to construct a corresponding standard image based on the workpiece prediction image; the difference image construction layer is used to construct a difference image based on the standard image and the standard image; the difference image enhancement layer is used to perform feature enhancement on the workpiece prediction image through the difference image to construct an enhanced defective workpiece image; the difference feature extraction layer is used to perform feature extraction on the enhanced defective workpiece image to construct a difference feature vector; the first feature splicing layer is used to splice the SLM process parameter set at the current monitoring time point with the difference feature vector to construct a process parameter optimization analysis data set; the SLM process optimization parameter set output layer is used to process the process parameter optimization analysis data set and output M process optimization parameter set.

[0009] As a preferred aspect of the present invention, the workpiece prediction image and the SLM process parameter set at the current monitoring time point are fed into a parameter adjustment model for processing, and an SLM process optimization parameter set is output. Specifically, the steps include:

[0010] Send the workpiece prediction image to the standard image construction layer for processing to construct a standard image;

[0011] In the difference image construction layer, the standard image and the workpiece prediction image are subtracted and then normalized to construct a difference image;

[0012] In the difference image enhancement layer, the artifact prediction image is multiplied with the key weight matrix and the value weight matrix respectively to construct the artifact key feature map K and the artifact value feature map V. Then, the difference image is multiplied with the query weight matrix to construct the difference query feature map Q. The attention weight matrix ATT = (QK T / (D) 0.5 ), and finally multiply the attention weight matrix ATT with the workpiece value feature map V to construct an enhanced defect workpiece image;

[0013] The enhanced defect workpiece image is sent to the difference feature extraction layer for processing to construct a difference feature vector;

[0014] In the first feature splicing layer, the SLM process parameter set at the current monitoring time point is spliced ​​with the difference feature vector to construct a process parameter optimization analysis data set;

[0015] The process parameter optimization analysis data set is sent to the SLM process optimization parameter set output layer for processing, which needs to pass through several hidden layers to output the SLM process optimization parameter set.

[0016] As a preferred aspect of the present invention, the workpiece image prediction model includes a timing analysis layer, a second feature stitching layer and a workpiece prediction image construction layer, wherein the timing analysis layer is used to perform timing analysis on the workpiece timing prediction data set to construct an SLM process timing analysis vector; the second feature stitching layer is used to stitch the SLM process parameter set at the current monitoring time point with the SLM process timing analysis vector to construct a workpiece prediction analysis vector; the workpiece prediction image construction layer is used to process the workpiece prediction analysis vector to construct a workpiece prediction image.

[0017] As a preferred aspect of the present invention, training the workpiece image prediction model specifically includes the following steps:

[0018] Obtain a workpiece image prediction training sample, which includes N+1 workpiece time series prediction data. Annotate the workpiece image prediction training sample with the workpiece monitoring image corresponding to the workpiece image feature vector of the last workpiece time series prediction data in the workpiece image prediction training sample. Form all annotated workpiece image prediction training samples into a workpiece image prediction training set. Then, send the workpiece image prediction training set to the parameter-initialized workpiece image prediction model for training. Use the first N workpiece time series prediction data in the workpiece image prediction training sample as the input of the time series analysis layer, use the SLM process parameter set of the last workpiece time series prediction data in the workpiece image prediction training sample as the input of the second feature splicing layer, calculate the workpiece image prediction loss value, and determine whether the workpiece image prediction loss value is within a first preset range. If the workpiece image prediction loss value is within the first preset range, output the trained workpiece image prediction model; otherwise, continue to train the workpiece image prediction model through the workpiece image prediction training set.

[0019] As a preferred aspect of the present invention, training the parameter adjustment model specifically includes the following steps:

[0020] Obtain several parameter adjustment training samples, each of which includes N workpiece timing prediction data, and label the parameter adjustment training samples using the process parameter optimization analysis data set; and form a parameter adjustment training set from all labeled parameter adjustment training samples.

