A friction stir welding and grinding processing method and robot integrated equipment

Abstract

The present invention discloses a friction stir welding and grinding processing method and robot integrated equipment, belonging to the field of equipment processing technology. The robot integrated equipment includes a robot device, a control system and an electric spindle system. The control system is used to determine the friction stir welding trajectory and the grinding trajectory based on the joint surface trajectory. Based on the application scenario data of the workpiece to be processed, the friction stir welding neural network model and the grinding neural network model are combined to obtain the friction stir welding process parameters and the grinding process parameters. Based on the friction stir welding process parameters, the friction stir welding trajectory, the grinding process parameters and the grinding trajectory, the robot device and the electric spindle system are driven to perform friction stir welding and grinding on the workpiece to be processed, and the robot device and the electric spindle system are driven to adjust the friction stir welding and grinding process, thereby realizing the integration of friction stir welding and grinding based on the robot integrated equipment and improving the quality of friction stir welding and grinding.

Description

Technical Field

[0001] The present invention belongs to the technical field of equipment processing, and in particular relates to a processing method of stir friction welding and grinding and a robot integrated equipment. Background Art

[0002] Friction stir welding refers to a solid-phase welding method that uses the rotating friction of the stirring head to heat up and plasticize the material, and achieve metallurgical bonding. It is widely used in new energy vehicles, aviation, aerospace and other fields. Moreover, after friction stir welding, the weld needs to be polished to ensure the flatness and smoothness of the weld surface. For friction stir welding or polishing equipment, there are mainly two types of equipment: gantry type and robotic type. Among them, robotic equipment has become the key direction for the future development of friction stir welding equipment due to its advantages such as good spatial posture accessibility, high production line integration capability, small size, easy handling, and easy automation and intelligence.

[0003] In the current friction stir welding and grinding process, it is usually necessary to find a more suitable process parameter for welding and grinding through repeated trials and attempts. However, since such process parameters are obtained through multiple attempts, they are inefficient and have poor precision, resulting in low accuracy of friction stir welding and grinding. Moreover, due to the large differences in the structure, size, clamping and heat dissipation adjustment of the workpiece to be processed, especially in the case of two-dimensional and three-dimensional trajectory welding, it is more obvious. Simply performing quantitative welding and grinding through process parameters is difficult to adapt to the changes in working conditions at different processing positions, resulting in low accuracy of friction stir welding and grinding. In addition, existing equipment only has a single function of friction stir welding or grinding. If both welding and grinding functions are required, different equipment must be used, which increases equipment investment and site occupation. Therefore, there is an urgent need for a friction stir welding and grinding processing method and robot integrated equipment to address the shortcomings of the existing technology. Summary of the Invention

[0004] The present invention aims to provide a processing method and robot integrated equipment for friction stir welding and grinding to solve the above-mentioned technical problems. By calculating the friction stir welding trajectory and the grinding trajectory, and determining the friction stir welding process parameters and the grinding process parameters based on the friction stir welding neural network model and the grinding neural network model, the adjustment of friction stir welding and grinding is achieved, thereby improving the processing accuracy of robot friction stir welding and grinding.

[0005] In order to solve the above technical problems, an embodiment of the present invention provides a method for friction stir welding and grinding, comprising:

[0006] Obtaining the workpiece to be processed, the friction stir welding neural network model and the polishing neural network model;

[0007] Based on the butt joint trajectory of the workpiece to be processed, obtaining a friction stir welding trajectory and a grinding trajectory of the workpiece to be processed;

[0008] Based on the application scenario data of the workpiece to be processed, combined with the friction stir welding neural network model and the polishing neural network model, data deduction is performed on the preset process parameter database to obtain the friction stir welding process parameters and polishing process parameters of the workpiece to be processed;

[0009] performing friction stir welding on the workpiece to be processed according to the friction stir welding process parameters and the friction stir welding trajectory of the workpiece to be processed, collecting real-time friction stir welding data, and then adjusting the friction stir welding process of the workpiece to be processed based on the real-time friction stir welding data;

[0010] After completing the stir friction welding of the workpiece to be processed, the workpiece to be processed is polished based on the polishing process parameters and polishing trajectory of the workpiece to be processed, and real-time polishing data is collected. Then, the polishing process of the workpiece to be processed is adjusted according to the real-time polishing data to complete the polishing of the workpiece to be processed.

[0011] It can be understood that, compared with the prior art, the present invention determines the friction stir welding trajectory and the grinding trajectory through the joint surface trajectory of the workpiece to be processed, and then obtains the friction stir welding process parameters and the grinding process parameters of the workpiece to be processed based on the application scenario data of the workpiece to be processed in combination with the friction stir welding neural network model and the grinding neural network model; then, friction stir welding is performed on the workpiece to be processed through the friction stir welding process parameters and the friction stir welding trajectory, and the friction stir welding is adjusted in real time based on the collected real-time friction stir welding data; after the friction stir welding is completed, the workpiece to be processed is polished through the grinding process parameters and the grinding trajectory, and the grinding is adjusted in real time based on the collected real-time grinding data, thereby realizing accurate friction stir welding and grinding of the workpiece to be processed and improving the quality of friction stir welding and grinding. The present invention generates a friction stir welding trajectory and a grinding trajectory through the trajectory of the butt surface of the workpiece to be processed, thereby ensuring the matching of the friction stir welding trajectory and the grinding trajectory, thereby enabling friction stir welding and grinding to be performed simultaneously by robotic equipment; through the friction stir welding neural network model and the grinding neural network model, intelligent decision-making of friction stir welding process parameters and grinding process parameters can be achieved, avoiding the problem of decreased process parameter accuracy caused by repeated trial and error commonly used in traditional processes, and by real-time adjustment of the friction stir welding and grinding processes, it is possible to cope with complex working conditions and sudden interference in the processing process, avoid processing defects, and thus improve the accuracy of friction stir welding and grinding.

[0012] As a preferred solution, the obtaining of the workpiece to be processed, the stir friction welding neural network model and the polishing neural network model specifically includes: obtaining an initial neural network model and a workpiece to be processed, and obtaining a stir friction welding process database and a polishing process database based on the workpiece to be processed; obtaining stir friction welding process characteristic data based on the stir friction welding process database, and training the neural network structure, loss function and optimizer of the initial neural network model based on the stir friction welding process characteristic data to obtain the stir friction welding neural network model; obtaining polishing process characteristic data based on the polishing process database, and training the neural network structure, loss function and optimizer of the initial neural network model based on the polishing process characteristic data to obtain the polishing neural network model.

[0013] As a preferred solution, the method of obtaining the friction stir welding trajectory and grinding trajectory of the workpiece to be processed based on the butt surface trajectory of the workpiece to be processed specifically includes: determining the welding discrete point data of the workpiece to be processed based on the butt surface trajectory of the workpiece to be processed, and determining the welding coordinates and welding posture values ​​of the workpiece to be processed based on the welding discrete point data of the workpiece to be processed; determining the friction stir welding trajectory of the workpiece to be processed based on the welding coordinates and welding posture values ​​of the workpiece to be processed; and determining the grinding trajectory of the workpiece to be processed based on the friction stir welding trajectory of the workpiece to be processed.

[0014] As a preferred solution, the method of determining the welding discrete point data of the workpiece to be processed based on the butt joint surface trajectory of the workpiece to be processed, and determining the welding coordinates and welding posture value of the workpiece to be processed based on the welding discrete point data of the workpiece to be processed, specifically includes: determining the welding discrete point data of the workpiece to be processed based on the butt joint surface trajectory of the workpiece to be processed, the welding discrete point data including: three-dimensional coordinate data of the welding point; performing two-dimensional coordinate projection on the welding discrete point data of the workpiece to be processed based on the three-dimensional coordinate data of the welding point to determine the welding point angle value of the workpiece to be processed; calculating the rotation angle posture value of the workpiece to be processed based on the welding point angle value of the workpiece to be processed; determining the welding posture value of the workpiece to be processed based on the rotation angle posture value of the workpiece to be processed; and determining the welding coordinates of the workpiece to be processed based on the three-dimensional coordinate data of the welding point of the workpiece to be processed.

[0015] As a preferred solution, the application scenario data of the workpiece to be processed is combined with the stir friction welding neural network model and the polishing neural network model to perform data deduction on the preset process parameter database to obtain the stir friction welding process parameters and polishing process parameters of the workpiece to be processed, specifically including: based on the application scenario data of the workpiece to be processed, weighting the tensile strength, yield strength and elastic modulus of the workpiece to be processed to obtain the weight values ​​corresponding to the tensile strength, yield strength and elastic modulus; constructing a quality evaluation function based on the weight values ​​corresponding to the tensile strength, yield strength and elastic modulus of the workpiece to be processed; inputting the candidate stir friction welding process parameters in the preset process parameter database into the stir friction welding neural network model, and performing data deduction in combination with the quality evaluation function to obtain the stir friction welding process parameters of the workpiece to be processed; inputting the candidate polishing process parameters in the preset process parameter database into the stir friction welding neural network model, and performing data deduction in combination with the quality evaluation function to obtain the polishing process parameters of the workpiece to be processed.

