Selective laser melting process monitoring method combining plume dynamic characteristics with graph convolutional network

Abstract

This invention discloses a method for monitoring a selective laser melting process by combining plume dynamic features with a graph convolutional network. The method comprises the following steps: 1. capturing plume images during the selective laser melting process using an off-axis high-speed camera; 2. performing threshold segmentation and feature extraction on the resulting plume images to obtain plume dynamic features; 3. establishing a melting state recognition method using a graph convolutional network based on graph convolutional deep learning; 4. training the graph convolutional network and evaluating its recognition performance; and 5. capturing real-time plume images and extracting plume dynamic features, which are then input into the trained model to identify and monitor the melting state during the selective laser melting process. This method improves the accuracy of melting state recognition and enables accurate monitoring of the selective laser melting process.

Description

Technical Field

[0001] The present invention belongs to the field of selective laser melting process monitoring, and specifically provides a selective laser melting process monitoring method that combines plume dynamic characteristics with a graph convolutional network. Background Art

[0002] The high degree of customization and excellent processing accuracy of selective laser melting demonstrate its advantages in various fields such as aerospace, automotive industry, and medical and health sectors. Since selective laser melting is a fast and non-stationary process, and it is difficult to directly measure the quality of the powder after melting and forming online, the use of process signals such as sound and optics to monitor the selective laser melting process has become one of the key issues in recent years. Among them, collecting optical signal characteristics during the processing by high-speed cameras is an important method for monitoring the selective laser melting process. This method can capture images of the plume composed of metal vapor and plasma during the processing. Existing studies have shown that there is a close relationship between plumes and process stability. The feasibility and effectiveness of plume-based selective laser melting process monitoring have been proven in many cases, but they are only in the theoretical stage and have not been put into actual process monitoring applications.

[0003] At the same time, most current plume-based selective laser melting process monitoring methods simply calculate simple statistical features of the plume, such as plume length, area, and brightness, and input these features into shallow machine learning models for melt state identification. These simple statistical features cannot describe the plume morphology under each processing condition in detail and accurately, nor can they reflect the instantaneous dynamics and dynamic behavior of the plume. At the same time, simple machine learning models generally only use the plume characteristics at the current moment as input, without including dynamic information about the changes in plume characteristics at consecutive moments. These models also generally lack the ability to extract time series signal features, resulting in a high false positive rate and inaccurate identification of the melt state. Summary of the Invention

[0004] In order to address the deficiencies of the above-mentioned prior art, the present invention proposes a method for monitoring the selective laser melting process by combining plume dynamic characteristics with a graph convolutional network, in order to improve the accuracy of melting state identification and solve the problem of low classification accuracy in the melting state identification of selective laser melting, thereby realizing efficient and accurate monitoring of the selective laser melting process.

[0005] In order to achieve the above-mentioned object, the present invention adopts the following technical solutions:

[0006] The characteristic of the selective laser melting process monitoring method combining plume dynamic characteristics with graph convolutional network of the present invention is that it is carried out according to the following steps:

[0007] Step 1: Collect near-infrared images of the selected laser melting process and crop them to retain the plume part, and convert the cropped images into grayscale images to obtain the plume image dataset. in, represents the nth plume image, y n represents the nth plume image I n Corresponding to the category label of the melting state, C, H, and W represent the number of channels, height, and width of the plume image; n = 1, 2, …, N, where N represents the total number of plume images;

[0008] Step 2: threshold segment the plume image and establish the plume dynamic feature V;

[0009] The plume feature points corresponding to the plume feature point value set in V are used as nodes of the graph, the plume feature values ​​in V are used as features on the corresponding nodes, and A and D are used as the adjacency matrix and degree matrix of the graph respectively, thereby constructing graph structure data;

[0010] Step 3: Establish a graph convolutional network based on graph convolution deep learning, including: spatiotemporal feature extraction module and feature output module;

[0011] Step 3.1: The spatiotemporal feature extraction module consists of L residual graph convolution modules;