[0021] Acquire a number of image construction training samples, where the image construction training samples include defective workpiece monitoring images and corresponding standard images, form all the image construction training samples into an image construction training set, and then feed the image construction training set into a parameter-initialized standard image construction layer for training, using the workpiece monitoring images in the image construction training samples as input and the standard images in the image construction training samples as target outputs, calculate an image construction loss value, and determine whether the image construction loss value is within a second preset range. If the image construction loss value is within the second preset range, output the trained standard image construction layer; otherwise, continue training the workpiece standard image construction layer using the image construction training set;

[0022] The parameters of the fixed standard image construction layer remain unchanged, the trained workpiece image prediction model and the parameter adjustment model are connected to form a fusion model, the parameter adjustment training set is sent to the fusion model for training, the fusion loss value is calculated, and it is determined whether the fusion loss value is within the third preset range. If the fusion loss value is within the third preset range, the trained parameter adjustment model is output; otherwise, the fusion model is continued to be trained using the parameter adjustment training set.

[0023] As a preferred aspect of the present invention, it also includes simulation optimization of parameters in the output layer of the SLM process optimization parameter set, specifically including the following steps:

[0024] Construct several network parameter simulation individuals, and form a population set of all network parameter simulation individuals, and set the maximum number of iterations;

[0025] Calculate the fitness of the network parameters and simulate the corresponding individuals;

[0026] Based on the fitness of the network parameters simulating the corresponding individuals, the population set is iteratively updated through the group optimization algorithm;

[0027] Until the number of iterations reaches the maximum number of iterations, the network parameter simulation individual with the largest fitness is output as the parameter in the output layer of the SLM process optimization parameter set.

[0028] As a preferred aspect of the present invention, the fitness corresponding to the network parameter simulation individual is calculated. The specific calculation method is as follows: the output layer of the SLM process optimization parameter set in the parameter adjustment model is set through the network parameter simulation individual, and then the set parameter adjustment model is tested by constructing training samples through several images, and the obtained accuracy is used as the fitness corresponding to the network parameter simulation individual.

[0029] As a preferred aspect of the present invention, the standard image construction layer is established based on the U-net model, and the artifact prediction image construction layer is set based on the decoder in the U-net model.

[0030] The present invention has the following advantages:

[0031] The present invention performs a temporal analysis of the changes in SLM process control parameters and the corresponding workpiece image changes. While studying the impact of the changes in SLM process control parameters on the workpiece, it predicts the changes in the workpiece under the premise that the SLM process control parameters remain unchanged. Based on the predicted workpiece changes, the defects of the workpiece are analyzed, and the subsequent SLM process control parameters are optimized based on the analyzed defects to avoid the occurrence of defects and thereby improve the quality of the workpiece. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 Schematic diagram of the process flow of the SLM process parameter analysis method based on machine learning adopted in an embodiment of the present invention.

[0033] Figure 2 This is a schematic diagram of the structure of the workpiece image prediction model used in an embodiment of the present invention.

[0034] Figure 3 This is a structural diagram of the parameter adjustment model used in an embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to make the technical solutions and advantages of the embodiments of the present application more clearly understood, the exemplary embodiments of the present application are further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, and are not an exhaustive list of all the embodiments. It should be noted that the embodiments and features of the embodiments in the present application can be combined with each other without conflicting changes.

[0036] Example 1, a SLM process parameter analysis method based on machine learning, comprising:

[0037] The SLM (Selective Laser Melting) process parameter set and workpiece monitoring image at the current monitoring time point are obtained. The SLM process parameter set includes SLM process parameters such as laser power and scanning speed, which can be obtained through a series of sensors such as laser power meters. These SLM process parameters can affect the quality of the formed workpiece. The workpiece monitoring image is a photographic image of the workpiece, which can be obtained through an industrial camera. The workpiece monitoring image can reflect the quality of the workpiece. The workpiece monitoring image is sent to the image feature extraction model for processing, and the workpiece image feature vector is output. The workpiece image feature vector can reflect the current state of the workpiece.