[0016] As a preferred solution, friction stir welding is performed on the workpiece to be processed according to the friction stir welding process parameters and friction stir welding trajectory of the workpiece to be processed, and real-time friction stir welding data is collected, and then the friction stir welding process of the workpiece to be processed is adjusted based on the real-time friction stir welding data. Specifically, it includes: performing friction stir welding on the workpiece to be processed according to the friction stir welding process parameters and friction stir welding trajectory of the workpiece to be processed; collecting real-time welding pressure, real-time welding torque and real-time welding resistance of the workpiece to be processed during the friction stir welding process to determine the real-time friction stir welding data of the workpiece to be processed; and adjusting the friction stir welding process of the workpiece to be processed based on the real-time welding pressure, real-time welding torque and real-time welding resistance of the workpiece to be processed.

[0017] As a preferred solution, the friction stir welding process of the workpiece to be processed is adjusted based on the real-time welding pressure, real-time welding torque and real-time welding resistance of the workpiece to be processed, specifically including: the friction stir welding process parameters of the workpiece to be processed include: optimal welding pressure, optimal welding torque and optimal welding resistance; obtaining the welding pressure threshold, welding torque threshold and welding resistance threshold of the workpiece to be processed;

[0018] The real-time welding pressure, the optimal welding pressure and the welding pressure threshold are compared to obtain a welding pressure comparison result, and the welding pressure depth of the stir friction welding process of the workpiece to be processed is adjusted according to the welding pressure comparison result; the real-time welding torque, the optimal welding torque and the welding torque threshold are compared to obtain a welding torque comparison result, and the welding speed of the stir friction welding process of the workpiece to be processed is adjusted according to the welding torque comparison result; the real-time welding resistance, the optimal welding resistance and the welding resistance threshold are compared to obtain a welding resistance comparison result, and the welding speed of the stir friction welding process of the workpiece to be processed is adjusted according to the welding resistance comparison result.

[0019] As a preferred solution, the workpiece to be processed is polished based on the polishing process parameters and polishing trajectory of the workpiece to be processed, and real-time polishing data is collected, and then the polishing process of the workpiece to be processed is adjusted according to the real-time polishing data to complete the polishing of the workpiece to be processed, specifically including: polishing the workpiece to be processed according to the polishing process parameters and polishing trajectory of the workpiece to be processed; collecting the real-time polishing pressure, real-time polishing torque and real-time polishing resistance of the workpiece to be processed during the polishing process to determine the real-time polishing data of the workpiece to be processed; and adjusting the polishing process of the workpiece to be processed based on the real-time polishing pressure, real-time polishing torque and real-time polishing resistance of the workpiece to be processed to complete the polishing of the workpiece to be processed.

[0020] As a preferred solution, the grinding process of the workpiece to be processed is adjusted based on the real-time grinding pressure, real-time grinding torque and real-time grinding resistance of the workpiece to be processed, specifically including: the grinding process parameters of the workpiece to be processed include: optimal grinding pressure, optimal grinding torque and optimal grinding resistance; obtaining the grinding pressure threshold, grinding torque threshold and grinding resistance threshold of the workpiece to be processed; comparing the real-time grinding pressure, optimal grinding pressure and grinding pressure threshold to obtain a grinding pressure comparison result, and adjusting the grinding pressure depth of the grinding process of the workpiece to be processed according to the grinding pressure comparison result; comparing the real-time grinding torque, optimal grinding torque and grinding torque threshold to obtain a grinding torque comparison result, and adjusting the grinding speed of the grinding process of the workpiece to be processed according to the grinding torque comparison result; comparing the real-time grinding resistance, optimal grinding resistance and grinding resistance threshold to obtain a grinding resistance comparison result, and adjusting the grinding speed of the grinding process of the workpiece to be processed according to the grinding resistance comparison result.

[0021] Accordingly, an embodiment of the present invention provides a robot integrated device for stir friction welding and polishing, comprising: a robot device, a control system and an electric spindle system; the drive control output end of the control system is electrically connected to the drive control input end of the robot device; the drive control output end of the control system is electrically connected to the drive control input end of the electric spindle system; the real-time processing data input end of the control system is wirelessly connected to the real-time processing data output end of the electric spindle system; the robot device is connected to the electric spindle system; the control system is used to obtain the workpiece to be processed, the stir friction welding neural network model and the polishing neural network model; based on the joint surface trajectory of the workpiece to be processed, the stir friction welding trajectory and the polishing trajectory of the workpiece to be processed are obtained; based on the application scenario data of the workpiece to be processed, combined with the stir friction welding neural network model and the polishing neural network model, the preset process parameter database is deduced to obtain the workpiece to be processed. The control system is also used to drive the robot device and the electric spindle system to perform friction stir welding on the workpiece to be processed based on the friction stir welding process parameters and the friction stir welding trajectory of the workpiece to be processed; the control system is also used to drive the robot device and the electric spindle system to grind the workpiece to be processed based on the grinding process parameters and the grinding trajectory of the workpiece to be processed after completing the friction stir welding of the workpiece to be processed; the electric spindle system is used to collect real-time friction stir welding data, so that the control system can drive the robot device and the electric spindle system to adjust the friction stir welding process of the workpiece to be processed based on the real-time friction stir welding data; the electric spindle system is also used to collect real-time grinding data, so that the control system can drive the robot device and the electric spindle system to adjust the grinding process of the workpiece to be processed based on the real-time grinding data.

[0022] It can be understood that, compared with the existing technology, the present device determines the stir friction welding trajectory and the grinding trajectory through the joint surface trajectory of the workpiece to be processed, and then obtains the stir friction welding process parameters and the grinding process parameters of the workpiece to be processed based on the application scenario data of the workpiece to be processed and combined with the stir friction welding neural network model and the grinding neural network model; then, stir friction welding is performed on the workpiece to be processed through the stir friction welding process parameters and the stir friction welding trajectory, and the stir friction welding is adjusted in real time based on the collected real-time stir friction welding data; after the stir friction welding is completed, the workpiece to be processed is polished through the grinding process parameters and the grinding trajectory, and the grinding is adjusted in real time based on the collected real-time grinding data, thereby realizing accurate stir friction welding and grinding of the workpiece to be processed and improving the quality of stir friction welding and grinding. This device generates a stir friction welding trajectory and a grinding trajectory through the trajectory of the butt surface of the workpiece to be processed, ensuring the matching of the stir friction welding trajectory and the grinding trajectory, so that stir friction welding and grinding can be carried out simultaneously through robotic equipment; through the stir friction welding neural network model and the grinding neural network model, intelligent decision-making of stir friction welding process parameters and grinding process parameters can be achieved, avoiding the problem of reduced process parameter accuracy caused by repeated trial and error commonly used in traditional processes, and through real-time adjustment of the stir friction welding and grinding processes, it can cope with complex working conditions and sudden interference in the processing process, avoid processing defects, and thus improve the accuracy of stir friction welding and grinding. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A flowchart of a friction stir welding and grinding method according to an embodiment of the present invention;

[0024] Figure 2 A schematic diagram of equipment connections for a friction stir welding and polishing robot integrated equipment provided by an embodiment of the present invention;

[0025] Figure 3 A schematic diagram of a welding robot integrated with friction stir welding and polishing provided in an embodiment of the present invention;

[0026] Figure 4 A schematic diagram of tool change for a friction stir welding and grinding robot integrated device provided by an embodiment of the present invention;

[0027] Figure 5 A schematic diagram of a friction stir welding and polishing robot integrated device provided in an embodiment of the present invention;

[0028] Figure 6 A schematic structural diagram of a friction stir welding and polishing robot integrated device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0030] Example 1

[0031] Please refer to Figure 1 , which is a flow chart of the steps of a friction stir welding and grinding processing method provided by an embodiment of the present invention, including steps S101 to S105.

[0032] Step S101: Obtain a workpiece to be processed, a friction stir welding neural network model, and a polishing neural network model.

[0033] Step S102: based on the butt joint trajectory of the workpiece to be processed, obtaining the friction stir welding trajectory and the grinding trajectory of the workpiece to be processed.

[0034] Step S103: Based on the application scenario data of the workpiece to be processed, combined with the friction stir welding neural network model and the polishing neural network model, data deduction is performed on the preset process parameter database to obtain the friction stir welding process parameters and polishing process parameters of the workpiece to be processed.

[0035] Step S104: performing friction stir welding on the workpiece to be processed according to the friction stir welding process parameters and the friction stir welding trajectory of the workpiece to be processed, collecting real-time friction stir welding data, and then adjusting the friction stir welding process of the workpiece to be processed based on the real-time friction stir welding data.

[0036] Step S105: After completing the friction stir welding of the workpiece to be processed, the workpiece to be processed is polished based on the polishing process parameters and polishing trajectory of the workpiece to be processed, and real-time polishing data is collected, and then the polishing process of the workpiece to be processed is adjusted according to the real-time polishing data to complete the polishing of the workpiece to be processed.

[0037] This embodiment determines the friction stir welding trajectory and the grinding trajectory through the trajectory of the butt surface of the workpiece to be processed, and then obtains the friction stir welding process parameters and the grinding process parameters of the workpiece to be processed based on the application scenario data of the workpiece to be processed in combination with the friction stir welding neural network model and the grinding neural network model; then, friction stir welding is performed on the workpiece to be processed through the friction stir welding process parameters and the friction stir welding trajectory, and the friction stir welding is adjusted in real time based on the collected real-time friction stir welding data; after the friction stir welding is completed, the workpiece to be processed is polished through the grinding process parameters and the grinding trajectory, and the grinding is adjusted in real time based on the collected real-time grinding data, thereby achieving accurate friction stir welding and grinding of the workpiece to be processed and improving the quality of friction stir welding and grinding. This embodiment generates a stir friction welding trajectory and a grinding trajectory through the trajectory of the butt surface of the workpiece to be processed, ensuring the matching of the stir friction welding trajectory and the grinding trajectory, so that stir friction welding and grinding can be performed simultaneously by robotic equipment; through the stir friction welding neural network model and the grinding neural network model, intelligent decision-making of stir friction welding process parameters and grinding process parameters can be achieved, avoiding the problem of reduced process parameter accuracy caused by repeated trial and error commonly used in traditional processes, and through real-time adjustment of the stir friction welding and grinding processes, it can cope with complex working conditions and sudden interference in the processing process, avoid processing defects, and thus improve the accuracy of stir friction welding and grinding.

[0038] Example 2

[0039] Please refer to Figure 2 , which is a schematic diagram of the equipment connection of a friction stir welding and grinding robot integrated equipment provided by an embodiment of the present invention; please refer to Figure 3 , which is a schematic diagram of a welding device for a friction stir welding and grinding robot integrated device provided by an embodiment of the present invention; please refer to Figure 4 , which is a schematic diagram of a tool change for a friction stir welding and grinding robot integrated device provided by an embodiment of the present invention; please refer to Figure 5 , which is a schematic diagram of polishing of a robot-integrated device improved by an embodiment of the present invention; Figure 6 , which is a schematic structural diagram of a friction stir welding and polishing robot integrated device provided by an embodiment of the present invention;

[0040] like Figures 2 to 6 As shown, a friction stir welding and grinding processing method according to the first embodiment of the present invention is applied to Figures 2 to 6A friction stir welding and grinding robot integrated device shown in the figure, in a friction stir welding and grinding robot integrated device (hereinafter referred to as the robot integrated device), includes a robot device 1, an electric spindle system 2 and a control system 3; wherein the drive control output end of the control system 3 is electrically connected to the drive control input end of the robot device 1; the drive control output end of the control system 3 is electrically connected to the drive control input end of the electric spindle system 2; the real-time processing data input end of the control system 3 is wirelessly connected to the real-time processing data output end of the electric spindle system 2; the robot device is connected to the electric spindle system; the electric spindle system System 2 includes: an electric spindle 21, a multi-dimensional mechanical sensor 22, a stirring head 23 and a grinding head 24, wherein the electric spindle 21, the multi-dimensional mechanical sensor 22 and the end shaft of the robot device 1 are coaxially connected in sequence; the stirring head 23 is detachably coaxially connected to the lower end of the electric spindle 21; the grinding head 24 is detachably coaxially connected to the lower end of the electric spindle 21; the multi-dimensional mechanical sensor 22 is used to collect process data of stir friction welding and grinding processes; the electric spindle 21 is used for detachable coaxial connection of the stirring head 23 or the grinding head 24; the data output end of the multi-dimensional mechanical sensor is used as the real-time processing data output of the electric spindle system 2 End; the control system 3 includes: a host computer 31, a motion control module 32, an auxiliary function control module 34 and a spindle control module 33; wherein the drive instruction output end of the host computer 31 is electrically connected to the drive instruction input end of the motion control module 32; the drive instruction output end of the host computer 31 is electrically connected to the drive instruction input end of the auxiliary function control module 34; the drive instruction output end of the host computer 31 is electrically connected to the drive instruction input end of the spindle control module 33; the data input end of the host computer 31 is wirelessly connected to the data output end of the multi-dimensional mechanical sensor 22, and the drive instruction output end of the motion control module 32 and The drive command output end of the spindle control module 33 serves as the drive control output end of the control system 3; the drive input end of the electric spindle 21 is electrically connected to the drive command output end of the spindle control module 33; the drive input end of the robot device 1 is electrically connected to the drive command output end of the motion control module 32; the data input end of the host computer 31 is used to receive the process data output by the multi-dimensional mechanical sensor 22; the motion control module 32 is used to receive the motion control instruction of the host computer 31, thereby controlling the posture and motion of the robot device 1, and then realizing the stir friction welding trajectory, welding posture, welding pressure depth dw, welding speed v w , grinding trajectory, grinding posture, grinding depth dp, grinding speed v pThe spindle control module 33 is used to receive the spindle control command of the host computer 31, thereby controlling the rotation speed of the electric spindle 21 and the tool change, thereby achieving the welding speed ω of the friction stir welding w and grinding speed ω p The auxiliary function control module 34 is used to receive auxiliary function control instructions from the host computer and control the auxiliary functions of the integrated robot device. The host computer 31 is equipped with process big data software, which is used to set the friction stir welding process parameters and the grinding process parameters.

[0041] It should be noted that the friction stir welding and grinding processing method described in the embodiment of the present invention is not limited to the following Figure 2 The robot integrated device shown in the figure, and other devices with similar structures can also apply the stir friction welding and grinding processing method described in the embodiment of the present invention. Figure 2 The robot integrated equipment shown is used to specifically explain a stir friction welding and grinding processing method described in an embodiment of the present invention.

[0042] like Figure 1 As shown, step S101 is to obtain the workpiece to be processed, the friction stir welding neural network model and the polishing neural network model.

[0043] In this embodiment, the obtaining of the workpiece to be processed, the friction stir welding neural network model and the polishing neural network model specifically includes: obtaining an initial neural network model and a workpiece to be processed, and obtaining a friction stir welding process database and a polishing process database based on the workpiece to be processed; obtaining friction stir welding process characteristic data based on the friction stir welding process database, and training the neural network structure, loss function and optimizer of the initial neural network model based on the friction stir welding process characteristic data to obtain the friction stir welding neural network model; obtaining polishing process characteristic data based on the polishing process database, and training the neural network structure, loss function and optimizer of the initial neural network model based on the polishing process characteristic data to obtain the polishing neural network model.

[0044] In an optional embodiment, an initial neural network model is obtained. The initial neural network model of this embodiment is a BP neural network. A friction stir welding process database is obtained based on the workpiece to be processed, and then friction stir welding process characteristic data is obtained based on the friction stir welding process database. The friction stir welding process characteristic data includes input information and output information of the friction stir welding process database. Specifically, the input information of the friction stir welding process database includes but is not limited to welding process parameters of different material types and different material thicknesses, such as: welding speed ω w, welding speed v w , welding depth d w , welding pressure P w , welding torque N w , welding resistance N w etc.; the output information includes welding quality information, i.e. joint tensile strength σ y , joint yield strength σ u , joint elastic modulus E, etc.; then the stir friction welding process characteristic data is divided into a training set and a test set; the neural network structure, loss function, optimizer and other model parameters of the initial neural network model are trained on the training set, and the final model performance is evaluated through the test set, and then the neural network model of stir friction process parameters-mechanical parameters-welding quality is determined, that is, the stir friction welding neural network model is determined.

[0045] In an optional embodiment, an initial neural network model is obtained. The initial neural network model of this embodiment is a BP neural network. A grinding process database is obtained based on the workpiece to be processed, and then grinding process feature data is obtained based on the grinding process database. The grinding process feature data includes input information and output information of the grinding process database. Specifically, the input information of the grinding process database includes but is not limited to grinding process parameters of different types of materials, that is, the grinding speed ω p , grinding speed v p , grinding depth d p , grinding pressure P p , grinding torque N p , grinding resistance F p etc. Output information includes weld surface roughness R, weld surface finish etc.; then the polishing process characteristic data is divided into a training set and a test set; the neural network structure, loss function and optimizer and other model parameters of the initial neural network model are trained by the training set, and the final model performance is evaluated by the test set, and then the neural network model of the polishing process-mechanical parameters-polishing quality is determined, that is, the polishing neural network model is determined. It should be noted that BP neural network (BackProgation Network, back propagation neural network) is a multi-layer feedforward neural network model. The core of BP neural network is to use the error back propagation algorithm, which enables the network to learn complex input-output mapping relationships, thereby completing various tasks such as function approximation, pattern recognition, classification, and prediction. In particular, the initial neural network model of this embodiment is not limited to BP neural network, and neural network models such as CNN model and LSTM model with functions such as classification and prediction can be applied to this embodiment after corresponding transformation.

[0046] In this embodiment, friction stir welding process characteristic data and grinding process characteristic data are obtained through the friction stir welding process database and the grinding process database of the workpiece to be processed, so as to train the initial neural network model respectively, and obtain the friction stir welding neural network model and the grinding neural network model, so that the friction stir welding neural network model and the grinding neural network model can fully explore the processing characteristics of the workpiece to be processed, thereby realizing intelligent decision-making of friction stir welding process parameters and grinding process parameters, avoiding the problem of reduced process parameter accuracy caused by repeated trial and error commonly used in traditional processes, thereby improving the accuracy of friction stir welding and grinding.

[0047] Step S102 is to obtain a friction stir welding trajectory and a grinding trajectory of the workpiece to be processed based on the butt surface trajectory of the workpiece to be processed.

[0048] In this embodiment, the friction stir welding trajectory and grinding trajectory of the workpiece to be processed are obtained based on the butt surface trajectory of the workpiece to be processed, specifically including: determining the welding discrete point data of the workpiece to be processed based on the butt surface trajectory of the workpiece to be processed, and determining the welding coordinates and welding posture values ​​of the workpiece to be processed based on the welding discrete point data of the workpiece to be processed; determining the friction stir welding trajectory of the workpiece to be processed based on the welding coordinates and welding posture values ​​of the workpiece to be processed; and determining the grinding trajectory of the workpiece to be processed based on the friction stir welding trajectory of the workpiece to be processed.

