Intelligent welding system based on real-time temperature control monitoring and welding method thereof

Abstract

The invention is suitable for the technical field of intelligent welding, and provides an intelligent welding system based on real-time temperature control monitoring and a welding method thereof.The intelligent welding system comprises a process prediction module, a process execution module, a temperature sensing module, an analysis prediction module and an adjustment execution module; the process preconceiving module is used for generating process parameters and preconceiving trend data according to the workpiece data; the process execution module is used for welding according to the process parameters; the temperature sensing module is used for acquiring temperature data of a welding spot through infrared thermal imaging and a fiber bragg grating; the analysis and prediction module is used for analyzing the temperature data and the workpiece parameters, predicting the temperature trend of the welding spot and giving adjustment data; and the adjustment execution module is used for converting the adjustment data into adjusted process parameters. According to the device, the problems that fluctuation and delay are generated during regulation and control of a welding regulation and control system, and an irreversible coarsening phenomenon occurs due to the fact that the temperature of a microstructure layer is too high are solved, and the effects of predicting the temperature trend in advance and improving the dynamic adaptability are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent welding, and more specifically, to an intelligent welding system based on real-time temperature control monitoring and its welding method. Background Art

[0002] Welding temperature monitoring is a key link to ensure welding quality, and its monitoring accuracy directly affects the mechanical properties and reliability of welded joints. In traditional welding processes, temperature monitoring mainly relies on infrared thermometry with fixed parameters or thermocouple contact measurement methods. However, with the development trend of diversified welding materials and complex structures, obvious deficiencies have emerged in the dynamic adaptability of existing monitoring systems.

[0003] Currently, the welding temperature monitoring system mainly has three aspects of adaptability defects: First, the welding control system will generate fluctuations during regulation, and even cause greater damage than without regulation; Second, it takes a certain amount of time to adjust the temperature to the appropriate temperature after regulation; Third, at the microstructural level, when the welding peak temperature exceeds the critical value, the grains of the workpiece will undergo irreversible coarsening.

[0004] Therefore, an intelligent welding system based on real-time temperature control monitoring and its welding method are proposed to solve the above problems. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide an intelligent welding system based on real-time temperature control monitoring and its welding method that reduces the risk of false alarms and improves the overall dynamic adaptability.

[0006] To achieve the above purpose, the present invention provides the following technical solution: An intelligent welding system based on real-time temperature control monitoring, including a process anticipation module, a process execution module, a temperature perception module, an analysis and prediction module, and an adjustment execution module; the process anticipation module is used to generate process parameters and anticipated trend data according to workpiece data; the process execution module is used to perform welding according to the process parameters; the temperature perception module is used to collect temperature data of the solder joint through infrared thermal imaging and fiber Bragg grating; the analysis and prediction module is used to analyze the temperature data and workpiece parameters to predict the temperature trend of the solder joint and give adjustment data; the adjustment execution module is used to convert the adjustment data into adjusted process parameters and control the actuator to operate according to the adjusted process parameters.

[0007] By adopting the above technical solution, temperature collection is carried out by infrared thermal imaging and fiber Bragg grating. The two can complement each other, have better adaptability to complex environments, and can verify each other after measuring the temperature, avoiding inconsistent detected temperatures and reducing the risk of false alarms.

[0008] The present invention is further configured such that: the temperature sensing module includes a temperature acquisition unit and a temperature integration unit; the temperature acquisition unit is used to acquire a first temperature through infrared thermal imaging and a second temperature through a fiber Bragg grating; the temperature integration unit is used to generate temperature data based on the first temperature and the second temperature.

[0009] The present invention is further configured such that: the analysis and prediction module includes a trend prediction unit and a data comparison unit; the trend prediction unit is used to generate trend data based on the temperature data, and make a prediction based on the trend data and workpiece parameters to obtain predicted trend data; the data comparison unit is used to compare the predicted trend data and the expected trend data to generate adjustment data.