[0038] The SLM process parameter set at the current monitoring time point and the workpiece image feature vector are spliced ​​together to form the workpiece timing prediction data, and the workpiece timing prediction data at the current monitoring time point and the previous N-1 workpiece timing prediction data are combined to form a workpiece timing prediction data set; the workpiece timing prediction data set is sent to the workpiece image prediction model for processing, and a workpiece prediction image is output; the workpiece image prediction model performs a temporal analysis on the workpiece timing prediction data set, and can learn the changes in workpiece parameters, and then learn the impact of the changes in workpiece parameters on the workpiece, thereby realizing the prediction of the workpiece image. The workpiece prediction image can reflect the subsequent workpiece quality, and thus provide a reference for the adjustment of the SLM process parameter set;

[0039] The workpiece prediction image and the SLM process parameter set at the current monitoring time point are fed into the parameter adjustment model for processing, and the SLM process optimization parameter set is output. The SLM process optimization parameter set includes various adjusted SLM process parameters. The SLM process optimization parameter set can be used to control the equipment that performs the SLM process, thereby achieving the effect of improving the quality of the workpiece;

[0040] For example, when the laser power is 75W and the scanning speed is 415mm / s, the grain structure of the bottom and side areas of the workpiece is relatively ordered and dense, showing a higher yield strength (645MPa) and greater ductility (53%). However, if the power and speed do not match (such as the power is too low or the speed is too fast), the molten pool will be too shallow, resulting in unfused areas in the bottom and middle areas of the workpiece, which will eventually affect the overall quality. When the input energy density (the combination of laser power and scanning speed) is too low (such as less than 70J / mm 3 ), the upper surface of the workpiece is prone to balling, affecting the surface quality. When the energy density is too high, the molten pool depth increases, which may cause keyhole phenomenon in the deep area and produce porosity defects. Therefore, by predicting the laser power and scanning speed in advance, it is possible to predict the fusion of the bottom and middle layers of the workpiece and adjust the LM process parameters in advance.

[0041] See also Figure 3, the parameter adjustment model includes a standard image construction layer, a difference image construction layer, a difference image enhancement layer, a difference feature extraction layer, a first feature stitching layer and an SLM process optimization parameter set output layer, wherein the standard image construction layer is established based on the U-net model, and is used to construct a corresponding standard image based on the workpiece prediction image. The standard image is a predicted image of a workpiece without defects, which can be used to determine the location and degree of defects. The defects here include pore defects, crack defects and deformation defects. In the SLM process, the laser melts the powder material layer by layer. As the number of layers increases, the heat inside the workpiece continues to accumulate, especially in workpieces with large sizes or complex geometric structures. This heat accumulation will cause the molten pool temperature to rise, which in turn affects the cooling rate and grain growth of the material, thereby causing these defects to occur; the difference image construction layer is used to construct the corresponding standard image based on the standard image and the standard image Construct a difference image, which can reflect the location and extent of the defect; the difference image enhancement layer is used to enhance the features of the workpiece prediction image through the difference image to construct an enhanced defect workpiece image, which can highlight the defect features in the workpiece prediction image; the difference feature extraction layer is established based on a pre-trained convolutional neural network. In this embodiment, the VGG16 model is used to extract features from the enhanced defect workpiece image to construct a difference feature vector, which can characterize the location and extent of the defect; the first feature splicing layer is used to splice the SLM process parameter set at the current monitoring time point with the difference feature vector to construct a process parameter optimization analysis data set; the SLM process optimization parameter set output layer is established based on a BP neural network to process the process parameter optimization analysis data set and output M process optimization parameter sets;