[0049] This embodiment directly derives welding discrete point data through the butt surface trajectory, ensuring that the welding coordinates and welding posture values ​​can accurately meet the actual stir friction welding requirements of the workpiece to be processed, and through the collaborative calculation of the welding coordinates and welding posture values, it can automatically adapt to the complex processing process of the workpiece to be processed, thereby improving the accuracy of the stir friction welding trajectory. Subsequently, the grinding trajectory is determined based on the stir friction welding trajectory to achieve collaborative quality control of different processes, further ensuring the accuracy of the grinding trajectory, and thus improving the accuracy of stir friction welding and grinding.

[0050] In this embodiment, the method of determining the welding discrete point data of the workpiece to be processed based on the butt joint surface trajectory of the workpiece to be processed, and determining the welding coordinates and welding posture value of the workpiece to be processed based on the welding discrete point data of the workpiece to be processed, specifically includes: determining the welding discrete point data of the workpiece to be processed based on the butt joint surface trajectory of the workpiece to be processed, the welding discrete point data including: three-dimensional coordinate data of the welding point; performing two-dimensional coordinate projection on the welding discrete point data of the workpiece to be processed based on the three-dimensional coordinate data of the welding point to determine the welding point angle value of the workpiece to be processed; calculating the angle posture value of the workpiece to be processed based on the welding point angle value of the workpiece to be processed; determining the welding posture value of the workpiece to be processed based on the angle posture value of the workpiece to be processed; and determining the welding coordinates of the workpiece to be processed based on the three-dimensional coordinate data of the welding point of the workpiece to be processed.

[0051] This embodiment ensures that the welding path strictly matches the actual geometric features of the workpiece to be processed by obtaining the three-dimensional coordinate data of the welding point. Then, through two-dimensional coordinate projection, the posture solution process of the complex spatial curve can be simplified, thereby reducing the calculation complexity and avoiding errors caused by overly complex calculations, thereby improving the accuracy of the welding coordinates and welding posture values, thereby improving the accuracy of the stir friction welding trajectory and the grinding trajectory, and thus improving the accuracy of stir friction welding and grinding.

[0052] It should be noted that the process inclination angle is a unique process characteristic of friction stir welding and is also one of the key factors determining the quality of the weld. Since the process inclination angle needs to remain consistent with the welding process, that is, friction stir welding requires real-time dynamic compensation for the offset of the linear coordinates of the tool center point caused by the inclination movement of the stirring head, and the programming of complex three-dimensional curved welding trajectories cannot be compiled using common arc motion and motion orientation control instructions. The welding trajectory coordinate value is composed of the spatial coordinate value XYZ and the arbitrary value ABC, and the XY direction inclination component value BC after the process. Therefore, this embodiment achieves the maintenance of the process inclination angle by performing two-dimensional coordinate projection on the three-dimensional coordinate data of the welding point, thereby ensuring the accuracy of the friction stir welding trajectory and the grinding trajectory.

[0053] Specifically, based on the joint surface trajectory of the workpiece to be processed, the welding discrete point data of the workpiece to be processed is determined, and the welding discrete point data includes: three-dimensional coordinate data of the welding point, each welding point P i and the next welding point P i+1 The coordinate point data can be calculated as follows:

[0054]

[0055] Then calculate the welding point angle value A of the welding point projected on the XY plane i for:

[0056]

[0057] Therefore, the Z-axis rotation angle value is equal to the welding point rotation angle value A i ; Then calculate the additional value b of the welding process after the tilt angle according to the following formula i and c i , where θ is the back tilt angle of the welding process (range 0 to 3 degrees);

[0058]

[0059] Then add the value b based on the tilt angle after welding process i and c i Calculate the rotation angle B in the X-axis and Y-axis directions i and C i , thus obtaining the welding coordinates and welding posture value Pi (X i ,Y i ,Z i ,A i ,B i ,C i ); thereby obtaining the friction stir welding trajectory of the workpiece to be processed; and using the friction stir welding trajectory of the workpiece to be processed as the grinding trajectory of the workpiece to be processed.

[0060] It should be noted that the compensation calculation process of the process inclination angle described in the above text embodiment belongs to a conventional offline programming calculation method. Experimenters can also choose other process inclination angle calculation algorithms for calculation according to actual needs, thereby constructing stir friction welding trajectory and grinding trajectory.

[0061] Step S103 is to perform data deduction on a preset process parameter database based on the application scenario data of the workpiece to be processed, in combination with the friction stir welding neural network model and the polishing neural network model, to obtain the friction stir welding process parameters and polishing process parameters of the workpiece to be processed.

[0062] In this embodiment, based on the application scenario data of the workpiece to be processed, combined with the stir friction welding neural network model and the polishing neural network model, data deduction is performed on the preset process parameter database to obtain the stir friction welding process parameters and polishing process parameters of the workpiece to be processed, specifically including: based on the application scenario data of the workpiece to be processed, weighting the tensile strength, yield strength and elastic modulus of the workpiece to be processed to obtain the weight values ​​corresponding to the tensile strength, yield strength and elastic modulus; constructing a quality evaluation function based on the weight values ​​corresponding to the tensile strength, yield strength and elastic modulus of the workpiece to be processed; inputting the candidate stir friction welding process parameters in the preset process parameter database into the stir friction welding neural network model, and performing data deduction in combination with the quality evaluation function to obtain the stir friction welding process parameters of the workpiece to be processed; inputting the candidate polishing process parameters in the preset process parameter database into the stir friction welding neural network model, and performing data deduction in combination with the quality evaluation function to obtain the polishing process parameters of the workpiece to be processed.

[0063] This embodiment distributes weights on the tensile strength, yield strength and elastic modulus through the application scenario data of the workpiece to be processed, thereby ensuring that the optimization direction of the stir friction welding process parameters and the grinding process parameters are strictly aligned with the actual working conditions. By performing data deduction on the preset process parameter database through the stir friction welding neural network model and the grinding neural network model, intelligent decision-making of the stir friction welding process parameters and the grinding process parameters can be achieved, avoiding the problem of decreased process parameter accuracy caused by repeated trial and error commonly used in traditional processes, thereby improving the accuracy of stir friction welding and grinding.

[0064] In an optional embodiment, due to the different application scenarios of the workpiece to be processed, for example, in the automotive field, the tensile strength has a high priority in the quality characteristics, while in the high-speed rail field, the yield strength has a high priority. Therefore, this embodiment uses the application scenario data of the workpiece to be processed to assign weights to the tensile strength, yield strength and elastic modulus, so that the stir friction welding and grinding of the workpiece to be processed can be made more in line with the actual working conditions. Specifically, through the maximum and minimum normalization method, the data of the tensile strength, yield strength and elastic modulus of the workpiece to be processed are mapped to the [0,1] interval, and the normalized values ​​of the tensile strength, yield strength and elastic modulus of the workpiece to be processed are set to Snorm, Ynorm and Enorm respectively; their corresponding weights are w Snorm 、w Ynorm and w Enorm ; Its weight coefficients satisfy the following priorities: w Snorm >w Ynorm ,w Snorm >w Enorm; Therefore, the weight distribution can be based on the following formula:

[0065] w Snorm +w Ynorm +w Enorm =1;

[0066] w Snorm ≥2w Ynorm ;

[0067] w Snorm ≥2w Enorm ;

[0068] In particular, this embodiment sets its weight to w Snorm =0.5, w Ynorm =0.25, w Enorm =0.25;

[0069] Therefore, based on the weight values ​​corresponding to the tensile strength, yield strength and elastic modulus of the workpiece to be processed, a quality evaluation function is constructed, specifically:

[0070] Q=w Snorm Snorm+w Ynorm ·Ynorm+w Enorm ·Enorm;

[0071] The preset process parameter database described in this embodiment includes the grinding process database and the stir friction welding process database described above; by inputting the candidate stir friction welding process parameters in the preset process parameter database into the stir friction welding neural network model, the optimal welding process parameters are selected from the stir friction welding process database with the value maximization of the quality evaluation function as the data deduction target, and the stir friction welding process parameters of the workpiece to be processed are obtained. The stir friction welding process parameters include: the optimal welding speed ω w0 , optimal welding speed v w0 , optimal welding depth d w0 , optimal welding pressure P w0 , Optimal welding torque N w0 and optimal welding resistance F w0 By inputting the candidate grinding process parameters in the preset process parameter database into the stir friction welding neural network model, maximizing the value of the quality evaluation function as the data deduction target, selecting the optimal welding process parameters from the grinding process database, and obtaining the grinding process parameters of the workpiece to be processed, the grinding process parameters include: optimal grinding speed ω p0 , optimal grinding speed v p0 , optimal grinding depth d p0 , optimal grinding pressure P p0 , Optimal grinding torque N p0 , optimal grinding resistance Fp0 .

[0072] Step S104 is to perform friction stir welding on the workpiece to be processed according to the friction stir welding process parameters and friction stir welding trajectory of the workpiece to be processed, collect real-time friction stir welding data, and then adjust the friction stir welding process of the workpiece to be processed based on the real-time friction stir welding data.