[0012] When l = 1, the plume dynamic features V, adjacency matrix A and degree matrix D are input into the lth residual graph convolution module for processing to obtain the lth plume spatiotemporal feature map H l ;

[0013] When l=2,3,…,L, the l-1th plume spatiotemporal characteristic map H l-1 , the adjacency matrix A and the degree matrix D are input into the lth residual graph convolution module for processing, and the lth plume spatiotemporal feature map H is obtained. l , so that the Lth residual graph convolution module outputs the Lth plume spatiotemporal graph H L , and serves as a coding feature of plume motion;

[0014] Step 3.2: The feature output module includes c linear layers and one Sigmoid layer;

[0015] For H L After the flattening operation, the one-dimensional plume motion encoding feature is obtained, which is then processed by c linear layers and one Sigmoid layer to obtain the melting state probability vector Q with a dimension of 1×num, where num is the number of categories of the melting state category label;

[0016] Step 4. A cross-entropy loss function is constructed based on Q and the true melting state label to update the parameters of the graph convolutional network, thereby obtaining a melting state classification model. This model is used to identify the plume image during the selective laser melting process and obtain the melting state category of the current process, thereby realizing the monitoring of the selective laser melting process.

[0017] The method for monitoring a selective laser melting process by combining plume dynamic features with a graph convolutional network according to the present invention is also characterized in that the plume dynamic features V, the adjacency matrix A, and the degree matrix D in step 2 are obtained according to the following steps:

[0018] Step 2.1: Use the OTSU algorithm to calculate the n-th plume image I n Perform threshold segmentation to obtain the nth image segmentation contour of the plume and the melt pool, and define its boundary points as in, is the coordinate of the i-th boundary point of the n-th image segmentation contour on the x-axis and y-axis, i = 1, 2, ..., P n , P n Represents the total number of boundary points on the nth image segmentation contour, and the minimum y-axis coordinate in the nth image segmentation contour is recorded as

[0019] Set the y-axis coordinate to arrive The contour within the range is regarded as the nth plume image I n The molten pool contour is set to The contour of the n-th plume image I n The plume profile of ; where h is the pixel-level height of the melt pool;

[0020] The centroid of the melt pool contour in the nth image segmentation contour is recorded as

[0021] Step 2.2: Take the centroid of the melt pool contour As a starting point, we will follow the nth plume image I n The positive direction of the x-axis is defined as the starting vector;

[0022] After the starting vector is rotated counterclockwise with a step size of m°, I is obtained. n M rotation vectors;

[0023] Combine the M rotation vectors with the n-th plume image I n The intersection points on the plume contour are defined as plume feature points, thus obtaining the plume feature point set F n ={f n,u |u=1,...,M}, where f n,uRepresents the u-th plume feature point, that is, the u-th rotation vector and I n The intersection of the plume contours;

[0024] Construct the adjacency matrix A={a uv |u=1,..,M,v=1,..,M}, where, a uv The angle between the u-th rotation vector and the v-th rotation vector among the M rotation vectors is expressed in degrees;

[0025] The degree matrix D={d uu |u=1,..,M} is defined as a diagonal matrix, where d uu represents the element in the u-th row and u-th column of the degree matrix D, and

[0026] Calculate the plume feature point to the centroid of the melt pool contour The Euclidean distance of n The plume eigenvalue set S n ={s n,v |v=1,...,M}, where s n,v represents the vth plume characteristic value, that is, the vth plume characteristic point f u,v To the centroid of the melt pool contour The Euclidean distance of

[0027] Get the nth plume image I n To the n+tth plume image I n+t The plume characteristic values ​​are stacked according to the time dimension, and the plume dynamic characteristics are obtained as V = {S j |j=n,..,n+t}, where S j Table 1: The jth plume image I j The set of plume eigenvalues.