[0042] VGG16 is a deep convolutional neural network model proposed by the Computer Vision Group of Oxford University and is widely used in image classification tasks. The model consists of 16 layers, including 13 convolutional layers and 3 fully connected layers. In this embodiment, the output of the second-to-last fully connected layer is selected as the difference feature vector. The design concept is to improve the feature extraction capability by increasing the depth of the network. Each convolution layer uses a 3x3 convolution kernel and introduces nonlinearity in combination with the ReLU activation function. The size of the feature map is gradually reduced through the maximum pooling operation to reduce the computational complexity. VGG16 has a simple structure and efficient parameter sharing, so it uses the same convolution kernel size in multiple layers. In particular, after pre-training on the ImageNet dataset, VGG16 exhibits good transfer learning capabilities, and is therefore often used as the basis for feature extraction and model fine-tuning.

[0043] This application performs a time-series analysis on the changes in SLM process control parameters and the corresponding workpiece image changes. While learning the impact of changes in SLM process control parameters on the workpiece, it predicts the changes in the workpiece under the premise that the SLM process control parameters remain unchanged. Based on the predicted workpiece changes, the defects of the workpiece are analyzed, and based on the analyzed defects, the subsequent SLM process control parameters are optimized to avoid the occurrence of defects and thus improve the quality of the workpiece.

[0044] The workpiece prediction image and the SLM process parameter set at the current monitoring time point are fed into the parameter adjustment model for processing, and the SLM process optimization parameter set is output. Specifically, the steps include:

[0045] The workpiece prediction image is sent to the standard image construction layer for processing to construct a standard image. It should be noted that the standard image construction layer is established with reference to the U-net model, including an encoder and a decoder, which can achieve the function of image reconstruction;

[0046] In the difference image construction layer, the standard image and the workpiece prediction image are subtracted and then normalized to construct a difference image. After the normalization operation, the position of the difference image that is not zero indicates that the subsequent workpiece will have defects;

[0047] In the difference image enhancement layer, the artifact prediction image is multiplied with the key weight matrix and the value weight matrix respectively to construct the artifact key feature map K and the artifact value feature map V. Then, the difference image is multiplied with the query weight matrix to construct the difference query feature map Q. The attention weight matrix ATT = (QK T / (D) 0.5 ), and finally multiply the attention weight matrix ATT with the workpiece value feature map V to construct the enhanced defect workpiece image; the query weight matrix, key weight matrix, and value weight matrix are all set based on the self-attention mechanism in the Transformer model;

[0048] The enhanced defect workpiece image is sent to the difference feature extraction layer for processing to construct a difference feature vector. It should be noted that the difference feature extraction layer performs operations such as convolution, pooling, and full connection to perform image feature extraction operations;

[0049] In the first feature splicing layer, the SLM process parameter set at the current monitoring time point is spliced ​​with the difference feature vector to construct a process parameter optimization analysis data set;

[0050] The process parameter optimization analysis data set is sent to the SLM process optimization parameter set output layer for processing, which needs to pass through several hidden layers to output the SLM process optimization parameter set.

[0051] See also Figure 2 The workpiece image prediction model includes a timing analysis layer, a second feature splicing layer and a workpiece prediction image construction layer, wherein the timing analysis layer is established based on the Transformer model, and is used to perform timing analysis on the workpiece timing prediction data set to construct an SLM process timing analysis vector. It should be noted that the SLM process timing analysis vector reflects the impact of workpiece parameter changes on the workpiece; the second feature splicing layer is used to splice the SLM process parameter set at the current monitoring time point with the SLM process timing analysis vector to construct a workpiece prediction analysis vector. Splicing the SLM process parameter set at the current monitoring time point with the SLM process timing analysis vector represents that the subsequent SLM control parameters remain unchanged; the workpiece prediction image construction layer is used to process the workpiece prediction analysis vector to construct a workpiece prediction image. The workpiece prediction image construction layer is set based on the decoder in the U-net model for image reconstruction.

[0052] Training the workpiece image prediction model includes the following steps:

[0053] Obtain workpiece image prediction training samples, which include N+1 workpiece time series prediction data. These workpiece time series prediction data are all obtained during the actual SLM process. The workpiece image prediction training samples are labeled with the workpiece monitoring image corresponding to the workpiece image feature vector of the last workpiece time series prediction data in the workpiece image prediction training samples. All labeled workpiece image prediction training samples are composed into a workpiece image prediction training set. The workpiece image prediction training set is then sent to the parameter-initialized workpiece image prediction model for training. The first N workpiece time series prediction data in the workpiece image prediction training samples are used as input to the time series analysis layer, and the SLM process parameter set of the last workpiece time series prediction data in the workpiece image prediction training samples is used as input to the second feature splicing layer. The workpiece image prediction loss value is calculated to determine whether the workpiece image prediction loss value is within a first preset range. The first preset range is set by the developer. If the workpiece image prediction loss value is within the first preset range, the trained workpiece image prediction model is output; otherwise, the workpiece image prediction model is continued to be trained using the workpiece image prediction training set.

[0054] Training the parameter adjustment model includes the following steps:

[0055] Obtain several parameter adjustment training samples, which include N workpiece timing prediction data. Similarly, these workpiece timing prediction data are all obtained during the actual SLM process. The parameter adjustment training samples are annotated using the process parameter optimization analysis data set. It should be noted that the process parameter optimization analysis data set is the optimal control parameter set for the subsequent SLM process corresponding to the parameter adjustment training samples, obtained based on expert experience, that is, the optimal control parameters adjusted by the actual operator; all annotated parameter adjustment training samples are combined into a parameter adjustment training set;

[0056] Obtain several image construction training samples, which include defective workpiece monitoring images and corresponding standard images. The standard images are obtained by developers based on manual repair or software repair, and the standard images are workpiece images without defects. All image construction training samples are combined into an image construction training set, and then the image construction training set is sent to the parameter-initialized standard image construction layer for training. The workpiece monitoring images in the image construction training samples are used as input, and the standard images in the image construction training samples are used as target outputs. The image construction loss value is calculated to determine whether the image construction loss value is within a second preset range. The second preset range is also set by the developer. If the image construction loss value is within the second preset range, the trained standard image construction layer is output; otherwise, the workpiece standard image construction layer is continued to be trained through the image construction training set;

[0057] The parameters of the fixed standard image construction layer remain unchanged, and the trained workpiece image prediction model and the parameter adjustment model are connected to form a fusion model. The parameter adjustment training set is sent to the fusion model for training, and the fusion loss value is calculated to determine whether the fusion loss value is within the third preset range. The third preset range is also set by the developer. If the fusion loss value is within the third preset range, the trained parameter adjustment model is output; otherwise, the fusion model is continued to be trained using the parameter adjustment training set.

[0058] In order to further improve the performance of the parameter adjustment model, the simulation optimization of the parameters in the output layer of the SLM process optimization parameter set is also included. The specific steps include the following:

[0059] Construct several network parameter simulation individuals, and group all network parameter simulation individuals into a population set, and set the maximum number of iterations. It should be noted that the network parameter simulation individuals include the parameters in the output layer of the SLM process optimization parameter set, such as the number of hidden layers, the number of hidden layer neurons, and the learning rate;

[0060] Calculate the fitness of the network parameter simulation individual. The specific calculation method is as follows: set the output layer of the SLM process optimization parameter set in the parameter adjustment model through the network parameter simulation individual. Then, test the set parameter adjustment model by constructing training samples using several images. The obtained accuracy is used as the fitness of the network parameter simulation individual.

[0061] On the basis of simulating the fitness of individuals corresponding to network parameters, the population set is iteratively updated through the group optimization algorithm. The group optimization algorithm here can be a particle swarm optimization algorithm.

[0062] Until the number of iterations reaches the maximum number of iterations, the network parameter simulation individual with the largest fitness is output as the parameter in the output layer of the SLM process optimization parameter set.

[0063] Although the present invention has been described with respect to a limited number of embodiments, it will be apparent to those skilled in the art, having benefit of the foregoing description, that other embodiments are contemplated within the scope of the invention thus described. Furthermore, it should be noted that the language used in this specification has been selected primarily for readability and didactic purposes, rather than for the purpose of explaining or limiting the subject matter of the present invention. Consequently, many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the appended claims. The disclosure of the present invention is intended to be illustrative and not restrictive of the scope of the invention, which is defined by the appended claims.

Claims

1. A SLM process parameter analysis method based on machine learning, characterized in that: include: Obtain the SLM process parameter set and workpiece monitoring image at the current monitoring time point, send the workpiece monitoring image to the image feature extraction model for processing, and output the workpiece image feature vector; The SLM process parameter set at the current monitoring time point and the workpiece image feature vector are spliced ​​to form the workpiece timing prediction data, and the workpiece timing prediction data at the current monitoring time point and the previous N-1 workpiece timing prediction data are combined to form a workpiece timing prediction data set; the workpiece timing prediction data set is fed into the workpiece image prediction model for processing, and a workpiece prediction image is output; the workpiece prediction image and the SLM process parameter set at the current monitoring time point are fed into the parameter adjustment model for processing, and an SLM process optimization parameter set is output; The parameter adjustment model includes a standard image construction layer, a difference image construction layer, a difference image enhancement layer, a difference feature extraction layer, a first feature splicing layer, and an SLM process optimization parameter set output layer. The standard image construction layer is used to construct a corresponding standard image based on the workpiece prediction image; the difference image construction layer is used to construct a difference image based on the standard image and the standard image; the difference image enhancement layer is used to enhance the features of the workpiece prediction image through the difference image to construct an enhanced defect workpiece image; The difference feature extraction layer is used to extract features from the enhanced defect workpiece image to construct a difference feature vector; The first feature splicing layer is used to splice the SLM process parameter set at the current monitoring time point with the difference feature vector to construct a process parameter optimization analysis data set; the SLM process optimization parameter set output layer is used to process the process parameter optimization analysis data set and output M process optimization parameter sets.

2. The SLM process parameter analysis method based on machine learning according to claim 1, characterized in that: The workpiece prediction image and the SLM process parameter set at the current monitoring time point are fed into the parameter adjustment model for processing, and the SLM process optimization parameter set is output. Specifically, the steps include: Send the workpiece prediction image to the standard image construction layer for processing to construct a standard image; In the difference image construction layer, the standard image and the workpiece prediction image are subtracted and then normalized to construct a difference image; In the difference image enhancement layer, the artifact prediction image is multiplied with the key weight matrix and the value weight matrix respectively to construct the artifact key feature map K and the artifact value feature map V. Then, the difference image is multiplied with the query weight matrix to construct the difference query feature map Q. The attention weight matrix ATT = (QK T / (D) 0.5 ), and finally multiply the attention weight matrix ATT with the workpiece value feature map V to construct an enhanced defect workpiece image; The enhanced defect workpiece image is sent to the difference feature extraction layer for processing to construct a difference feature vector; In the first feature splicing layer, the SLM process parameter set at the current monitoring time point is spliced ​​with the difference feature vector to construct a process parameter optimization analysis data set; The process parameter optimization analysis data set is sent to the SLM process optimization parameter set output layer for processing, which needs to pass through several hidden layers to output the SLM process optimization parameter set.

3. The SLM process parameter analysis method based on machine learning according to claim 2, characterized in that: The workpiece image prediction model includes a timing analysis layer, a second feature splicing layer, and a workpiece prediction image construction layer. The timing analysis layer is used to perform timing analysis on the workpiece timing prediction dataset to construct an SLM process timing analysis vector. The second feature splicing layer is used to splice the SLM process parameter set at the current monitoring time point with the SLM process timing analysis vector to construct a workpiece prediction analysis vector; the workpiece prediction image construction layer is used to process the workpiece prediction analysis vector to construct a workpiece prediction image.

4. The SLM process parameter analysis method based on machine learning according to claim 3, characterized in that: Training the workpiece image prediction model includes the following steps: Obtain a workpiece image prediction training sample, which includes N+1 workpiece time series prediction data. Annotate the workpiece image prediction training sample with the workpiece monitoring image corresponding to the workpiece image feature vector of the last workpiece time series prediction data in the workpiece image prediction training sample. Form all annotated workpiece image prediction training samples into a workpiece image prediction training set. Then, send the workpiece image prediction training set to the parameter-initialized workpiece image prediction model for training. Use the first N workpiece time series prediction data in the workpiece image prediction training sample as the input of the time series analysis layer, use the SLM process parameter set of the last workpiece time series prediction data in the workpiece image prediction training sample as the input of the second feature splicing layer, calculate the workpiece image prediction loss value, and determine whether the workpiece image prediction loss value is within a first preset range. If the workpiece image prediction loss value is within the first preset range, output the trained workpiece image prediction model; otherwise, continue to train the workpiece image prediction model through the workpiece image prediction training set.

5. The SLM process parameter analysis method based on machine learning according to claim 4, characterized in that: Training the parameter adjustment model includes the following steps: Obtain several parameter adjustment training samples, each of which includes N workpiece timing prediction data, and label the parameter adjustment training samples using the process parameter optimization analysis data set; and form a parameter adjustment training set from all labeled parameter adjustment training samples. Acquire a number of image construction training samples, where the image construction training samples include defective workpiece monitoring images and corresponding standard images, form all the image construction training samples into an image construction training set, and then feed the image construction training set into a parameter-initialized standard image construction layer for training, using the workpiece monitoring images in the image construction training samples as input and the standard images in the image construction training samples as target outputs, calculate an image construction loss value, and determine whether the image construction loss value is within a second preset range. If the image construction loss value is within the second preset range, output the trained standard image construction layer; otherwise, continue training the workpiece standard image construction layer using the image construction training set; The parameters of the fixed standard image construction layer remain unchanged, the trained workpiece image prediction model and the parameter adjustment model are connected to form a fusion model, the parameter adjustment training set is sent to the fusion model for training, the fusion loss value is calculated, and it is determined whether the fusion loss value is within the third preset range. If the fusion loss value is within the third preset range, the trained parameter adjustment model is output; otherwise, the fusion model is continued to be trained using the parameter adjustment training set.

6. The SLM process parameter analysis method based on machine learning according to claim 5, characterized in that: It also includes simulation optimization of parameters in the output layer of the SLM process optimization parameter set, which specifically includes the following steps: Construct several network parameter simulation individuals, and form a population set of all network parameter simulation individuals, and set the maximum number of iterations; Calculate the fitness of the network parameters and simulate the corresponding individuals; Based on the fitness of the network parameters simulating the corresponding individuals, the population set is iteratively updated through the group optimization algorithm; Until the number of iterations reaches the maximum number of iterations, the network parameter simulation individual with the largest fitness is output as the parameter in the output layer of the SLM process optimization parameter set.

7. The SLM process parameter analysis method based on machine learning according to claim 6, characterized in that: The fitness corresponding to the network parameter simulation individual is calculated. The specific calculation method is as follows: the output layer of the SLM process optimization parameter set in the parameter adjustment model is set through the network parameter simulation individual, and then the set parameter adjustment model is tested by constructing training samples through several images. The obtained accuracy is used as the fitness corresponding to the network parameter simulation individual.

8. The SLM process parameter analysis method based on machine learning according to claim 7, characterized in that: The standard image construction layer is built based on the U-net model, and the artifact prediction image construction layer is set based on the decoder in the U-net model.

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