[0073] In this embodiment, friction stir welding is performed on the workpiece to be processed according to the friction stir welding process parameters and the friction stir welding trajectory of the workpiece to be processed, and real-time friction stir welding data is collected, and then the friction stir welding process of the workpiece to be processed is adjusted based on the real-time friction stir welding data. Specifically, it includes: performing friction stir welding on the workpiece to be processed according to the friction stir welding process parameters and the friction stir welding trajectory of the workpiece to be processed; collecting the real-time welding pressure, real-time welding torque and real-time welding resistance of the workpiece to be processed during the friction stir welding process to determine the real-time friction stir welding data of the workpiece to be processed; and adjusting the friction stir welding process of the workpiece to be processed based on the real-time welding pressure, real-time welding torque and real-time welding resistance of the workpiece to be processed.

[0074] In this embodiment, the stir friction welding process of the workpiece to be processed is adjusted based on the real-time welding pressure, real-time welding torque and real-time welding resistance of the workpiece to be processed, specifically including: the stir friction welding process parameters of the workpiece to be processed include: optimal welding pressure, optimal welding torque and optimal welding resistance; obtaining the welding pressure threshold, welding torque threshold and welding resistance threshold of the workpiece to be processed; comparing the real-time welding pressure, optimal welding pressure and welding pressure threshold to obtain a welding pressure comparison result, and adjusting the welding pressure depth of the stir friction welding process of the workpiece to be processed according to the welding pressure comparison result; comparing the real-time welding torque, optimal welding torque and welding torque threshold to obtain a welding torque comparison result, and adjusting the welding speed of the stir friction welding process of the workpiece to be processed according to the welding torque comparison result; comparing the real-time welding resistance, optimal welding resistance and welding resistance threshold to obtain a welding resistance comparison result, and adjusting the welding speed of the stir friction welding process of the workpiece to be processed according to the welding resistance comparison result.

[0075] This embodiment obtains real-time welding pressure, real-time welding torque and real-time welding resistance, and compares them with the optimal welding pressure, optimal welding torque and optimal welding resistance respectively, so as to adjust the welding rotation speed, welding speed and welding pressure depth of the workpiece to be processed during the stir friction welding process, thereby adapting to changes in working conditions at different processing positions, thereby improving the accuracy of stir friction welding.

[0076] In an optional embodiment, the real-time welding pressure P is collected by the multi-dimensional mechanical sensor 22. w1 , Real-time welding torque N w1 and real-time welding resistance F w1 ; Set the welding pressure threshold ΔP of the workpiece to be processed w Set to 1% P w0 ~15%P w0 ; Welding torque threshold ΔN w Set to 1% N w0 ~15%N w0 ; Welding resistance threshold ΔF w Set to 1%F w0 ~15%F w0 ;

[0077] Then the real-time welding pressure P w1 and optimal welding pressure P w0 When the absolute value of the difference between the two is greater than the welding pressure threshold, that is, |P w1 -P w0 |>ΔP w When the welding depth is adjusted, specifically, when P w1 Greater than P w0 When the welding depth d is reduced at a certain rate w1 , until |P w1 -P w0 |≤ΔP w When P w1 Less than P w0 When the real-time welding depth d is increased at a certain rate w1 , until |P w1 -P w0 |≤ΔP w In particular, the process big data software of the host computer 31 sends motion control instructions to the motion control module 32, so that the motion control module 32 controls the movement of the robot device 1, thereby achieving the real-time welding pressure depth d w1 Control, in which the real-time welding depth d w1 The control can be completed through methods such as classical PID control and fuzzy PID control;

[0078] Then the real-time welding torque N w1 With the optimal welding torque N w0 When the absolute value of the difference between the two is greater than the welding torque threshold ΔN w When |N w1 -N w0 |>ΔN w When the welding speed is adjusted, specifically, when N w1 Greater than Nw0 When the welding speed is increased at a certain rate, the real-time welding speed ω w1 , until |N w1 -N w0 |≤ΔN w ; When N w1 Less than N w0 When the welding speed is reduced at a certain rate ω w1 , until |N w1 -N w0 |≤ΔN w In particular, the process big data software of the host computer 31 sends motion control instructions to the spindle control module 33, so that the spindle control module 33 controls the rotation of the electric spindle 21, thereby realizing the real-time welding speed ω w1 Control of real-time welding pressure depth w1 The control can be completed through methods such as classical PID control and fuzzy PID control;

[0079] Then the real-time welding resistance F w1 With the optimal welding resistance F w0 When the absolute value of the difference between the two is greater than the welding torque threshold ΔF w When |F w1 -F w0 |>ΔF w When F w1 Greater than F w0 When the real-time welding speed v is reduced at a certain rate w1 , until |F w1 -F w0 |≤ΔF w ; F w1 Less than F w0 When the real-time welding speed v is increased at a certain rate w1 , until |F w1 -F w0 |≤ΔF w In particular, the process big data software of the host computer 31 sends motion control instructions to the motion control module 32, so that the motion control module 32 controls the movement of the robot device 1, thereby realizing the real-time welding speed v w1 Control of real-time welding speed v w1 The control can be achieved through methods such as classical PID control and fuzzy PID control.

[0080] It should be noted that, with respect to the above comparisons of the real-time welding pressure, the real-time welding torque and the real-time welding resistance, those skilled in the art can make combinations and selections according to actual needs.

[0081] Step S105 is to grind the workpiece to be processed based on the grinding process parameters and grinding trajectory of the workpiece to be processed after completing the stir friction welding of the workpiece to be processed, and collect real-time grinding data, and then adjust the grinding process of the workpiece to be processed according to the real-time grinding data to complete the grinding of the workpiece to be processed.

[0082] In this embodiment, the workpiece to be processed is polished based on the polishing process parameters and polishing trajectory of the workpiece to be processed, and real-time polishing data is collected, and then the polishing process of the workpiece to be processed is adjusted according to the real-time polishing data to complete the polishing of the workpiece to be processed, specifically including: polishing the workpiece to be processed according to the polishing process parameters and polishing trajectory of the workpiece to be processed; collecting the real-time polishing pressure, real-time polishing torque and real-time polishing resistance of the workpiece to be processed during the polishing process to determine the real-time polishing data of the workpiece to be processed; adjusting the polishing process of the workpiece to be processed based on the real-time polishing pressure, real-time polishing torque and real-time polishing resistance of the workpiece to be processed to complete the polishing of the workpiece to be processed.

[0083] In this embodiment, the grinding process of the workpiece to be machined is adjusted based on the real-time grinding pressure, real-time grinding torque and real-time grinding resistance of the workpiece to be machined, specifically including:

[0084] The grinding process parameters of the workpiece to be processed include: optimal grinding pressure, optimal grinding torque and optimal grinding resistance; obtaining the grinding pressure threshold, grinding torque threshold and grinding resistance threshold of the workpiece to be processed; comparing the real-time grinding pressure, optimal grinding pressure and grinding pressure threshold to obtain a grinding pressure comparison result, and adjusting the grinding pressure depth of the grinding process of the workpiece to be processed according to the grinding pressure comparison result; comparing the real-time grinding torque, optimal grinding torque and grinding torque threshold to obtain a grinding torque comparison result, and adjusting the grinding speed of the grinding process of the workpiece to be processed according to the grinding torque comparison result; comparing the real-time grinding resistance, optimal grinding resistance and grinding resistance threshold to obtain a grinding resistance comparison result, and adjusting the grinding speed of the grinding process of the workpiece to be processed according to the grinding resistance comparison result.

[0085] This embodiment obtains real-time grinding pressure, real-time grinding torque and real-time grinding resistance, and compares them with the optimal grinding pressure, optimal grinding torque and optimal grinding resistance respectively, so as to adjust the grinding rotation speed, grinding speed and grinding pressure depth of the workpiece to be processed during the grinding process, thereby adapting to the changes in working conditions at different processing positions, thereby improving the grinding accuracy.

[0086] In an alternative embodiment, if Figure 3As shown, the robot integrated equipment performs friction stir welding on the workpiece 4 to be processed through the stirring head 23. After completing the friction stir welding of the workpiece to be processed, as shown in FIG. Figure 4 As shown, the host computer 31 sends a spindle control instruction to the spindle control module 33, so that the spindle control module 33 drives the electric spindle system 2 to replace the stirring head 23 at the end of the electric spindle 21 with the grinding head 24, and then Figure 5 As shown, the robot-integrated device grinds the workpiece 4 to be processed by means of a grinding head 24 .