[0028] Each residual graph convolution module in step 3.1 includes a graph convolution layer, a spatiotemporal gated convolution module, and a residual edge module;

[0029] Step 3.1.1, the graph convolution layer in the lth residual graph convolution module uses formula (1) to obtain the lth plume spatial domain feature gcn l :

[0030]

[0031] In formula (1), I e Represents The same dimension unit matrix, when l = 1, let H l-1 =V,W l is the weight to be learned in the graph convolution layer of the lth residual graph convolution module;

[0032] Step 3.1.2: The spatiotemporal gated convolution module in the lth residual graph convolution module is used to convolutional map of gcn l Process and output the lth plume time domain feature tcn l ;

[0033] Step 3.1.3: The residual edge module in the lth residual graph convolution module uses the convolution layer, batch normalization layer and ReLU activation function to sequentially l-1 Process and output the lth plume residual feature res l ;

[0034] Step 3.1.4: TCN l and res l After adding, we get H l .

[0035] The step 3.1.2 includes:

[0036] Step a: The spatiotemporal gated convolution module uses a 1D convolutional layer to convolutionally convolution the gcn l Processing is performed to obtain the lth 1D convolution feature

[0037] Step b, the spatiotemporal gated convolution module uses the gated linear unit GLU to convolutional neural networks (GCN). l and Processing is performed to obtain the lth plume time domain feature tcn l ;

[0038] The linear gating unit GLU converts gcn into l Evenly split into two dilated convolution input features and in, After a hole convolution layer and a Sigmoid function, the lth gated probability feature is obtained After processing by another dilated convolutional layer, the lth gated numerical feature is obtained

[0039] Will and After addition, we get the lth gated convolution feature

[0040] Will and After multiplication, the lth plume time domain feature tcn is obtained l ;

[0041] The electronic device of the present invention includes a memory and a processor, and is characterized in that the memory is used to store a program that supports the processor to execute the selective laser melting process monitoring method, and the processor is configured to execute the program stored in the memory.

[0042] The present invention provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and the computer program executes the steps of the selective laser melting process monitoring method when the computer program is executed by a processor.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. The present invention proposes a method for monitoring the selective laser melting process based on plume information. This method adopts an advanced graph convolution deep learning model, takes the plume dynamic map obtained during the real-time processing process as input, and uses the final melting state as the label, thereby solving the mapping problem between the plume signal and the final melting quality, and realizing the prediction of the melting state through an optical monitoring platform, thereby realizing online selective laser melting forming quality monitoring.

[0045] 2. This paper proposes a graph-structured method for establishing plume dynamic features. By combining plume image features at multiple time steps, the two-dimensional image signal is converted into graph-structured data that effectively describes plume dynamics. The graph structure also defines nodes, features, adjacency matrices, and degree matrices. This feature incorporates information about plume changes over time, enhancing the inherent correlation between plume dynamics and melting conditions.

[0046] 3. This paper designs a graph convolutional network based on graph convolution to achieve melt state classification. This model uses time-gated convolution to improve its ability to extract temporal features, and a residual graph convolution module to extract plume features. This model achieves high accuracy in melt state classification, effectively improving the precision of selective laser melting process monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flow chart of the method for monitoring the selective laser melting process based on plume dynamic characteristics and graph convolutional networks of the present invention;

[0048] Figure 2 is a schematic diagram of the experimental platform and monitoring equipment of the present invention;

[0049] Figure 3 is a schematic diagram of the plume image collected by the present invention and the single-track electron microscope image corresponding to the five types of melting state labels;

[0050] Figure 4 It is a structural diagram of the graph convolutional network of the present invention;

[0051] Figure 5 It is a structural diagram of the residual graph convolution module of the present invention;

[0052] Figure 6 It is a structural diagram of the time-gated convolution module of the present invention. DETAILED DESCRIPTION

[0053] In this embodiment, a method for monitoring the selective laser melting process is developed by combining plume dynamic characteristics with graph convolutional networks. The specific process is as follows: Figure 1 As shown, follow the steps below:

[0054] Step 1: Collect the near-infrared image of the selected laser melting process and crop it to retain the plume part. In this example, the experimental platform is as follows: Figure 2 As shown, the cropped image is converted into a grayscale image to obtain the plume image dataset in, represents the nth plume image, y n represents the nth plume image I n Corresponding to the category label of the molten state, C, H, and W represent the number of channels, height, and width of the plume image; n = 1, 2, …, N, where N represents the total number of plume images.

[0055] In this embodiment, the model of the selective laser melting equipment used is the YLR-200-SM-AC laser, the laser power range is 0-200w, the high-speed camera model of the plume image acquisition equipment is FASTCAMMiniUX50 / 100 (near-infrared camera), and an external band-pass filter of 700-1000nm is installed. The angle between the camera and the platform is 30°, and the camera sampling frequency is 3000PFS. The metal powder material used in the embodiment is 304L stainless steel powder, and by setting different process parameters, a single-track melting experiment is carried out to simulate five different melting states. The collected plume images are 3 channels, and the image size is 120×160. A total of 2,800 plume images are collected to form a data set. The five types of melting states are normal melting, slightly overmelting, overmelting, slightly undermelting, and undermelting. The plume image examples and the melt channel electron microscope images of the five types of melting states are shown as follows. Figure 3 shown.

[0056] Step 2: threshold segment the plume image and establish the plume dynamic feature V;

[0057] Step 2.1: Use the OTSU algorithm to calculate the n-th plume image I n Perform threshold segmentation to obtain the nth image segmentation contour of the plume and the melt pool, and define its boundary points as in, is the coordinate of the i-th boundary point of the n-th image segmentation contour on the x-axis and y-axis, i = 1, 2, ..., P n , P n Represents the total number of boundary points on the nth image segmentation contour, and the minimum y-axis coordinate in the nth image segmentation contour is recorded as

[0058] Set the y-axis coordinate to arrive The contour within the range is regarded as the nth plume image I n The molten pool contour is set to The contour of the n-th plume image I n The plume profile is: where h is the pixel-level height of the molten pool. In this embodiment, h = 20;

[0059] The centroid of the melt pool contour in the nth image segmentation contour is recorded as

[0060] Step 2.2: Take the centroid of the melt pool contour As a starting point, we will follow the nth plume image I n The positive direction of the x-axis is defined as the starting vector;

[0061] After the starting vector is rotated counterclockwise with a step size of m°, I is obtained. n M rotation vectors.

[0062] Combine the M rotation vectors with the n-th plume image I n The intersection points on the plume contour are defined as plume feature points, thus obtaining the plume feature point set F n ={f n,u |u=1,...,M}, where f n,u Represents the u-th plume feature point, that is, the u-th rotation vector and I n The intersection of the plume contours.

[0063] Construct the adjacency matrix A={a uv |u=1,..,M,v=1,..,M}, where, a uv The angle between the u-th rotation vector and the v-th rotation vector among the M rotation vectors is expressed in degrees;

[0064] The degree matrix D={d uu |u=1,..,M} is defined as a diagonal matrix, where d uu represents the element in the u-th row and u-th column of the degree matrix D, and

[0065] Calculate the plume feature point to the centroid of the melt pool contour The Euclidean distance ofn The plume eigenvalue set S n ={s n,v |v=1,...,M}, where s n,v represents the vth plume characteristic value, that is, the vth plume characteristic point f u,v To the centroid of the melt pool contour The Euclidean distance of

[0066] In this example, the angular step of the vector rotation is m°=10°, and a total of 19 rotation vectors are obtained, that is, M=19.

[0067] Get the nth plume image I n To the n+tth plume image I n+t The plume characteristic values ​​are stacked according to the time dimension, and the plume dynamic characteristics are obtained as V = {S j |j=n,..,n+t}, where S j Table 1: The jth plume image I j In this example, t=5.

[0068] The plume feature points corresponding to the plume feature point value set in V are used as nodes of the graph, the plume feature values ​​in V are used as features on the corresponding nodes, and A and D are used as the adjacency matrix and degree matrix of the graph respectively, thereby constructing graph structure data.

[0069] Step 3: Establish a graph convolutional network based on graph convolutional deep learning. In this example, the graph convolutional network structure used is as follows: Figure 4 As shown, it includes: spatiotemporal feature extraction module and feature output module;

[0070] Step 3.1: The spatiotemporal feature extraction module consists of L residual graph convolution modules;

[0071] When l = 1, the plume dynamic features V, adjacency matrix A and degree matrix D are input into the lth residual graph convolution module for processing to obtain the lth plume spatiotemporal feature map H l ; In this embodiment, the dimensions of V are 1×5×19, and the dimensions of A and D are 19×19.

[0072] When l=2,3,…,L, the l-1th plume spatiotemporal characteristic map H l-1 , the adjacency matrix A and the degree matrix D are input into the lth residual graph convolution module for processing, and the lth plume spatiotemporal feature map H is obtained. l , so that the Lth residual graph convolution module outputs the Lth plume spatiotemporal graph H L, and used as plume motion encoding features; in this example case, the number of residual graph convolution modules is 2, that is, L = 2, where the dimension of the plume spatiotemporal feature map output by the first residual graph convolution module is 32×3×19, and the dimension of the plume spatiotemporal feature map output by the second residual graph convolution module is 64×1×19, that is, H L The dimensions are 64×1×19.

[0073] Each residual graph convolution module in step 3.1 contains a graph convolution layer, a spatiotemporal gated convolution module, and a residual edge module. In this example, the residual graph convolution module used is as follows: Figure 5 As shown;

[0074] Step 3.1.1, the graph convolution layer in the lth residual graph convolution module uses formula (1) to obtain the lth plume spatial domain feature gcn l :

[0075]

[0076] In formula (1), I e Represents The same dimension unit matrix, when l = 1, let H l-1 =V,W l is the weight to be learned in the graph convolution layer of the lth residual graph convolution module.

[0077] Step 3.1.2: The spatiotemporal gated convolution module in the lth residual graph convolution module is used to convolutional map of gcn l Process and output the lth plume time domain feature tcn l In this embodiment, the spatiotemporal gated convolution module is as follows Figure 6 As shown:

[0078] Step a: The spatiotemporal gated convolution module uses a 1D convolutional layer to convolutionally convolution the gcn l Processing is performed to obtain the lth 1D convolution feature In this embodiment, the convolution kernel size of the 1D convolution layer is 1×1, and the stride is 1.

[0079] Step b, the spatiotemporal gated convolution module uses the gated linear unit GLU to convolutional neural networks (GCN). l and Processing is performed to obtain the lth plume time domain feature tcn l ;

[0080] The linear gate control unit GLU converts gcn into l Evenly split into two dilated convolution input features and in, After a hole convolution layer and a Sigmoid function, the lth gated probability feature is obtained After processing by another dilated convolutional layer, the lth gated numerical feature is obtained

[0081] Will and After addition, we get the lth gated convolution feature

[0082] Will and After multiplication, the lth plume time domain feature tcn is obtained l ;

[0083] In this embodiment, the convolution kernel size of the dilated convolution is 2×1 and the step size is 1.

[0084] Step 3.1.3: The residual edge module in the lth residual graph convolution module uses the convolution layer, batch normalization layer and ReLU activation function to sequentially l-1 Process and output the lth plume residual feature res l ; In this embodiment, the convolution kernel size of the convolution layer is 2×1, and the step size is 1.

[0085] Step 3.1.4: TCN l and res l After adding, we get H l .

[0086] Step 3.2: The feature output module contains c linear layers and one Sigmoid layer;

[0087] For H L After the flattening operation is performed, a one-dimensional plume motion encoding feature is obtained, which is then processed sequentially through c linear layers and one Sigmoid layer to obtain a melting state probability vector Q of dimension 1×num, where num is the number of categories of melting state category labels. In this embodiment, the one-dimensional plume motion encoding feature obtained after the flattening operation has a dimension of 1×1216, the number of linear layers is 2, the output dimension of the first linear layer is 1×512, and the output dimension of the second linear layer is 1×5, that is, the dimension of Q is 1×5, where the melting state categories include normal melting, slightly overmelting, overmelting, slightly undermelting, and undermelting, that is, num=5.

[0088] Step 4: A cross-entropy loss function is constructed based on Q and the true melt state label. This function is used to update the parameters of the graph convolutional network, thereby obtaining a melt state classification model. This model is used to identify plume images during the selective laser melting process and determine the melt state category of the current process, thereby enabling monitoring of the selective laser melting process. In this example, 2800 plume images were collected to form a dataset and partitioned into training, test, and validation sets in a 6:2:2 ratio. The Adam optimizer was used with an initial learning rate of 0.01, which was reduced by 10% every 10 iterations. The training process was repeated 200 times.

[0089] In this embodiment, an electronic device includes a memory and a processor. The memory is used to store a program that supports the processor to execute the tool wear state monitoring method. The processor is configured to execute the program stored in the memory.

[0090] In this embodiment, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for monitoring the melting state of selective laser melting are executed.

Claims

1. A method for monitoring the selective laser melting process by combining plume dynamic characteristics with graph convolutional networks, characterized in that: Follow these steps: Step 1: Collect near-infrared images of the selected laser melting process and crop them to retain the plume part, and convert the cropped images into grayscale images to obtain the plume image dataset. in, represents the nth plume image, y n represents the nth plume image I n Corresponding to the category label of the melting state, C, H, and W represent the number of channels, height, and width of the plume image; n = 1, 2, …, N, where N represents the total number of plume images; Step 2: threshold segment the plume image and establish the plume dynamic feature V; The plume feature points corresponding to the plume feature point value set in V are used as nodes of the graph, the plume feature values ​​in V are used as features on the corresponding nodes, and A and D are used as the adjacency matrix and degree matrix of the graph respectively, thereby constructing graph structure data; Step 3: Establish a graph convolutional network based on graph convolutional deep learning, including: spatiotemporal feature extraction module and feature output module; Step 3.1: The spatiotemporal feature extraction module consists of L residual graph convolution modules; When l = 1, the plume dynamic features V, adjacency matrix A and degree matrix D are input into the lth residual graph convolution module for processing to obtain the lth plume spatiotemporal feature map H l ; When l=2,3,…,L, the l-1th plume spatiotemporal characteristic map H l-1 , the adjacency matrix A and the degree matrix D are input into the lth residual graph convolution module for processing, and the lth plume spatiotemporal feature map H is obtained. l , so that the Lth residual graph convolution module outputs the Lth plume spatiotemporal graph H L , and serves as a coding feature of plume motion; Step 3.2: The feature output module includes c linear layers and one Sigmoid layer; For H L After the flattening operation, the one-dimensional plume motion encoding feature is obtained, which is then processed by c linear layers and one Sigmoid layer to obtain the melting state probability vector Q with a dimension of 1×num, where num is the number of categories of the melting state category label; Step 4. A cross-entropy loss function is constructed based on Q and the true melting state label to update the parameters of the graph convolutional network, thereby obtaining a melting state classification model. This model is used to identify the plume image during the selective laser melting process and obtain the melting state category of the current process, thereby realizing the monitoring of the selective laser melting process.

2. The method for monitoring a selective laser melting process by combining plume dynamic characteristics with a graph convolutional network according to claim 1 is characterized in that: The plume dynamic characteristics V, adjacency matrix A and degree matrix D in step 2 are obtained by the following steps: Step 2.1: Use the OTSU algorithm to calculate the n-th plume image I n Perform threshold segmentation to obtain the nth image segmentation contour of the plume and the melt pool, and define its boundary points as in, is the coordinate of the i-th boundary point of the n-th image segmentation contour on the x-axis and y-axis, i = 1, 2, ..., P n , P n Represents the total number of boundary points on the nth image segmentation contour, and the minimum y-axis coordinate in the nth image segmentation contour is recorded as Set the y-axis coordinate to arrive The contour within the range is regarded as the nth plume image I n The molten pool contour is set to The contour of the n-th plume image I n The plume profile of ; where h is the pixel-level height of the melt pool; The centroid of the melt pool contour in the nth image segmentation contour is recorded as Step 2.2: Take the centroid of the melt pool contour As a starting point, we will follow the nth plume image I n The positive direction of the x-axis is defined as the starting vector; After the starting vector is rotated counterclockwise with a step size of m°, I is obtained. n M rotation vectors; Combine the M rotation vectors with the n-th plume image I n The intersection points on the plume contour are defined as plume feature points, thus obtaining the plume feature point set F n ={f n,u |u=1,...,M}, where f n,u Represents the u-th plume feature point, that is, the u-th rotation vector and I n The intersection of the plume contours; Construct the adjacency matrix A={a uv |u=1,..,M,v=1,..,M}, where, a uv The angle between the u-th rotation vector and the v-th rotation vector among the M rotation vectors is expressed in degrees; The degree matrix D={d uu |u=1,..,M} is defined as a diagonal matrix, where d uu represents the element in the u-th row and u-th column of the degree matrix D, and Calculate the plume feature point to the centroid of the melt pool contour The Euclidean distance of n The plume eigenvalue set S n ={s n,v |v=1,...,M}, where s n,v represents the vth plume characteristic value, that is, the vth plume characteristic point f u,v To the centroid of the melt pool contour The Euclidean distance of Get the nth plume image I n To the n+tth plume image I n+t The plume characteristic values ​​are stacked according to the time dimension, and the plume dynamic characteristics are obtained as V = {S j |j=n,..,n+t}, where S j Table 1: The jth plume image I j The set of plume eigenvalues.

3. The method for monitoring a selective laser melting process by combining plume dynamic characteristics with a graph convolutional network according to claim 1 is characterized in that: Each residual graph convolution module in step 3.1 includes a graph convolution layer, a spatiotemporal gated convolution module, and a residual edge module; Step 3.1.1, the graph convolution layer in the lth residual graph convolution module uses formula (1) to obtain the lth plume spatial domain feature gcn l : In formula (1), I e Represents The same dimension unit matrix, when l = 1, let H l-1 =V,W l is the weight to be learned in the graph convolution layer of the lth residual graph convolution module; Step 3.1.2: The spatiotemporal gated convolution module in the lth residual graph convolution module is used to convolutional map of gcn l Process and output the lth plume time domain feature tcn l ; Step 3.1.3: The residual edge module in the lth residual graph convolution module uses the convolution layer, batch normalization layer and ReLU activation function to sequentially l-1 Process and output the lth plume residual feature res l ; Step 3.1.4: TCN l and res l After adding, we get H l .

4. The method for monitoring a selective laser melting process by combining plume dynamic characteristics with a graph convolutional network according to claim 3 is characterized in that: The step 3.1.2 includes: Step a: The spatiotemporal gated convolution module uses a 1D convolutional layer to convolutionally convolution the gcn l Processing is performed to obtain the lth 1D convolution feature Step b, the spatiotemporal gated convolution module uses the gated linear unit GLU to convolutional neural networks (GCN). l and Processing is performed to obtain the lth plume time domain feature tcn l ; The gated linear unit GLU converts gcn into l Evenly split into two dilated convolution input features and in, After a hole convolution layer and a Sigmoid function, the lth gated probability feature is obtained After processing by another dilated convolutional layer, the lth gated numerical feature is obtained Will and After addition, we get the lth gated convolution feature Will and After multiplication, the lth plume time domain feature tcn is obtained l .

5. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the selective laser melting process monitoring method according to any one of claims 1 to 4, and the processor is configured to execute the program stored in the memory.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for monitoring a selective laser melting process according to any one of claims 1 to 4 are executed.

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