Vacuum laser welding online monitoring method

Abstract

The application discloses a kind of vacuum laser welding online monitoring methods, it collects the secondary current signal of vacuum laser welding, and analyzes the correlation law of secondary current signal and weld forming, penetration, and then based on secondary current signal, the weld appearance of vacuum laser welding is actively controlled, finally effectively realizes the online closed-loop monitoring of vacuum laser welding process.

Description

Technical Field

[0001] The invention relates to an online monitoring method for vacuum laser welding. Background Art

[0002] Vacuum laser welding is a welding technology that uses a laser beam to heat, melt, and join workpieces in a vacuum environment at high energy density. Because manual intervention is difficult during vacuum laser welding, fully closed-loop automatic control is essential to address process defects such as poor forming. Research in this area has been extensively conducted domestically and internationally. Osaka University in Japan used in-situ high-speed X-ray imaging to observe the dynamic behavior of the keyhole and molten pool during high-power vacuum laser welding, as well as the defect formation process. High-speed imaging also enabled observation of the flow behavior of the molten pool surface. Harbin Institute of Technology and Shanghai Jiao Tong University in China, in collaboration with Osaka University, have also conducted experimental observations of molten pool flow behavior. However, the slow processing speed of in-situ X-ray experiments or high-speed imaging makes it difficult to achieve closed-loop monitoring of the vacuum laser welding process due to the slow image signal processing speed. Furthermore, the presence of metal vapor in the vacuum chamber during vacuum laser welding makes online monitoring of the welding process by high-speed photography and other methods susceptible to vapor obstruction and contamination, making online visual monitoring of the vacuum laser welding process challenging. Therefore, the existing vacuum laser welding monitoring methods are difficult to effectively realize online closed-loop monitoring of the vacuum laser welding process and are difficult to meet the needs of real-time adjustment of process parameters. Summary of the Invention

[0003] To address the shortcomings of the prior art, the present invention provides an online monitoring method for vacuum laser welding, comprising the following steps: 1) collecting secondary current signals of vacuum laser welding; 2) analyzing the correlation between the secondary current signals and weld formation and penetration; and 3) actively controlling the weld morphology of vacuum laser welding based on the secondary current signals.

[0004] Preferably, the vacuum laser welding online monitoring method of the present invention comprises the following steps:

[0005] 1) Collect the secondary current signal of the vacuum laser welding process;

[0006] 2) Based on the collected secondary current signals, an orthogonal experimental design method was used to conduct multi-process vacuum laser welding experiments. The correlation between process parameters and weld formation and penetration was analyzed. The Taguchi algorithm was applied to establish the intrinsic relationship between process parameters, weld formation and penetration. The correlation between secondary current and process parameters was analyzed and established based on the signal collection electrode, signal transmission and amplification system, and computer online real-time analysis technology. Based on the above analysis results, the correlation law between process parameters, secondary current, and weld formation was obtained.

[0007] 3) Using a support vector machine, linear and nonlinear regression relationships are set up to compare and analyze the secondary current characteristic values ​​and weld characteristic maps under specific process parameters, and the characteristics of the weld morphology and penetration depth corresponding to the process parameters of the electrical signal are obtained, and the characteristics are placed in the supervision area for supervision and classification; based on the support vector machine, the original pattern space is mapped to a high-dimensional feature space to determine whether the weld formation and penetration depth corresponding to the secondary current signal are ideal. If not, the optimal classification hyperplane is found in the feature space, and feedback is used to control the changes in the laser parameters to stabilize the welding process again, thereby achieving active control of the weld morphology.

[0008] For more specific details of the vacuum laser welding online monitoring method of the present invention, please refer to the specific implementation method.