[0010] An intelligent welding method based on real-time temperature control monitoring, using an intelligent welding system based on real-time temperature control monitoring as described above, includes the following steps: S1. Generate process parameters and expected trend data according to workpiece data; S2. Perform welding according to the process parameters, and acquire a first temperature through infrared thermal imaging and a second temperature through a fiber Bragg grating; S4. Generate temperature data based on the first temperature and the second temperature; S51. Generate trend data based on the temperature data, and obtain predicted trend data through finite element analysis prediction based on the trend data and workpiece parameters; S52. Compare the predicted trend data and the expected trend data, and obtain adjustment data through a welding quality evaluation function; S6. Generate adjusted process parameters based on the adjustment data; S7. Control the actuator to operate according to the adjusted process parameters, and jump to S2 until the welding is completed.

[0011] The present invention is further configured such that: the formula of the real-time temperature field prediction model for finite element analysis is:

[0012] Wherein, is used to describe the change of temperature with time, is used to describe the diffusion of heat in space, ρ is the material density, c p is the specific heat capacity, T is the temperature data, t is time, k is the thermal conductivity, and q is the heat source intensity.

[0013] By adopting the above technical solutions, problems that will occur in actual processing can be foreseen in advance, and thus precise welding temperature control can be achieved through subsequent adjustments.

[0014] The present invention is further configured such that: the formula of the welding quality evaluation function is:

[0015] Among them, α is the peak temperature deviation weight coefficient, and △T max is the maximum temperature deviation, β is the temperature fluctuation weight coefficient, T is the temperature data, and T opt is to minimize the temperature deviation from the ideal value, γ is the microstructure change weight coefficient, and σ micro is the standard deviation of microstructure change.

[0016] By adopting the above technical solution, the welding quality evaluation function can be monitored from three aspects, and the data can be dynamically optimized and adjusted, thereby improving the overall dynamic adaptability.

[0017] The present invention is further set as: generating temperature data according to the first temperature and the second temperature means comparing the difference between the first temperature and the second temperature; If the difference is within the preset deviation, then the second temperature is used as the temperature data; If the difference is outside the preset deviation, then the average value of the first temperature and the second temperature is calculated, and the average value is used as the temperature data.

[0018] In summary, the present application includes at least one of the following beneficial technical effects: 1. Infrared thermal imaging and fiber Bragg grating are used for temperature acquisition. The two can complement each other, have better adaptability to complex environments, and can verify each other after measuring the temperature, avoiding inconsistent detected temperatures and reducing the risk of false alarms.

[0019] 2. Problems that will occur in actual processing can be predicted in advance, and then precise welding temperature control can be achieved through subsequent adjustments.

[0020] 3. The welding quality evaluation function can be monitored from three aspects, and the data can be dynamically optimized and adjusted, thereby improving the overall dynamic adaptability. Brief Description of the Drawings

[0021] Figure 1 It is a schematic relationship diagram of an intelligent welding system based on real-time temperature control monitoring of the present invention.

[0022] Figure 2 It is a schematic diagram of steps S1-S4 in an intelligent welding method based on real-time temperature control monitoring of the present invention.

[0023] Figure 3 It is a schematic diagram of steps S4-S7 in an intelligent welding method based on real-time temperature control monitoring of the present invention. Detailed Description of the Invention

[0024] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other. The present invention will be described in detail below with reference to the drawings and in combination with the embodiments.

[0025] It should be pointed out that, unless otherwise specified, all technical and scientific terms used in this application have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs.

[0026] Please refer to Figure 1-2 , the present invention provides the following technical solutions: Embodiment 1, refer to Figure 1 , an intelligent welding system based on real-time temperature control monitoring, including a process anticipation module, a process execution module, a temperature perception module, an analysis and prediction module, and an adjustment and execution module; The process anticipation module is used to generate process parameters and anticipated trend data according to workpiece data; Among them, the workpiece parameters include workpiece material density, workpiece specific heat capacity, etc., the process parameters include welding torch temperature, driver driving parameters, driver movement path, etc., and the anticipated trend data is mainly the prediction of the solder joint temperature trend.

[0027] The process execution module is used to perform welding according to the process parameters; The temperature perception module is used to collect the temperature data of the solder joint through infrared thermal imaging and fiber Bragg grating; Through the above-mentioned infrared thermal imaging and fiber Bragg grating for temperature collection, the two can complement each other and have better adaptability to complex environments. Infrared thermal imaging can quickly obtain the temperature distribution on the surface of the workpiece and is suitable for large-area scanning; the fiber Bragg grating can directly contact the object to be measured and provide high-precision temperature data for a single point; the combination of the two can achieve point-plane collaborative monitoring, and can verify each other after measuring the temperature, avoiding inconsistent detected temperatures and reducing the risk of false alarms.

[0028] The temperature perception module includes a temperature collection unit and a temperature integration unit; The temperature collection unit is used to collect the first temperature through infrared thermal imaging and the second temperature through the fiber Bragg grating; The temperature integration unit is used to generate temperature data according to the first temperature and the second temperature; Among them, the first temperature collected by infrared thermal imaging is mainly the temperature data of large-area scanning of the solder joint and its vicinity, and the second temperature collected by the fiber Bragg grating is mainly the high-precision temperature data of the solder joint position. When integrating, the second temperature is the main one and the first temperature is the auxiliary one. If the measured temperature error is within the preset range, the second temperature is used as the integrated temperature data. If the measured temperature error is outside the preset range, the operator is reminded to check the infrared thermal imaging and the fiber Bragg grating.

[0029] The analysis and prediction module is used to analyze temperature data and workpiece parameters to predict the temperature trend of the solder joint and give adjustment data; The analysis and prediction module includes a trend prediction unit and a data comparison unit; The trend prediction unit is used to generate trend data based on the temperature data, and make a prediction based on the trend data and workpiece parameters to obtain predicted trend data; The data comparison unit is used to generate adjustment data by comparing the predicted trend data with the expected trend data.

[0030] The adjustment execution module is used to convert the adjustment data into adjusted process parameters and control the actuator to operate according to the adjusted process parameters.

[0031] Through the above system, the temperature during welding processing is monitored, and the temperature change in the next period of time, that is, the predicted trend data, is predicted based on the temperature and workpiece parameters. The adjustment data is generated based on the predicted trend data and the expected temperature change, that is, the expected trend data, so as to solve the problem of welding temperature control.

[0032] Example 2, refer to Figure 2 , an intelligent welding method based on real-time temperature control monitoring, using the above intelligent welding system based on real-time temperature control monitoring, including the following steps: S1. Generate process parameters and expected trend data according to workpiece data; S2. Perform welding according to the process parameters, collect the first temperature through infrared thermal imaging, and collect the second temperature through fiber Bragg grating; S4. Generate temperature data based on the first temperature and the second temperature; S5. Predict the temperature trend of the solder joint based on the temperature data and workpiece parameters, and give adjustment data; The more specific steps of S5 are: S51. Generate trend data based on the temperature data and time, and predict the predicted trend data based on the trend data and workpiece parameters; Among them, the predicted trend data is calculated by the real-time temperature field prediction model of finite element analysis, and its formula is:

[0033] Among them, is used to describe the change of temperature with time, is used to describe the diffusion of heat in space, ρ is the material density, c p is the specific heat capacity, T is the temperature data, t is the time, k is the thermal conductivity, and q is the heat source intensity; For example, taking oxygen-free copper as the workpiece, its material density ρ = 8960 kg / m 3 , specific heat capacity cp = 385 J / (kg·K), the thermal conductivity k = 398 W / (m·K), and the heat source intensity q = 1.5×10 6 W / m 3 . At the initial stage of welding start-up, assuming that the local temperature distribution is approximately uniform (▽T≈0) and ignoring the spatial heat diffusion term, the heat conduction equation can be simplified as follows:

[0034] Substituting the data, we get: ; According to the above formula, within 0.005 seconds of applying the heat source, the theoretical temperature rise in the local area is approximately 0.00218℃; The above results can be corrected by Kalman filtering combined with sensor data to achieve sub-millimeter spatial resolution; By predicting the temperature change through this model, problems in actual processing can be foreseen in advance, and then precise welding temperature control can be achieved through subsequent adjustments.

[0035] S52. Compare the predicted trend data and the expected trend data to generate adjustment data; Among them, the adjustment data is obtained through a welding quality evaluation function, and its formula is:

[0036] Among them, α is the peak temperature deviation weight coefficient, △T max is the maximum temperature deviation, β is the temperature fluctuation weight coefficient, T is the temperature data, T opt is the minimum temperature deviation from the ideal value, γ is the microstructure change weight coefficient, σ micro is the standard deviation of microstructure change; For example, assuming that within 0.02 s, the minimum temperature deviation from the ideal value T opt = 1700℃, the maximum temperature T max = 1760℃, the maximum temperature deviation △T max = 60℃, the peak temperature deviation weight coefficient α = 1, the temperature fluctuation weight coefficient β = 0.01, and without considering the microstructure change, substituting into the welding quality evaluation function, we get:

[0037] This result can also be combined with a genetic algorithm to output the best parameter combination to achieve further optimization effects.

[0038] By monitoring or calculating the cumulative error of the maximum temperature deviation, the minimum temperature deviation from the ideal value T opt and the microstructure uniformity through the welding quality evaluation function, the adjustment data can be dynamically optimized, thereby improving the overall dynamic adaptability.

[0039] S6. Generate adjusted process parameters based on the adjustment data; S7. Control the actuator to operate according to the adjusted process parameters, and jump to S2 until the welding is completed.

[0040] Embodiment 3. An intelligent welding method based on real-time temperature control monitoring according to Embodiment 2, wherein the generation of temperature data according to the first temperature and the second temperature in S4 can be obtained by the following method: Compare the difference between the first temperature and the second temperature; If the difference is within the preset deviation, use the second temperature as the temperature data; If the difference is outside the preset deviation, calculate the average value of the first temperature and the second temperature, and use the average value as the temperature data; For example, if the preset deviation is 1 °C, the first temperature is 100 °C, and the second temperature is 100.5 °C, then the second temperature 100.5 °C is used as the temperature data at this time. If the second temperature is 102 °C, then the temperature data at this time is 101 °C.

[0041] Accurately measure the temperature data in this way to improve the reliability and accuracy of the temperature data.

[0042] Obviously, the above-described embodiments are only a part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

Claims

1. An intelligent welding system based on real-time temperature control monitoring, characterized in that: Including: A process anticipation module, configured to generate process parameters and anticipated trend data according to workpiece data; A process execution module, configured to perform welding according to the process parameters; A temperature sensing module, configured to collect temperature data of the solder joints through infrared thermal imaging and fiber Bragg grating; An analysis and prediction module, configured to analyze the temperature data, the anticipated trend data, and the workpiece parameters to predict the temperature trend of the solder joints and give adjustment data; And An adjustment execution module, configured to convert the adjustment data into adjusted process parameters and control the actuator to operate according to the adjusted process parameters.

2. An intelligent welding system based on real-time temperature control monitoring according to claim 1, wherein: The temperature sensing module includes a temperature acquisition unit and a temperature integration unit; The temperature acquisition unit is configured to collect a first temperature through infrared thermal imaging and a second temperature through fiber Bragg grating; The temperature integration unit is configured to generate temperature data according to the first temperature and the second temperature.

3. An intelligent welding system based on real-time temperature control monitoring according to claim 2, wherein: The analysis and prediction module includes a trend prediction unit and a data comparison unit; The trend prediction unit is configured to generate trend data according to the temperature data and make a prediction according to the trend data and the workpiece parameters to obtain predicted trend data; The data comparison unit is configured to compare the predicted trend data and the anticipated trend data to generate adjustment data.

4. An intelligent welding method based on real-time temperature control monitoring, using an intelligent welding system based on real-time temperature control monitoring as described in claim 3, characterized in that, Including the following steps: S1. Generate process parameters and anticipated trend data according to workpiece data; S2. Perform welding according to the process parameters, collect a first temperature through infrared thermal imaging, and collect a second temperature through fiber Bragg grating; S4. Generate temperature data according to the first temperature and the second temperature; S5. