Online monitoring laser directional energy deposition strain prediction method and device

Abstract

The invention discloses an on-line monitoring laser directional energy deposition strain prediction method and device, and the method comprises the steps: obtaining an image data set in a machining part manufacturing process, and carrying out the data preprocessing, and obtaining a preprocessed image data set; introducing significance feature analysis, and constructing a multi-scale multi-modal fusion prediction model; and performing laser directional energy deposition strain prediction on the preprocessed image data set based on a multi-scale multi-modal fusion prediction model to obtain a laser directional energy deposition strain value prediction result in the manufacturing process of the machined part. The device comprises a laser DED manufacturing system and a manufacturing monitoring system. According to the embodiment of the invention, the method can improve the efficiency of laser directional energy deposition strain prediction and the accuracy of laser directional energy deposition strain prediction. The method and the device for predicting the strain of the laser directional energy deposition based on online monitoring can be widely applied to the technical field of laser directional energy deposition.

Description

Technical Field

[0001] The present invention relates to the technical field of laser directional energy deposition, and in particular to an online monitoring laser directional energy deposition strain prediction method and device. Background Art

[0002] Laser directed energy deposition (DED) technology is an advanced additive manufacturing process, but it is a non-equilibrium processing technology with rapid cooling rate and high thermal gradient. Many complex physical and chemical changes will occur in a very short time, making it difficult to ensure the consistency of processed parts. After the laser beam scans, the material near the molten pool will be subjected to repeated thermal cycles. The thermal gradient and thermal cycle in the manufacturing process will lead to the accumulation of internal stress, further causing residual stress and strain inside the part. At present, the strain prediction for the laser directed energy deposition process is mainly based on finite element analysis, which requires a lot of simplification of the real physical process and requires huge calculation amount and time. In addition, traditional stress and strain measurement methods such as drilling technology, X-ray technology and neutron diffraction technology are non-in-situ measurements and cannot detect the strain data of the welding heat affected zone, that is, it is impossible to quickly and accurately predict the strain of the laser DED process. Summary of the invention

[0003] In order to solve the above technical problems, the purpose of the present invention is to provide an online monitoring laser directed energy deposition strain prediction method and device, which can improve the efficiency and accuracy of laser directed energy deposition strain prediction.

[0004] The first technical solution adopted by the present invention is: an online monitoring laser directed energy deposition strain prediction method, comprising the following steps:

[0005] Acquire an image data set during the manufacturing process of a processed part and perform data preprocessing to obtain a preprocessed image data set;

[0006] Introduce significant feature analysis and build a multi-scale multi-modal fusion prediction model;

[0007] Based on the multi-scale and multi-modal fusion prediction model, the laser directed energy deposition strain prediction is performed on the preprocessed image data set to obtain the prediction results of the laser directed energy deposition strain value in the manufacturing process of the processed parts.

[0008] Furthermore, the step of obtaining an image data set during the manufacturing process of the processed parts and performing data preprocessing to obtain a preprocessed image data set specifically includes:

[0009] Acquire an image data set during the manufacturing process of a machined part, wherein the image data set includes a molten pool image, a thermal imaging image, and a manufacturing sidewall image;

[0010] According to the aspect ratio of the molten pool, the molten pool image is preprocessed to obtain a pseudo-color image of the molten pool;

[0011] Perform matching calculation between adjacent images of the manufactured side wall image to obtain the strain thermodynamic map;

[0012] Combine the pseudo-color image of the molten pool, the thermal imaging image and the strain thermodynamic map and perform time-space alignment and sliding window division processing in sequence to obtain the pseudo-color image of the molten pool after division, the thermal imaging image after division and the strain thermodynamic map after division;

[0013] Get parameter vector;

[0014] The input data set is constructed by combining the pseudo-color image of the melt pool after segmentation, the thermal imaging image after segmentation and the parameter vector;

[0015] The divided strain thermodynamic map is used as label data and combined with the input data set to obtain the preprocessed image data set.

[0016] Further, the step of performing image preprocessing on the molten pool image according to the aspect ratio of the molten pool to obtain a pseudo-color image of the molten pool specifically includes:

[0017] Determine four corner points of the molten pool area according to the molten pool image;

[0018] The four corner points of the molten pool area are calculated through the perspective transformation function of OpenCV to obtain the homography matrix;

[0019] Performing perspective transformation correction on the melt pool image according to the homography matrix to obtain a corrected melt pool image;

[0020] The corrected molten pool image is filtered to remove random noise points to obtain a filtered molten pool image;

[0021] The filtered molten pool image is subjected to binarization and threshold segmentation in turn to obtain the molten pool contour;

[0022] The aspect ratio information of the melt pool contour is obtained through the minimum circumscribed rectangle and normalized to obtain a pseudo-color image of the melt pool.

[0023] Furthermore, the step of performing matching calculation between adjacent images of the manufacturing sidewall image to obtain the strain thermodynamic map specifically includes:

[0024] The manufacturing sidewall images acquired at adjacent moments are processed by pixel feature point association matching using a sub-pixel matching algorithm to obtain the manufacturing sidewall images after association matching;

[0025] The full-field strain calculation is performed on the associated matched manufacturing sidewall image according to the full-field three-dimensional spatial coordinates to obtain the strain thermodynamic map.

[0026] Further, the parameter vector specifically includes laser power, scanning speed, powder feeding rate, layer thickness, scanning strategy, gas flow rate, beam diameter and defocus, wherein:

[0027] The laser power indicates the laser emission power set by the laser;

[0028] The scanning speed represents the displacement speed of the cladding head;

[0029] The powder feeding rate refers to the powder flow rate emitted by the powder feeder and collected by the cladding head;

[0030] The layer thickness indicates the lifting height of the cladding head after printing a layer during the manufacturing process of the processed parts;

[0031] The scanning strategy represents the scanning rules preset in the manufacturing process of the machined parts;

[0032] The gas flow rate indicates the flow rate of the protective gas;

[0033] The beam diameter refers to the diameter of the laser beam after passing through the focusing lens and the collimating lens.

[0034] The defocus amount indicates the distance that the focus deviates from the processed part.

[0035] Furthermore, the multi-scale multi-modal fusion prediction model includes a multi-scale multi-modal feature extraction module, a significant feature analysis module and a strain value prediction module, the output end of the multi-scale multi-modal feature extraction module is connected to the input end of the significant feature analysis module, and the output end of the significant feature analysis module is connected to the input end of the strain value prediction module, wherein:

[0036] The multi-scale multi-modal feature extraction module includes a shallow feature extraction module, a convolution operation module and a fusion module, and the convolution operation module includes a two-dimensional convolution layer, a batch normalization and a linear rectification unit;

[0037] The significant feature analysis module includes a t-distributed random neighborhood embedding layer and a principal component analysis layer;

[0038] The strain value prediction module includes a support vector machine.

[0039] Further, the step of performing laser directed energy deposition strain prediction on the preprocessed image data set based on the multi-scale multi-modal fusion prediction model to obtain the laser directed energy deposition strain value prediction result in the manufacturing process of the processed parts specifically includes:

[0040] Input the preprocessed image dataset into the multi-scale multi-modal fusion prediction model;

[0041] The multi-scale multi-modal feature extraction module based on the multi-scale multi-modal fusion prediction model performs shallow feature extraction, downsampling convolution operation and feature fusion processing on the pre-processed image data set in turn to obtain the fused image features;

[0042] Based on the salient feature analysis module of the multi-scale multi-modal fusion prediction model, the fused image features are subjected to data dimensionality reduction processing to obtain the image features after dimensionality reduction;

[0043] Based on the strain value prediction module of the multi-scale multi-modal fusion prediction model, the reduced-dimensional image features are classified and the laser directed energy deposition strain is predicted to obtain the prediction results of the laser directed energy deposition strain value in the manufacturing process of processed parts.

[0044] The second technical solution adopted by the present invention is: an online monitoring laser directed energy deposition strain prediction device, including a laser DED manufacturing system and a manufacturing monitoring system, wherein:

[0045] The laser DED manufacturing system is used to provide a parameter vector;

[0046] The manufacturing monitoring system is used to obtain image data sets during the manufacturing process of machined parts.

[0047] Furthermore, the laser DED manufacturing system includes a robot, a laser, a water cooler, a powder feeding system and a cladding head, wherein:

[0048] The robot is used to provide the displacement required in the manufacturing process of the machined parts;

[0049] The laser is used to provide laser emission power;

[0050] The water cooler is used to cool the laser;

[0051] The powder feeding system is used to eject metal powder through the cladding head to provide raw materials for the manufacturing process of machined parts;

[0052] The cladding head is used to gather metal powder at one point.

[0053] Further, the manufacturing monitoring system includes a molten pool industrial camera, a thermal imager and a strain visual sensor, wherein:

[0054] The molten pool industrial camera is used to obtain the molten pool image;

[0055] The thermal imager is used to obtain thermal imaging images;

[0056] The strain vision sensor is used to obtain a manufacturing side wall image.

[0057] The beneficial effects of the method and system of the present invention are as follows: the present invention obtains an image data set in the process of machining parts manufacturing and performs data preprocessing to obtain a preprocessed image data set, and by applying known information such as equipment, machining parameters, and machining process, the information is more fully utilized, and the accuracy of strain prediction is improved without adding other monitoring tools, and then significant feature analysis is introduced to construct a multi-scale multi-modal fusion prediction model, and finally, based on the multi-scale multi-modal fusion prediction model, laser directed energy deposition strain prediction is performed on the preprocessed image data set to obtain the prediction result of the laser directed energy deposition strain value in the process of machining parts manufacturing. The multi-scale multi-modal fusion framework integrates and analyzes the local details and global context of multi-source data, which can effectively improve the accuracy of feature extraction and the prediction ability of the model, and improves the accuracy of prediction through significant feature analysis, and inputs the pseudo-color image of the aspect ratio of the molten pool in the local window as an image feature into the deep learning model to extract features, and the process parameters and thermal imaging are combined. Figure 1 Taking the same into consideration, it provides rich information for strain prediction, greatly reduces the time of strain prediction, and improves the efficiency of strain prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a flow chart of the steps of an online monitoring laser directed energy deposition strain prediction method of the present invention;

[0059] Figure 2 It is a flowchart of the steps of strain prediction of laser directed energy deposition provided by an embodiment of the present invention;

[0060] Figure 3 is a structural diagram of a laser DED manufacturing system provided by a specific embodiment of the present invention;

[0061] Figure 4 It is a structural schematic diagram of an online monitoring laser directed energy deposition strain prediction device of the present invention;

[0062] Figure 5 is a schematic diagram of the structure of a multi-scale multi-modal fusion prediction model constructed in an embodiment of the present invention;

[0063] Figure 6 Schematic diagram of four different laser scanning modes provided by an embodiment of the present invention.

[0064] Figure numerals: 1. robot; 2. L-shaped light baffle; 3. rangefinder bracket; 4. molten pool industrial camera; 5. active light source; 6. macro lens; 7. cladding head; 8. thin wall manufacturing; 9. thermal imager; 10. micro tripod; 11. vise; 12. cladding platform; 13. cross bracket; 14. strain vision sensor; 15. measuring head; 16. blue light LED. DETAILED DESCRIPTION

[0065] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The step numbers in the following embodiments are only provided for the convenience of explanation and description, and the order between the steps is not limited in any way. The execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0066] First of all, it should be noted that laser directed energy deposition (DED) technology is an advanced additive manufacturing process. It melts metal powder through a high-energy-density heat source and deposits it layer by layer. It has a wide range of material applicability, excellent mechanical properties, fast manufacturing speed, high design flexibility, and high efficiency in part repair and modification. DED technology can realize the manufacturing of complex geometric shapes and is suitable for a variety of industrial fields, including aerospace, automobiles, and medical treatment. It also supports the processing of a variety of materials and composite materials, providing advantages that traditional manufacturing methods cannot match. However, it is a non-equilibrium processing technology with a fast cooling rate and high thermal gradient. Many complex physical and chemical changes will occur in a very short time, making it difficult to ensure the consistency of the processed parts. After the laser beam scans, the material near the molten pool will be subjected to repeated thermal cycles. The thermal gradient and thermal cycle in the manufacturing process will lead to the accumulation of internal stress, further causing residual stress and strain inside the part.

[0067] Currently, strain prediction for laser directed energy deposition process is mainly based on finite element analysis, which requires a lot of simplification of the real physical process and requires huge amount of calculation and time.

[0068] Based on this, Figure 2 As shown, the embodiment of the present invention obtains a molten pool image, a thermal imaging image and an image of a manufacturing side wall during the manufacturing process, obtains the molten pool length and molten pool width according to the molten pool image, processes the molten pool aspect ratio into a molten pool aspect ratio pseudo-color image, matches adjacent images according to the manufacturing side wall image, calculates a strain thermodynamic map, aligns the molten pool aspect ratio pseudo-color image, thermal imaging image and strain thermodynamic map, and performs position alignment and sliding window division on the molten pool aspect ratio pseudo-color image, uses the parameter vector in the manufacturing process, the molten pool aspect ratio pseudo-color image 32×32 and the thermal imaging image 32×32 as input data, labels them with the strain mean within 32×32 of the corresponding position, and divides the processed data into a training set and a test set in proportion; a deep convolutional neural network is constructed, and the strain value is predicted by the constructed multi-scale multi-modal fusion framework; the method can quickly and accurately predict the strain value, greatly saving the strain time required by the simulation method.

[0069] Reference Figure 1 The present invention provides an online monitoring laser directed energy deposition strain prediction method, the method comprising the following steps:

[0070] S100, acquiring an image data set in a manufacturing process of a processed part and performing data preprocessing to obtain a preprocessed image data set;

[0071] S110, acquiring an image data set during the manufacturing process of a processed part, wherein the image data set includes a molten pool image, a thermal imaging image, and a manufacturing sidewall image;

[0072] Specifically, the acquisition of thermal images is first explained. The temperature information of the manufacturing process is collected by a thermal imager placed on the manufacturing side wall. The acquisition frequency of the thermal imager is 60Hz, and the acquisition temperature range is 0℃-2000℃. Secondly, the acquisition of the molten pool image is explained. The molten pool image is obtained by a paraxially mounted molten pool industrial camera. The built-in active vision and macro lens are used to ensure image quality and obtain rich molten pool information. The molten pool industrial camera has an acquisition frequency of 60Hz and a resolution of 1280×1024. Finally, the acquisition of the manufacturing side wall image is explained. The manufacturing side wall image is obtained by obtaining a high-definition image through two measuring heads.

[0073] S120, performing image preprocessing on the molten pool image according to the aspect ratio of the molten pool to obtain a molten pool pseudo-color image;

[0074] Specifically, the four corner points of the melt pool area are determined according to the melt pool image; the four corner points of the melt pool area are calculated by the perspective transformation function of OpenCV to obtain the homography matrix; the melt pool image is perspective transformed and corrected according to the homography matrix to obtain the corrected melt pool image; the corrected melt pool image is filtered to eliminate random noise points to obtain the filtered melt pool image; the filtered melt pool image is binarized and threshold segmented in turn to obtain the melt pool contour; the aspect ratio information of the melt pool contour is obtained by the minimum circumscribed rectangle and normalized to obtain a pseudo-color image of the melt pool.

[0075] In this embodiment, due to the 30° tilt angle between the camera and the molten pool, the captured image has perspective distortion, especially in the measurement of the length and area of ​​the molten pool. The image is corrected to a top-down perspective through perspective transformation to eliminate the influence of the angle, which specifically includes the following steps: first, four corner points of the molten pool area are selected from the distorted image as reference points, and then the homography matrix is ​​calculated using the perspective transformation function of OpenCV according to the actual positions of these points. Finally, the original image is corrected to a top-down perspective through perspective transformation to eliminate the geometric distortion caused by the angle. De-noising, enhancement and binarization are performed on the corrected image, the molten pool contour is extracted using threshold segmentation, the width and length of the molten pool are obtained through the minimum circumscribed rectangle, the aspect ratio of the molten pool is calculated, and then the aspect ratio information of the molten pool is normalized, and the corresponding pseudo-color image is generated through pseudo-color mapping.

[0076] S130, performing matching calculation between adjacent images of the manufacturing side wall image to obtain a strain thermodynamic map;

[0077] Specifically, the manufacturing sidewall images acquired at adjacent moments are processed by pixel feature point association matching through a sub-pixel matching algorithm to obtain the manufacturing sidewall image after association matching; the full-field strain calculation is performed on the manufacturing sidewall image after association matching according to the full-field three-dimensional spatial coordinates to obtain the strain thermodynamic map.

[0078] Furthermore, it should be noted that the full-field three-dimensional spatial coordinates are calculated by the measuring head's own software after the two measuring heads are calibrated, which is equivalent to the three-dimensional coordinates automatically calculated by the binocular camera after calibration through the calibration plate.

[0079] In this embodiment, the collected high-definition manufacturing side wall images are imported into the algorithm frame by frame, and the pixel points on the two adjacent images are associated through the image matching algorithm, and the corresponding position on the subsequent image is found for the pixel points on the previous image, and then the full-field strain is calculated based on the full-field three-dimensional spatial coordinates obtained previously.

[0080] A sub-pixel matching algorithm is used to match feature points of the manufacturing sidewall images acquired at adjacent moments. The sub-pixel matching algorithm calculates the similarity value of each sub-region through a sub-region similarity function to obtain a matching result; the translation and deformation components of the reference sub-region are estimated according to the matching structure, and then the Lagrangian strain value is calculated based on these parameter values ​​as the initial values ​​of the iteration, and the strain value of the pixel point in the ROI area of ​​the manufacturing layer wall in the manufacturing sidewall image is calculated;

[0081] Among them, the calculation expression of the sub-area similarity function is:

[0082]

[0083] In the above formula, C LS It represents the sub-region similarity value obtained after reverse Newton iteration when each sub-region is matched, M represents the sub-region radius matched by the two-dimensional digital image correlation method, i represents the horizontal image pixel coordinate, j represents the vertical image pixel coordinate, f(x i ,y i ) represents the gray value distribution function of the reference image, represents the average gray value of the sub-area of ​​the reference image, represents the average gray value of the deformed image sub-area, g(x' i , y' i ) represents the gray value distribution function of the deformed image, Δ(f 2 ) represents the grayscale value variance of the reference image sub-area, Δ(g 2 ) represents the gray value variance of the deformed image.

[0084] S140, combining the molten pool pseudo-color image, the thermal imaging image, and the strain thermodynamic map and sequentially performing spatiotemporal alignment and sliding window division processing to obtain a divided molten pool pseudo-color image, a divided thermal imaging image, and a divided strain thermodynamic map;

[0085] Specifically, the obtained molten pool aspect ratio pseudo-color map, thermal imaging map, and strain thermodynamic map are aligned in time and space, the corresponding coordinate positions and time of the three images are aligned, and then divided into 32×32 sliding windows. The divided molten pool aspect ratio pseudo-color map 32×32, thermal imaging map 32×32 and parameter vector are used as input data, and the corresponding strain mean within 32×32 is used as the label. Then the labeled data is divided into training data and test data, the data information collected by multiple sensors is aligned in time and position, and the information in the same window is used as features, which provides rich features for accurate prediction of strain.

[0086] S150, obtaining a parameter vector;

[0087] Specifically, the parameter vector includes laser power, scanning speed, powder feeding rate, layer thickness, scanning strategy, gas flow rate, beam diameter and defocus, wherein the laser power indicates the laser emission power set by the laser; the scanning speed indicates the displacement speed of the cladding head; the powder feeding rate indicates the powder flow rate emitted by the powder feeder and collected by the cladding head; the layer thickness indicates the lifting height of the cladding head after printing a layer during the manufacturing process of the processed parts; the scanning strategy indicates the preset scanning rules during the manufacturing process of the processed parts; the gas flow rate indicates the flow rate of the protective gas; the beam diameter indicates the diameter of the laser beam after passing through the focusing mirror and the collimating mirror; the defocus indicates the distance that the focus deviates from the processed parts.

[0088] Furthermore, it is necessary to specify that laser power refers to the laser emission power set by the laser; scanning speed refers to the displacement speed of the cladding head; powder feeding rate refers to the powder flow rate sent by the powder feeder and collected by the cladding head; layer thickness refers to the lifting height of the cladding head after printing a layer during the manufacturing process; scanning strategy refers to several preset scanning speeds in the manufacturing process; gas flow refers to the flow rate of protective gas; beam diameter refers to the diameter reached by the laser beam after passing through the focusing lens and collimating lens; defocus refers to the distance from the focus to the workpiece, which is mainly used to change the power density radiated to the workpiece surface. The focal plane is located above the workpiece for positive defocus, and vice versa for negative defocus.

[0089] like Figure 6 As shown, the scanning strategy in the embodiment of the present invention is explained, wherein: Figure 6(a) indicates inter-layer dwell S-line scanning. The laser is turned off 10 mm before the robot is about to be lifted. The laser is still turned off when the layer height is raised. After lifting, the laser is turned on after moving 10 mm in the opposite direction. This ensures a certain dwell time between layers, thereby reducing heat accumulation and ensuring that no bulges are generated on both sides of the thin-walled parts. Figure 6 (b) represents S-shaped scanning, where the end of the robot scans layer by layer in an S-shape without stopping between layers. This type of scanning can print more efficiently and save printing time; Figure 6 (c) indicates unidirectional scanning, where the robot scans in a single direction, starting from one side and ending at the other side each time. This scanning method can have a longer interlayer cooling time and further reduce heat accumulation, but it is more time-consuming; Figure 6 (d) represents double-layer S-shaped scanning, where the two adjacent layers are scanned in a unidirectional manner, and the double layers are scanned in an S-line manner, which can combine the advantages of both to ensure a certain efficiency while reducing heat accumulation.

[0090] S160, combining the divided molten pool pseudo-color image, the divided thermal imaging image and the parameter vector to construct an input data set;

[0091] S170 , using the divided strain thermodynamic map as label data and combining it with the input data set to obtain a preprocessed image data set.

[0092] S200, introduce significant feature analysis and build a multi-scale multi-modal fusion prediction model;

[0093] Specifically, Figure 5 As shown, the multi-scale multi-modal fusion prediction model includes a multi-scale multi-modal feature extraction module, a significant feature analysis module and a strain value prediction module. The output end of the multi-scale multi-modal feature extraction module is connected to the input end of the significant feature analysis module, and the output end of the significant feature analysis module is connected to the input end of the strain value prediction module. The multi-scale multi-modal feature extraction module includes a shallow feature extraction module, a convolution operation module and a fusion module. The convolution operation module includes a two-dimensional convolution layer, a batch normalization and a linear rectification unit; the significant feature analysis module includes a t-distributed random neighborhood embedding layer and a principal component analysis layer; the strain value prediction module includes a support vector machine.

[0094] S300, based on the multi-scale multi-modal fusion prediction model, the laser directed energy deposition strain is predicted for the preprocessed image data set to obtain the prediction result of the laser directed energy deposition strain value in the manufacturing process of the processed parts.

[0095] S310, inputting the preprocessed image data set into a multi-scale multi-modal fusion prediction model;

[0096] S320, a multi-scale multi-modal feature extraction module based on a multi-scale multi-modal fusion prediction model, sequentially performing shallow feature extraction, downsampling convolution operation and feature fusion processing on the pre-processed image data set to obtain fused image features;

[0097] Specifically, the pseudo-color image of the aspect ratio of the melt pool and the thermal image are first extracted into a 16×16×32 feature map through shallow features, where the shallow feature extraction first uses 32 convolution kernels, each with a size of 3×3 and a stride of 2, and then uses 64 convolution kernels, each with a size of 1×1 and a stride of 1; then, through a series of convolution operations, the shallow extracted feature map is downsampled to 8×8×64 and 4×4×128 in turn, and jump connections are used in this process to retain feature information, where each layer of downsampling convolution operations is implemented in the form of "Conv2d+BN+ReLU", that is, a two-dimensional convolution layer, batch normalization and a linear rectifier unit; after feature extraction, the extracted feature map is fused with the parameter vector through a fusion module, and the fusion module uses a multi-head attention mechanism to dynamically select and combine features.

[0098] S330, based on the salient feature analysis module of the multi-scale multi-modal fusion prediction model, performing data dimensionality reduction processing on the fused image features to obtain image features after dimensionality reduction;

[0099] Specifically, the fused features are processed by t-SNE (t-distributed stochastic neighbor embedding layer) and PCA (principal component analysis layer) for dimensionality reduction to improve the efficiency and visualization effect of subsequent analysis.

[0100] S340, based on the strain value prediction module of the multi-scale multi-modal fusion prediction model, classifies the image features after dimensionality reduction and predicts the laser directed energy deposition strain, and obtains the prediction results of the laser directed energy deposition strain value in the manufacturing process of the processed parts.

[0101] Specifically, the significant features after dimensionality reduction are input into SVM (support vector machine) for classification and analysis, and finally the prediction results of strain values ​​are output.

[0102] Reference Figure 4 , an online monitoring laser directed energy deposition strain prediction device, including a laser DED manufacturing system and a manufacturing monitoring system, wherein:

[0103] The laser DED manufacturing system is used to provide a parameter vector;

[0104] Specifically, the laser DED manufacturing system includes a robot, a laser, a water cooler, a powder feeding system and a cladding head, wherein the robot is used to provide the displacement required in the manufacturing process of the processed parts; the laser is used to provide the laser emission power; the water cooler is used to cool the laser; the powder feeding system is used to spray metal powder through the cladding head to provide raw materials in the manufacturing process of the processed parts; and the cladding head is used to gather the metal powder at one point.

[0105] In this embodiment, if Figure 3 As shown, the robot 1 provides the displacement required for manufacturing and can ensure the precise displacement of the end of the actuator. The laser emits a high-energy laser beam with a laser power range of 0 to 20,000 W to provide the required energy for the manufacturing process. The water chiller cools the laser. The powder feeder sprays the metal powder through the cladding head to provide the required raw materials for manufacturing. The cladding head is installed at the end of the robot and is an actuator for printing and manufacturing. It can not only gather the metal powder to one point, but also accurately output the laser beam to the powder gathering point to complete the specific manufacturing process.

[0106] Manufacturing monitoring systems are used to acquire image datasets during the manufacturing process of machined parts.

[0107] Specifically, the manufacturing monitoring system includes a molten pool industrial camera, a thermal imager and a strain vision sensor, wherein the molten pool industrial camera is used to obtain molten pool images; the thermal imager is used to obtain thermal imaging images; and the strain vision sensor is used to obtain manufacturing side wall images.

[0108] In this embodiment, if Figure 4 As shown, the manufacturing monitoring system is mainly composed of three sensors, namely, a molten pool industrial camera 4, a thermal imager 9 and a strain visual sensor 14; the molten pool industrial camera is installed on a paragon bracket 3, and moves synchronously with the cladding head 7, on which a macro lens 6 is installed, and an active light source 5 is provided inside to provide illumination; the thermal imager 9 is installed on a micro tripod 10, and placed together on a cladding platform 12, located on the left side of the manufactured part, and perpendicular to the side wall of the manufactured part; the strain visual sensor 14 is installed with two measuring heads 15 for collecting images of the side walls of the manufactured part, and the strain visual sensor is installed on a cross bracket 13, and a blue light LED 16 is placed next to it to provide sufficient light source for strain image acquisition, and an L-shaped light shielding plate 2 is provided next to the manufactured part to reduce the influence of laser on image acquisition.

[0109] The molten pool industrial camera 4 is installed on the paraxial bracket 3 and moves synchronously with the cladding head 7. A macro lens 6 is installed on it to filter and reduce the interference of the laser on the image, and an active light source 5 is provided inside it to provide the illumination required for image acquisition. The molten pool industrial camera is at an angle of 30° to the cladding head, the acquisition frame rate is 60Hz, and the resolution is 1280×1024, which can capture the molten pool image during the manufacturing process.

[0110] The thermal imager 9 is mounted on a miniature tripod 10 and placed together on a cladding platform 12. A vise 11 is provided under the cladding platform, located on the left side of the manufactured part and perpendicular to the side wall of the manufactured part. The thermal imager has a temperature measurement range of 0 to 2000°C and a sampling frequency of 60Hz. It can collect the temperature of the parts during the manufacturing process and the temperature information in the ROI area.

[0111] The strain vision sensor 14 has two measuring heads 15 installed on it for collecting the side wall image of the manufactured part. The strain vision sensor is installed on the manufactured thin wall 8 on the cross bracket 13, and a blue light LED 16 is placed next to it to provide sufficient light for strain image acquisition. An L-shaped light baffle 2 is set next to the manufactured part to reduce the influence of the laser on image acquisition.

[0112] The contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0113] The above is a specific description of the preferred implementation of the present invention, but the invention is not limited to the embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for predicting strain of laser directed energy deposition by online monitoring, characterized in that: The following steps are involved: Acquire an image data set during the manufacturing process of a processed part and perform data preprocessing to obtain a preprocessed image data set; Introduce significant feature analysis and build a multi-scale multi-modal fusion prediction model; Based on the multi-scale and multi-modal fusion prediction model, the laser directed energy deposition strain prediction is performed on the preprocessed image data set to obtain the prediction results of the laser directed energy deposition strain value in the manufacturing process of the processed parts.

2. The method for predicting strain of laser directed energy deposition by online monitoring according to claim 1, characterized in that: The step of obtaining an image data set during the manufacturing process of the processed parts and performing data preprocessing to obtain a preprocessed image data set specifically includes: Acquire an image data set during the manufacturing process of a machined part, wherein the image data set includes a molten pool image, a thermal imaging image, and a manufacturing sidewall image; According to the aspect ratio of the molten pool, the molten pool image is preprocessed to obtain a pseudo-color image of the molten pool; Perform matching calculation between adjacent images of the manufactured side wall image to obtain the strain thermodynamic map; Combine the pseudo-color image of the molten pool, the thermal imaging image and the strain thermodynamic map and perform time-space alignment and sliding window division processing in sequence to obtain the pseudo-color image of the molten pool after division, the thermal imaging image after division and the strain thermodynamic map after division; Get parameter vector; The input data set is constructed by combining the pseudo-color image of the melt pool after segmentation, the thermal imaging image after segmentation and the parameter vector; The divided strain thermodynamic map is used as label data and combined with the input data set to obtain the preprocessed image data set.

3. The method for predicting strain of laser directed energy deposition by online monitoring according to claim 2, characterized in that: The step of performing image preprocessing on the molten pool image according to the aspect ratio of the molten pool to obtain a molten pool pseudo-color image specifically includes: Determine four corner points of the molten pool area according to the molten pool image; The four corner points of the molten pool area are calculated through the perspective transformation function of OpenCV to obtain the homography matrix; Performing perspective transformation correction on the melt pool image according to the homography matrix to obtain a corrected melt pool image; The corrected molten pool image is filtered to remove random noise points to obtain a filtered molten pool image; The filtered molten pool image is subjected to binarization and threshold segmentation in turn to obtain the molten pool contour; The aspect ratio information of the melt pool contour is obtained through the minimum circumscribed rectangle and normalized to obtain a pseudo-color image of the melt pool.

4. The method for predicting strain of laser directed energy deposition by online monitoring according to claim 2, characterized in that: The step of performing matching calculation between adjacent images of the manufacturing side wall image to obtain the strain thermodynamic map specifically includes: The manufacturing sidewall images acquired at adjacent moments are processed by pixel feature point association matching using a sub-pixel matching algorithm to obtain the manufacturing sidewall images after association matching; The full-field strain calculation is performed on the associated matched manufacturing sidewall image according to the full-field three-dimensional spatial coordinates to obtain the strain thermodynamic map.

5. The method for predicting strain of laser directed energy deposition by online monitoring according to claim 2, characterized in that: The parameter vector specifically includes laser power, scanning speed, powder feeding rate, layer thickness, scanning strategy, gas flow rate, beam diameter and defocus, wherein: The laser power indicates the laser emission power set by the laser; The scanning speed represents the displacement speed of the cladding head; The powder feeding rate refers to the powder flow rate emitted by the powder feeder and collected by the cladding head; The layer thickness indicates the lifting height of the cladding head after printing a layer during the manufacturing process of the processed parts; The scanning strategy represents the scanning rules preset in the manufacturing process of the machined parts; The gas flow rate indicates the flow rate of the protective gas; The beam diameter refers to the diameter of the laser beam after passing through the focusing lens and the collimating lens. The defocus amount indicates the distance that the focus deviates from the processed part.

6. The method for predicting strain of laser directed energy deposition by online monitoring according to claim 1, characterized in that: The multi-scale multi-modal fusion prediction model includes a multi-scale multi-modal feature extraction module, a significant feature analysis module and a strain value prediction module, the output end of the multi-scale multi-modal feature extraction module is connected to the input end of the significant feature analysis module, and the output end of the significant feature analysis module is connected to the input end of the strain value prediction module, wherein: The multi-scale multi-modal feature extraction module includes a shallow feature extraction module, a convolution operation module and a fusion module, and the convolution operation module includes a two-dimensional convolution layer, a batch normalization and a linear rectification unit; The significant feature analysis module includes a t-distributed random neighborhood embedding layer and a principal component analysis layer; The strain value prediction module includes a support vector machine.

7. The method for predicting strain of laser directed energy deposition by online monitoring according to claim 6, characterized in that: The step of performing laser directed energy deposition strain prediction on the preprocessed image data set based on the multi-scale multi-modal fusion prediction model to obtain the laser directed energy deposition strain value prediction result in the manufacturing process of the processed parts specifically includes: Input the preprocessed image dataset into the multi-scale multi-modal fusion prediction model; The multi-scale multi-modal feature extraction module based on the multi-scale multi-modal fusion prediction model performs shallow feature extraction, downsampling convolution operation and feature fusion processing on the pre-processed image data set in turn to obtain the fused image features; Based on the salient feature analysis module of the multi-scale multi-modal fusion prediction model, the fused image features are subjected to data dimensionality reduction processing to obtain the image features after dimensionality reduction; Based on the strain value prediction module of the multi-scale multi-modal fusion prediction model, the reduced-dimensional image features are classified and the laser directed energy deposition strain is predicted to obtain the prediction results of the laser directed energy deposition strain value in the manufacturing process of processed parts.

8. An online monitoring laser directed energy deposition strain prediction device, characterized in that: It includes laser DED manufacturing system and manufacturing monitoring system, including: The laser DED manufacturing system is used to provide a parameter vector; The manufacturing monitoring system is used to obtain image data sets during the manufacturing process of machined parts.

9. The on-line monitoring laser directed energy deposition strain prediction device according to claim 8, characterized in that: The laser DED manufacturing system includes a robot, a laser, a water cooler, a powder feeding system and a cladding head, wherein: The robot is used to provide the displacement required in the manufacturing process of the machined parts; The laser is used to provide laser emission power; The water cooler is used to cool the laser; The powder feeding system is used to eject metal powder through the cladding head to provide raw materials for the manufacturing process of machined parts; The cladding head is used to gather metal powder at one point.

10. The on-line monitoring laser directed energy deposition strain prediction device according to claim 8, characterized in that: The manufacturing monitoring system includes a molten pool industrial camera, a thermal imager and a strain vision sensor, wherein: The molten pool industrial camera is used to obtain the molten pool image; The thermal imager is used to obtain thermal imaging images; The strain vision sensor is used to obtain a manufacturing side wall image.

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