[0009] The advantages and beneficial effects of the present invention are: providing an online monitoring method for vacuum laser welding, which collects the secondary current signal of vacuum laser welding, analyzes the correlation between the secondary current signal and weld formation and penetration depth, and then actively controls the weld morphology of vacuum laser welding based on the secondary current signal, and finally effectively realizes online closed-loop monitoring of the vacuum laser welding process. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0011] The following embodiments are further described in conjunction with the accompanying drawings and examples. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0012] The technical solutions specifically implemented in the present invention are as follows:

[0013] like Figure 1 As shown, the present invention provides a method for online monitoring of vacuum laser welding weld formation based on secondary current signals, comprising the following steps:

[0014] 1) Collecting the secondary current signal of the vacuum laser welding process; During vacuum laser welding, in a vacuum chamber, the metal material is irradiated by laser, and a large number of free electrons are generated in the metal vapor and plasma formed by the evaporation of the material. These free electrons are secondary electrons, and the current formed by the secondary electrons is the secondary current. A thin copper plate is set as a secondary current collector in the vacuum chamber of the vacuum laser welding process, and the thin copper plate is connected to the top of the welding plate. A potential difference is generated between the thin copper plate and the workpiece, and an external load resistor is connected to form a loop. The thin copper plate outputs the collected secondary current to the outside, generating a voltage drop on the load resistor, thereby realizing real-time collection of the secondary current signal during the welding process;

[0015] 2) Based on the collected secondary current signals, an orthogonal experimental design method was used to conduct multi-process vacuum laser welding experiments. The correlation between process parameters and weld formation and penetration was analyzed. The Taguchi algorithm was applied to establish the intrinsic relationship between process parameters, weld formation and penetration. The correlation between secondary current and process parameters was analyzed and established based on the signal collection electrode, signal transmission and amplification system, and computer online real-time analysis technology. Based on the above analysis results, the correlation law between process parameters, secondary current, and weld formation was obtained.

[0016] 3) Using a support vector machine, linear and nonlinear regression relationships are set up to compare and analyze the secondary current characteristic values ​​and weld characteristic maps under specific process parameters, and the characteristics of the weld morphology and penetration depth corresponding to the process parameters of the electrical signal are obtained, and the characteristics are placed in the supervision area for supervision and classification; based on the support vector machine, the original pattern space is mapped to a high-dimensional feature space to determine whether the weld formation and penetration depth corresponding to the secondary current signal are ideal. If not, the optimal classification hyperplane is found in the feature space, and feedback is used to control the changes in the laser parameters to stabilize the welding process again, thereby achieving active control of the weld morphology.

[0017] The vacuum laser welding online monitoring method of the present invention specifically comprises the following steps:

[0018] 1) Collect secondary current signals of vacuum laser welding

[0019] During vacuum laser welding, in a vacuum chamber, when the metal material is irradiated by laser, a large number of free electrons are generated in the metal vapor and plasma formed by the evaporation of the material. These free electrons are secondary electrons, and the current formed by the secondary electrons is the secondary current. A thin copper plate is set as a secondary current collector in the vacuum chamber of the vacuum laser welding. The thin copper plate is connected to the top of the welding plate, and a potential difference is generated between the thin copper plate and the workpiece. An external load resistor is connected to form a loop. The thin copper plate outputs the collected secondary current to the outside, generating a voltage drop on the load resistor, thereby realizing real-time collection of the secondary current signal during the welding process. The collected secondary current signal is transmitted and amplified. The amplified secondary current signal is subjected to digital processing such as Fourier transform. The digitally processed secondary current signal is displayed online, and the signal spectrum and characteristic values ​​are analyzed in real time, thereby completing the detection of the secondary current signal during the vacuum laser welding process.

[0020] Based on a high-power laser welding platform, a detection device with high light transmittance, high temperature resistance, high voltage resistance, and secondary current signal collection is used. The use of high-transmittance quartz glass improves the device's penetration of laser light. A 15mm thick-walled 304 stainless steel cavity is used to form the overall structure of the detection device, improving the device's high-pressure and high-temperature resistance, while also improving the device's electrical conductivity. A flexible hinge constraint design is used to achieve miniaturization and lightweighting of the laser welding device. The device's airtightness requirements are met by using high-temperature resistant annular sealing strips. A detection device pressure regulation system integrating a vacuum pump, pressure reducing valve, shut-off valve, vacuum valve, exhaust valve, and vacuum gauge is used to adjust the internal pressure of the closed cavity during the welding process.