[0087] In an optional embodiment, the real-time grinding pressure P is collected by the multi-dimensional mechanical sensor 22. p1 , Real-time grinding torque N p1 and real-time grinding resistance F p1 ; Set the grinding pressure threshold ΔP of the workpiece to be processed p Set to 1% P p0 ~15%P p0 ; Grinding torque threshold ΔN p Set to 1% N p0 ~15%N p0 ; Grinding resistance threshold ΔF p Set to 1%F p0 ~15%F p0 ;

[0088] Then the real-time grinding pressure P p1 With the optimal grinding pressure P p0 Compare them. When the absolute value of the difference between the two is greater than the grinding pressure threshold, that is, |P p1 -P p0 |>ΔP p When the grinding depth is adjusted, specifically, when P p1 Greater than P p0 When the real-time grinding depth d is reduced at a certain rate p1 , until |P p1 -P p0 |≤ΔP p When P p1 Less than P p0 When the real-time grinding depth d is increased at a certain rate p1 , until |P p1 -P p0 |≤ΔP p In particular, the process big data software of the host computer 31 sends motion control instructions to the motion control module 32, so that the motion control module 32 controls the movement of the robot device 1, thereby achieving real-time grinding depth d p1 Control of real-time welding depth d p1 The control can be completed through methods such as classical PID control and fuzzy PID control;

[0089] Then the torque N will be polished in real time p1 With the optimal grinding torque N p0 When the absolute value of the difference between the two is greater than the grinding torque threshold ΔN p When |N p1 -N p0 |>ΔN p When N p1 Greater than N p0 When the grinding speed is increased at a certain rate ω p1 , until |N p1 -N p0 |≤ΔN p ; When N p1 Less than N p0 When the grinding speed is reduced at a certain rate ω p1 , until |N p1 -N p0 |≤ΔN p In particular, the process big data software of the host computer 31 sends a motion control instruction to the spindle control module 33, so that the spindle control module 33 controls the rotation of the electric spindle 21, thereby realizing the real-time grinding speed ω p1 Control of real-time grinding speed ω p1 The control can be completed through methods such as classical PID control and fuzzy PID control;

[0090] Then the resistance F will be polished in real time p1 With optimal grinding resistance F p0 When the absolute value of the difference between the two is greater than the grinding resistance threshold ΔF p When |F p1 -F p0 |>ΔF p When F p1 Greater than F p0 When the real-time grinding speed v is reduced at a certain rate p1 , until |F p1 -F p0 |≤ΔF p When F p1 Less than F p0 When the real-time grinding speed is increased at a certain rate v p1 , until |F p1 -F p0 |≤ΔF p In particular, the process big data software of the host computer 31 sends motion control instructions to the motion control module 32, so that the motion control module 32 controls the movement of the robot device 1, thereby achieving the real-time grinding speed vp1 Control of real-time grinding speed v p1 The control can be achieved through methods such as classical PID control and fuzzy PID control.

[0091] It should be noted that, with respect to the above comparisons of grinding welding pressure, grinding welding torque and real-time grinding resistance, those skilled in the art can make combinations and selections according to actual needs.

[0092] A stir friction welding and grinding processing method described in the second embodiment of the present invention trains the initial neural network model through the stir friction welding process database and the grinding process database respectively to obtain the stir friction welding neural network model and the grinding neural network model; then, according to the joint surface trajectory of the workpiece to be processed, two-dimensional coordinate projection is performed to calculate the welding coordinates and welding posture values ​​of the workpiece to be processed, and the stir friction welding trajectory and grinding trajectory of the workpiece to be processed are determined; then, based on the application scenario data of the workpiece to be processed, combined with the stir friction welding neural network model and the grinding neural network model, data deduction is performed through the quality evaluation function to obtain stir friction welding process parameters and grinding process parameters; then, according to the stir friction welding process parameters and the grinding process parameters, stir friction welding and grinding are performed on the workpiece to be processed in turn, and real-time adjustment is performed during the stir friction welding and grinding process to achieve accurate stir friction welding and grinding of the workpiece to be processed. The present invention generates a friction stir welding trajectory and a grinding trajectory through the trajectory of the butt surface of the workpiece to be processed, thereby ensuring the matching of the friction stir welding trajectory and the grinding trajectory, thereby enabling friction stir welding and grinding to be performed simultaneously by robotic equipment; through the friction stir welding neural network model and the grinding neural network model, intelligent decision-making of friction stir welding process parameters and grinding process parameters can be achieved, avoiding the problem of decreased process parameter accuracy caused by repeated trial and error commonly used in traditional processes, and by real-time adjustment of the friction stir welding and grinding processes, it is possible to cope with complex working conditions and sudden interference in the processing process, avoid processing defects, and thus improve the accuracy of friction stir welding and grinding.

[0093] Example 3

[0094] Please refer to Figure 6, which is a structural schematic diagram of a robot integrated device for stir friction welding and polishing provided by an embodiment of the present invention, comprising: a robot device 1, a control system 3 and an electric spindle system 2; the drive control output end of the control system 3 is electrically connected to the drive control input end of the robot device 1; the drive control output end of the control system 3 is electrically connected to the drive control input end of the electric spindle system 2; the real-time processing data input end of the control system 3 is wirelessly connected to the real-time processing data output end of the electric spindle system 2; the robot device is connected to the electric spindle system; the control system 3 is used to obtain a workpiece to be processed, a stir friction welding neural network model and a polishing neural network model; based on the butt surface trajectory of the workpiece to be processed, the stir friction welding trajectory and the polishing trajectory of the workpiece to be processed are obtained; based on the application scenario data of the workpiece to be processed, combined with the stir friction welding neural network model and the polishing neural network model, the preset process parameter database is deduced to obtain the The stir friction welding process parameters and grinding process parameters of the workpiece to be processed; the control system 3 is also used to drive the robot equipment and the electric spindle system to stir friction welding the workpiece to be processed based on the stir friction welding process parameters and the stir friction welding trajectory of the workpiece to be processed; the control system 3 is also used to drive the robot equipment and the electric spindle system to grind the workpiece to be processed based on the grinding process parameters and the grinding trajectory of the workpiece to be processed after completing the stir friction welding of the workpiece to be processed; the electric spindle system 2 is used to collect real-time stir friction welding data, so that the control system can drive the robot equipment and the electric spindle system to adjust the stir friction welding process of the workpiece to be processed based on the real-time stir friction welding data; the electric spindle system 2 is also used to collect real-time grinding data, so that the control system can drive the robot equipment and the electric spindle system to adjust the grinding process of the workpiece to be processed based on the real-time grinding data.

[0095] In this embodiment, the electric spindle system 2 includes: an electric spindle 21, a multidimensional mechanical sensor 22, a stirring head 23 and a grinding head 24, wherein the electric spindle 21, the multidimensional mechanical sensor 22 and the end shaft of the robot device 1 are coaxially connected in sequence; the stirring head 23 is detachably coaxially connected to the lower end of the electric spindle 21; the grinding head 24 is detachably coaxially connected to the lower end of the electric spindle 21; the multidimensional mechanical sensor 22 is used to collect process data of stir friction welding and grinding processes; the electric spindle 21 is used to perform detachable coaxial connection of the stirring head 23 or the grinding head 24; the drive input end of the electric spindle 21 serves as the drive control input end of the electric spindle system 2.

[0096] In this embodiment, the control system 3 includes: a host computer 31, a motion control module 32, an auxiliary function control module 34 and a spindle control module 33; wherein, the drive instruction output end of the host computer 31 is electrically connected to the drive instruction input end of the motion control module 32; the drive instruction output end of the host computer 31 is electrically connected to the drive instruction input end of the auxiliary function control module 34; the drive instruction output end of the host computer 31 is electrically connected to the drive instruction input end of the spindle control module 33; the data input end of the host computer 31 is wirelessly connected to the data output end of the multi-dimensional mechanical sensor 22, and the drive instruction output end of the motion control module 32 and the drive instruction output end of the spindle control module 33 both serve as the drive control output end of the control system 3; the drive input end of the electric spindle 21 is electrically connected to the drive instruction output end of the spindle control module 33; the drive input end of the robot device 1 is electrically connected to the drive instruction output end of the motion control module 32;

[0097] The data input end of the host computer 31 is used to receive the process data output by the multi-dimensional mechanical sensor 22; the motion control module 32 is used to receive the motion control instructions of the host computer 31, thereby controlling the posture and motion of the robot device 1, and thus realizing the control of the stir friction welding trajectory, welding posture, welding pressure depth dw, welding speed v w , grinding trajectory, grinding posture, grinding depth dp, grinding speed v p The spindle control module 33 is used to receive the spindle control command of the host computer 31, thereby controlling the rotation speed of the electric spindle 21 and the tool change, thereby achieving the welding speed ω of the friction stir welding w and grinding speed ω p The auxiliary function control module 34 is used to receive auxiliary function control instructions from the host computer and control the auxiliary functions of the integrated robot device. The host computer 31 is equipped with process big data software, which is used to set the friction stir welding process parameters and the grinding process parameters.

[0098] In this embodiment, the control system 3 is used to obtain the workpiece to be processed, the stir friction welding neural network model and the polishing neural network model; based on the joint surface trajectory of the workpiece to be processed, the stir friction welding trajectory and the polishing trajectory of the workpiece to be processed are obtained; based on the application scenario data of the workpiece to be processed, combined with the stir friction welding neural network model and the polishing neural network model, data deduction is performed on the preset process parameter database to obtain the stir friction welding process parameters and the polishing process parameters of the workpiece to be processed; wherein, the obtaining of the workpiece to be processed, the stir friction welding neural network model and the polishing neural network model specifically includes: obtaining an initial neural network model and the workpiece to be processed, and obtaining a stir friction welding process database and a polishing process database based on the workpiece to be processed; obtaining stir friction welding process feature data based on the stir friction welding process database, and training the neural network structure, loss function and optimizer of the initial neural network model based on the stir friction welding process feature data to obtain the stir friction welding neural network model; obtaining polishing process feature data based on the polishing process database, and training the neural network structure, loss function and optimizer of the initial neural network model based on the polishing process feature data to obtain the polishing neural network model. The method of obtaining the friction stir welding trajectory and grinding trajectory of the workpiece to be processed based on the butt joint surface trajectory of the workpiece to be processed specifically includes: determining the welding discrete point data of the workpiece to be processed based on the butt joint surface trajectory of the workpiece to be processed, and determining the welding coordinates and welding posture values ​​of the workpiece to be processed based on the welding discrete point data of the workpiece to be processed; determining the friction stir welding trajectory of the workpiece to be processed based on the welding coordinates and welding posture values ​​of the workpiece to be processed; and determining the grinding trajectory of the workpiece to be processed based on the friction stir welding trajectory of the workpiece to be processed. The method of determining the welding discrete point data of the workpiece to be processed based on the butt joint surface trajectory of the workpiece to be processed, and determining the welding coordinates and welding posture value of the workpiece to be processed based on the welding discrete point data of the workpiece to be processed, specifically includes: determining the welding discrete point data of the workpiece to be processed based on the butt joint surface trajectory of the workpiece to be processed, the welding discrete point data including: three-dimensional coordinate data of the welding point; performing two-dimensional coordinate projection on the welding discrete point data of the workpiece to be processed based on the three-dimensional coordinate data of the welding point to determine the welding point angle value of the workpiece to be processed; calculating the rotation angle posture value of the workpiece to be processed based on the welding point angle value of the workpiece to be processed; determining the welding posture value of the workpiece to be processed based on the rotation angle posture value of the workpiece to be processed; and determining the welding coordinates of the workpiece to be processed based on the three-dimensional coordinate data of the welding point of the workpiece to be processed.The method is based on the application scenario data of the workpiece to be processed, combined with the stir friction welding neural network model and the polishing neural network model, and performs data deduction on the preset process parameter database to obtain the stir friction welding process parameters and polishing process parameters of the workpiece to be processed, specifically including: based on the application scenario data of the workpiece to be processed, performing weight distribution on the tensile strength, yield strength and elastic modulus of the workpiece to be processed, and obtaining the weight values ​​corresponding to the tensile strength, yield strength and elastic modulus respectively; constructing a quality evaluation function based on the weight values ​​corresponding to the tensile strength, yield strength and elastic modulus of the workpiece to be processed; inputting the candidate stir friction welding process parameters in the preset process parameter database into the stir friction welding neural network model, and performing data deduction in combination with the quality evaluation function to obtain the stir friction welding process parameters of the workpiece to be processed; inputting the candidate polishing process parameters in the preset process parameter database into the stir friction welding neural network model, and performing data deduction in combination with the quality evaluation function to obtain the polishing process parameters of the workpiece to be processed.

[0099] In this embodiment, the electric spindle system 2 is used to collect real-time friction stir welding data, so that the control system 3 drives the robot device and the electric spindle system to adjust the friction stir welding process of the workpiece to be processed based on the real-time friction stir welding data; wherein, the multi-dimensional mechanical sensor 22 is used to collect the real-time welding pressure, real-time welding torque and real-time welding resistance of the workpiece to be processed during the friction stir welding process, and determine the real-time friction stir welding data of the workpiece to be processed; thereby, the control system 3 drives the robot device 1 and the electric spindle system 2 to adjust the friction stir welding process of the workpiece to be processed based on the real-time welding pressure, real-time welding torque and real-time welding resistance of the workpiece to be processed; wherein, the friction stir welding process parameters of the workpiece to be processed include: optimal welding pressure, optimal welding torque and optimal welding resistance; the host computer 31 is used to obtain the welding pressure threshold, welding torque threshold and welding resistance threshold of the workpiece to be processed; the host computer 31 is also used to compare the real-time welding pressure, optimal welding pressure and optimal welding torque. force and welding pressure threshold, obtain a welding pressure comparison result, and generate a motion control instruction according to the welding pressure comparison result, so that the motion control module 32 controls the movement of the robot device 1 based on the motion control instruction, thereby adjusting the welding pressure depth of the stir friction welding process of the workpiece to be processed; the host computer 31 is also used to compare the real-time welding torque, the optimal welding torque and the welding torque threshold, obtain a welding torque comparison result, and generate a motion control instruction according to the welding torque comparison result, so that the spindle control module 33 controls the rotation of the electric spindle 21 based on the motion control instruction, thereby adjusting the welding speed of the stir friction welding process of the workpiece to be processed; the host computer 31 is also used to compare the real-time welding resistance, the optimal welding resistance and the welding resistance threshold, obtain a welding resistance comparison result, and generate a motion control instruction according to the welding torque comparison result, so that the motion control module 32 controls the movement of the robot device 1 based on the motion control instruction, thereby adjusting the welding speed of the stir friction welding process of the workpiece to be processed.

[0100] In this embodiment, the electric spindle system 2 is also used to collect real-time grinding data, so that the control system 3 drives the robot device 1 and the electric spindle system 2 to adjust the grinding process of the workpiece to be processed based on the real-time grinding data; wherein, the multi-dimensional mechanical sensor 22 is used to collect the real-time grinding pressure, real-time grinding torque and real-time grinding resistance of the workpiece to be processed during the grinding process, and determine the real-time grinding data of the workpiece to be processed; and then the control system 3 adjusts the grinding process of the workpiece to be processed based on the real-time grinding pressure, real-time grinding torque and real-time grinding resistance of the workpiece to be processed to complete the grinding of the workpiece to be processed; wherein, the grinding process parameters of the workpiece to be processed include: optimal grinding pressure, optimal grinding torque and optimal grinding resistance; the host computer 31 is used to obtain the grinding pressure threshold, grinding torque threshold and grinding resistance threshold of the workpiece to be processed; the host computer 31 is also used to compare the real-time grinding pressure, optimal grinding pressure and grinding resistance The upper computer 31 is further used to compare the real-time grinding torque, the optimal grinding torque and the grinding torque threshold to obtain the grinding torque comparison result, and generate a motion control instruction according to the grinding pressure comparison result, so that the motion control module 32 controls the movement of the robot device 1 based on the motion control instruction, thereby adjusting the grinding pressure depth of the grinding process of the workpiece to be processed; the upper computer 31 is also used to compare the real-time grinding torque, the optimal grinding torque and the grinding torque threshold to obtain the grinding torque comparison result, and generate a motion control instruction according to the grinding torque comparison result, so that the spindle control module 33 controls the rotation of the electric spindle 21 based on the motion control instruction, thereby adjusting the grinding speed of the grinding process of the workpiece to be processed; the upper computer 31 is also used to compare the real-time grinding resistance, the optimal grinding resistance and the grinding resistance threshold to obtain the grinding resistance comparison result, and generate a motion control instruction according to the grinding resistance comparison result, so that the motion control module 32 controls the movement of the robot device 1 based on the motion control instruction, thereby adjusting the grinding speed of the grinding process of the workpiece to be processed.

[0101] This embodiment determines the friction stir welding trajectory and the grinding trajectory through the trajectory of the butt surface of the workpiece to be processed, and then obtains the friction stir welding process parameters and the grinding process parameters of the workpiece to be processed based on the application scenario data of the workpiece to be processed in combination with the friction stir welding neural network model and the grinding neural network model; then, friction stir welding is performed on the workpiece to be processed through the friction stir welding process parameters and the friction stir welding trajectory, and the friction stir welding is adjusted in real time based on the collected real-time friction stir welding data; after the friction stir welding is completed, the workpiece to be processed is polished through the grinding process parameters and the grinding trajectory, and the grinding is adjusted in real time based on the collected real-time grinding data, thereby achieving accurate friction stir welding and grinding of the workpiece to be processed and improving the quality of friction stir welding and grinding. This embodiment generates a stir friction welding trajectory and a grinding trajectory through the trajectory of the butt surface of the workpiece to be processed, ensuring the matching of the stir friction welding trajectory and the grinding trajectory, so that stir friction welding and grinding can be performed simultaneously by robotic equipment; through the stir friction welding neural network model and the grinding neural network model, intelligent decision-making of stir friction welding process parameters and grinding process parameters can be achieved, avoiding the problem of reduced process parameter accuracy caused by repeated trial and error commonly used in traditional processes, and through real-time adjustment of the stir friction welding and grinding processes, it can cope with complex working conditions and sudden interference in the processing process, avoid processing defects, and thus improve the accuracy of stir friction welding and grinding.

[0102] To sum up, the embodiment of the present invention determines the stir friction welding trajectory and the grinding trajectory through the joint surface trajectory of the workpiece to be processed, and then obtains the stir friction welding process parameters and the grinding process parameters of the workpiece to be processed based on the application scenario data of the workpiece to be processed in combination with the stir friction welding neural network model and the grinding neural network model; then, stir friction welding is performed on the workpiece to be processed through the stir friction welding process parameters and the stir friction welding trajectory, and the stir friction welding is adjusted in real time based on the collected real-time stir friction welding data; after the stir friction welding is completed, the workpiece to be processed is polished through the grinding process parameters and the grinding trajectory, and the grinding is adjusted in real time based on the collected real-time grinding data, thereby realizing accurate stir friction welding and grinding of the workpiece to be processed and improving the quality of stir friction welding and grinding. The embodiment of the present invention generates a stir friction welding trajectory and a grinding trajectory through the trajectory of the butt surface of the workpiece to be processed, ensuring the matching of the stir friction welding trajectory and the grinding trajectory, so that stir friction welding and grinding can be performed simultaneously by robotic equipment; through the stir friction welding neural network model and the grinding neural network model, intelligent decision-making of stir friction welding process parameters and grinding process parameters can be achieved, avoiding the problem of reduced process parameter accuracy caused by repeated trial and error commonly used in traditional processes, and through real-time adjustment of the stir friction welding and grinding processes, it can cope with complex working conditions and sudden interference in the processing process, avoid processing defects, and thus improve the accuracy of stir friction welding and grinding.

[0103] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for friction stir welding and grinding, characterized in that: include: Obtaining a workpiece to be processed, a friction stir welding neural network model, and a polishing neural network model, including: obtaining an initial neural network model and a workpiece to be processed, and obtaining a friction stir welding process database and a polishing process database based on the workpiece to be processed; obtaining friction stir welding process characteristic data based on the friction stir welding process database, and training a neural network structure, a loss function, and an optimizer of the initial neural network model based on the friction stir welding process characteristic data to obtain a friction stir welding neural network model; obtaining polishing process characteristic data based on the polishing process database, and training a neural network structure, a loss function, and an optimizer of the initial neural network model based on the polishing process characteristic data to obtain a polishing neural network model; Based on the butt joint surface trajectory of the workpiece to be processed, obtaining a friction stir welding trajectory and a grinding trajectory of the workpiece to be processed, including: determining welding discrete point data of the workpiece to be processed based on the butt joint surface trajectory of the workpiece to be processed, and determining welding coordinates and welding posture values ​​of the workpiece to be processed based on the welding discrete point data of the workpiece to be processed; determining the friction stir welding trajectory of the workpiece to be processed based on the welding coordinates and welding posture values ​​of the workpiece to be processed; and determining the grinding trajectory of the workpiece to be processed based on the friction stir welding trajectory of the workpiece to be processed; Based on the application scenario data of the workpiece to be processed, in combination with the stir friction welding neural network model and the polishing neural network model, data deduction is performed on a preset process parameter database to obtain the stir friction welding process parameters and polishing process parameters of the workpiece to be processed, including: based on the application scenario data of the workpiece to be processed, weighting the tensile strength, yield strength and elastic modulus of the workpiece to be processed to obtain the weight values ​​corresponding to the tensile strength, yield strength and elastic modulus; constructing a quality evaluation function based on the weight values ​​corresponding to the tensile strength, yield strength and elastic modulus of the workpiece to be processed; inputting the candidate stir friction welding process parameters in the preset process parameter database into the stir friction welding neural network model, and performing data deduction in combination with the quality evaluation function to obtain the stir friction welding process parameters of the workpiece to be processed; inputting the candidate polishing process parameters in the preset process parameter database into the stir friction welding neural network model, and performing data deduction in combination with the quality evaluation function to obtain the polishing process parameters of the workpiece to be processed; performing friction stir welding on the workpiece to be processed according to the friction stir welding process parameters and the friction stir welding trajectory of the workpiece to be processed, collecting real-time friction stir welding data, and then adjusting the friction stir welding process of the workpiece to be processed based on the real-time friction stir welding data; After completing the stir friction welding of the workpiece to be processed, the workpiece to be processed is polished based on the polishing process parameters and polishing trajectory of the workpiece to be processed, and real-time polishing data is collected. Then, the polishing process of the workpiece to be processed is adjusted according to the real-time polishing data to complete the polishing of the workpiece to be processed.

2. A friction stir welding and grinding processing method according to claim 1, characterized in that: The step of determining the welding discrete point data of the workpiece to be processed based on the butt joint surface trajectory of the workpiece to be processed, and determining the welding coordinates and welding posture value of the workpiece to be processed based on the welding discrete point data of the workpiece to be processed, specifically includes: Determining welding discrete point data of the workpiece to be processed based on the butt joint surface trajectory of the workpiece to be processed, wherein the welding discrete point data includes: three-dimensional coordinate data of the welding point; Based on the three-dimensional coordinate data of the welding points, performing two-dimensional coordinate projection on the welding discrete point data of the workpiece to be processed to determine the rotation angle value of the welding point of the workpiece to be processed; Calculating a rotation angle posture value of the workpiece to be processed based on a welding point rotation angle value of the workpiece to be processed; Determining a welding posture value of the workpiece to be processed based on the rotation angle posture value of the workpiece to be processed; The welding coordinates of the workpiece to be processed are determined based on the three-dimensional coordinate data of the welding points of the workpiece to be processed.

3. The friction stir welding and grinding method according to claim 1, wherein: The method of performing friction stir welding on the workpiece to be processed according to the friction stir welding process parameters and the friction stir welding trajectory of the workpiece to be processed, collecting real-time friction stir welding data, and then adjusting the friction stir welding process of the workpiece to be processed based on the real-time friction stir welding data specifically includes: performing friction stir welding on the workpiece to be processed according to the friction stir welding process parameters and friction stir welding trajectory of the workpiece to be processed; collecting real-time welding pressure, real-time welding torque, and real-time welding resistance of the workpiece to be processed during the friction stir welding process to determine real-time friction stir welding data of the workpiece to be processed; The friction stir welding process of the workpiece to be processed is adjusted based on the real-time welding pressure, real-time welding torque and real-time welding resistance of the workpiece to be processed.

4. A friction stir welding and grinding processing method according to claim 3, characterized in that: The adjusting of the friction stir welding process of the workpiece to be processed based on the real-time welding pressure, real-time welding torque and real-time welding resistance of the workpiece to be processed specifically includes: The friction stir welding process parameters of the workpiece to be processed include: optimal welding pressure, optimal welding torque and optimal welding resistance; Obtaining a welding pressure threshold, a welding torque threshold, and a welding resistance threshold of the workpiece to be processed; Comparing the real-time welding pressure, the optimal welding pressure, and the welding pressure threshold to obtain a welding pressure comparison result, and adjusting the welding pressure depth during the friction stir welding process of the workpiece to be processed according to the welding pressure comparison result; Comparing the real-time welding torque, the optimal welding torque, and the welding torque threshold to obtain a welding torque comparison result, and adjusting the welding speed of the friction stir welding process of the workpiece to be processed according to the welding torque comparison result; The real-time welding resistance, the optimal welding resistance and the welding resistance threshold are compared to obtain a welding resistance comparison result, and the welding speed of the friction stir welding process of the workpiece to be processed is adjusted according to the welding resistance comparison result.

5. A friction stir welding and grinding processing method according to claim 4, characterized in that: The step of grinding the workpiece to be processed based on the grinding process parameters and grinding trajectory of the workpiece to be processed, collecting real-time grinding data, and then adjusting the grinding process of the workpiece to be processed according to the real-time grinding data to complete the grinding of the workpiece to be processed specifically includes: Grinding the workpiece to be processed according to the grinding process parameters and grinding trajectory of the workpiece to be processed; collecting real-time grinding pressure, real-time grinding torque, and real-time grinding resistance of the workpiece to be processed during the grinding process to determine real-time grinding data of the workpiece to be processed; Based on the real-time grinding pressure, real-time grinding torque and real-time grinding resistance of the workpiece to be machined, the grinding process of the workpiece to be machined is adjusted to complete the grinding of the workpiece to be machined.

6. A friction stir welding and grinding processing method according to claim 5, characterized in that: The step of adjusting the grinding process of the workpiece to be machined based on the real-time grinding pressure, real-time grinding torque, and real-time grinding resistance of the workpiece to be machined specifically includes: The grinding process parameters of the workpiece to be processed include: optimal grinding pressure, optimal grinding torque and optimal grinding resistance; Obtaining a grinding pressure threshold, a grinding torque threshold, and a grinding resistance threshold of the workpiece to be processed; Comparing the real-time grinding pressure, the optimal grinding pressure, and the grinding pressure threshold to obtain a grinding pressure comparison result, and adjusting the grinding pressure depth of the grinding process of the workpiece to be processed according to the grinding pressure comparison result; comparing the real-time grinding torque, the optimal grinding torque, and the grinding torque threshold to obtain a grinding torque comparison result, and adjusting the grinding speed of the grinding process of the workpiece to be processed according to the grinding torque comparison result; The real-time grinding resistance, the optimal grinding resistance and the grinding resistance threshold are compared to obtain a grinding resistance comparison result, and the grinding speed of the grinding process of the workpiece to be processed is adjusted according to the grinding resistance comparison result.

7. A robot-integrated friction stir welding and grinding device, characterized in that: A friction stir welding and grinding processing method suitable for use in any one of claims 1 to 6, comprising: a robotic device, a control system, and an electric spindle system; The drive control output terminal of the control system is electrically connected to the drive control input terminal of the robot device; the drive control output terminal of the control system is electrically connected to the drive control input terminal of the electric spindle system; the real-time processing data input terminal of the control system is wirelessly connected to the real-time processing data output terminal of the electric spindle system; the robot device is connected to the electric spindle system; The control system is used to obtain a workpiece to be processed, a friction stir welding neural network model, and a polishing neural network model; based on the butt surface trajectory of the workpiece to be processed, the friction stir welding trajectory and the polishing trajectory of the workpiece to be processed are obtained; based on the application scenario data of the workpiece to be processed, combined with the friction stir welding neural network model and the polishing neural network model, data deduction is performed on a preset process parameter database to obtain the friction stir welding process parameters and polishing process parameters of the workpiece to be processed; The control system is further configured to drive the robot device and the electric spindle system to perform friction stir welding on the workpiece to be processed based on the friction stir welding process parameters and friction stir welding trajectory of the workpiece to be processed; The control system is further configured to drive the robot device and the electric spindle system to grind the workpiece to be processed based on the grinding process parameters and grinding trajectory of the workpiece to be processed after completing the friction stir welding of the workpiece to be processed; The electric spindle system is used to collect real-time friction stir welding data, so that the control system drives the robot device and the electric spindle system to adjust the friction stir welding process of the workpiece to be processed based on the real-time friction stir welding data; The electric spindle system is also used to collect real-time grinding data, so that the control system drives the robot device and the electric spindle system to adjust the grinding process of the workpiece to be processed based on the real-time grinding data.

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