[0021] 2) Analyze the correlation between secondary current signal and weld formation and penetration

[0022] Based on the detected secondary current signal of vacuum laser welding, the orthogonal experimental design method is used to carry out vacuum laser welding experiments under multiple process conditions (including ambient pressure, laser power, welding speed, defocusing amount, collector distance). The ultra-depth of field three-dimensional microscope, SEM, and metallographic corrosion technology are used to obtain the weld surface morphology, weld cross-sectional morphology and deep penetration map under different processes, analyze the correlation between process parameters and weld formation and penetration, establish the intrinsic connection between process parameters and weld formation and penetration, and establish a welding process control method; based on the signal collector, signal transmission and amplification system and computer online real-time analysis technology, the original input electrical signal is decomposed into the eigenmode function through empirical mode decomposition and time domain analysis is performed to obtain the current size. The formula is as follows:

[0023]

[0024] The relationship between the secondary current and process parameters was analyzed to obtain a stable process control method for the current signal generated during vacuum laser welding. Based on the current signal control method and the relationship between weld morphology, penetration and process parameters, the current signal changes under each process parameter were analyzed to obtain the correlation between the current signal and weld formation and penetration.

[0025] 3) Active control of weld morphology in vacuum laser welding based on secondary current signal

[0026] A vacuum laser welding experiment was carried out, combining semiconductor laser backlight light source, narrowband filtering, and image processing technology. The collected electrical signals were filtered and amplified by an op amp, compared with cluttered signals by a comparator, and shaped by a shaper. The main frequency of the signal and the energy ratio of the main frequency area were selected as feature parameters to characterize the signal, remove welding interference signals, and obtain specific current signal output values ​​based on the feature parameters. A support vector machine was used to set linear and nonlinear regression relationships, and the secondary current signal feature values ​​and weld feature maps under specific process parameters were compared and analyzed to obtain the weld morphology and penetration characteristics of the process parameters corresponding to the electrical signal. The features were placed in the supervision area for supervision and classification. The original pattern space was mapped to a high-dimensional feature space based on the support vector machine to determine whether the weld formation and penetration corresponding to the current signal were ideal. If not, the optimal classification hyperplane was found in the feature space, and the changes in the laser parameters were fed back to control the welding process to stabilize the welding process again, thereby achieving active control of the weld morphology.

[0027] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A vacuum laser welding online monitoring method, characterized in that: The following steps are involved: 1) Collect the secondary current signal of the vacuum laser welding process; a large number of free electrons are generated inside the metal vapor and plasma formed by material evaporation. These free electrons are called secondary electrons, and the current generated by the secondary electrons is called secondary current; 2) Analyze the correlation between the secondary current signal and weld formation and penetration depth. Based on the collected secondary current signals, an orthogonal experimental design method was used to conduct multi-process vacuum laser welding experiments. The correlation between process parameters and weld formation and penetration depth was analyzed. The Taguchi algorithm was applied to establish the intrinsic relationship between process parameters, weld formation and penetration depth. Based on the signal collector, signal transmission and amplification system, and computer online real-time analysis technology, the correlation between the secondary current and process parameters is analyzed and established; based on the above analysis results, the correlation between the process parameters, secondary current, and weld formation is obtained; 3) Actively control the weld morphology of vacuum laser welding based on the secondary current signal; use support vector machine to set linear and nonlinear regression relationships, compare and analyze the secondary current characteristic values ​​and weld characteristic maps under specific process parameters, and obtain the characteristics of weld morphology and penetration depth of the process parameters corresponding to the electrical signal, and put them into the supervision area for supervision and classification; map the original pattern space to the high-dimensional feature space based on the support vector machine to determine whether the weld formation and penetration depth corresponding to the secondary current signal are ideal. If not, find the optimal classification hyperplane in the feature space, and feedback control the changes in laser parameters to stabilize the welding process again, thereby achieving active control of the weld morphology.

2. The vacuum laser welding online monitoring method according to claim 1, characterized in that: The specific steps include: 1) Collect secondary current signals of vacuum laser welding A thin copper plate serving as a secondary current collector is placed in the vacuum chamber of a vacuum laser welding process. The plate is connected to the top of the welding plate, generating a potential difference between the plate and the workpiece. An external load resistor is connected to form a loop. The plate outputs the collected secondary current, generating a voltage drop across the load resistor, enabling real-time collection of the secondary current signal during the welding process. The collected secondary current signal is then transmitted and amplified. Performing Fourier transform digital processing on the amplified secondary current signal; The digitally processed secondary current signal is displayed online and the signal spectrum and characteristic values ​​are analyzed in real time, ultimately completing the detection of the secondary current signal during the vacuum laser welding process. 2) Analyze the correlation between secondary current signal and weld formation and penetration Based on the detected secondary current signal of vacuum laser welding, an orthogonal experimental design method was used to carry out vacuum laser welding experiments under multiple process conditions. Ultra-depth-of-field three-dimensional microscopy, SEM, and metallographic corrosion technology were used to obtain the weld surface morphology, weld cross-sectional morphology, and deep penetration maps under different processes. The correlation between process parameters and weld formation and penetration was analyzed, and the intrinsic connection between process parameters and weld formation and penetration was established, and a welding process control method was established. Based on the signal collecting electrode, signal transmission and amplification system and computer online real-time analysis technology, the original input electrical signal is decomposed into the eigenmode function through empirical mode decomposition and time domain analysis is performed to obtain the current magnitude; Analyze the relationship between secondary current and process parameters to obtain a stable process control method for the current signal generated during vacuum laser welding; Based on the current signal control method and the correlation between weld morphology, penetration and process parameters, the current signal changes under each process parameter are analyzed to obtain the correlation law between the current signal and weld formation and penetration; 3) Active control of weld morphology in vacuum laser welding based on secondary current signal Conduct vacuum laser welding experiments, combining semiconductor laser backlight, narrowband filtering, and image processing technology. The collected electrical signals are filtered and amplified by an op amp, compared with clutter signals by a comparator, and shaped by a shaper. The signal's dominant frequency and dominant frequency region energy ratio are selected as characteristic parameters to characterize the signal, remove welding interference signals, and extract specific current signal output values ​​based on the characteristic parameters. Using support vector machines, linear and nonlinear regression relationships are set up, and the characteristic values ​​of secondary current signals under specific process parameters and weld characteristic maps are compared and analyzed to obtain the characteristics of weld morphology and penetration depth of process parameters corresponding to the electrical signals. The characteristics are placed in the supervision area for supervision and classification. Based on the support vector machine, the original pattern space is mapped to a high-dimensional feature space to determine whether the weld formation and penetration depth corresponding to the current signal are ideal. If not, the optimal classification hyperplane is found in the feature space, and feedback is used to control the changes in laser parameters to stabilize the welding process again, thereby achieving active control of the weld morphology.

3. The vacuum laser welding online monitoring method according to claim 2, characterized in that: In step 1), based on a high-power laser welding platform, a detection device with high light transmittance, high temperature resistance, high voltage resistance, and secondary current signal collection is used.

4. The vacuum laser welding online monitoring method according to claim 3, characterized in that: In step 1), high-transmittance quartz glass is used to improve the penetration of the device to laser light.

5. The vacuum laser welding online monitoring method according to claim 4, characterized in that: In step 1), a 15 mm thick-walled 304 stainless steel cavity is used to form the overall structure of the detection device, thereby improving the device's resistance to high pressure and high temperature, and also improving the device's electrical conductivity.

6. The vacuum laser welding online monitoring method according to claim 5, characterized in that: In step 1), a flexible hinge constraint design is adopted to achieve miniaturization and lightweighting of the laser welding device.

7. The vacuum laser welding online monitoring method according to claim 6, characterized in that: In step 1), the airtightness requirement of the device is achieved by using a high-temperature resistant annular sealing strip.

8. The vacuum laser welding online monitoring method according to claim 7, characterized in that: In step 1), a pressure regulating system of a detection device integrating a vacuum pump, a pressure reducing valve, a stop valve, a vacuum valve, an exhaust valve, and a vacuum gauge is used to adjust the internal pressure of the closed cavity during the welding process.

9. The vacuum laser welding online monitoring method according to claim 2, characterized in that: In step 2), the multiple process conditions include ambient pressure, laser power, welding speed, defocusing amount, and collector distance.

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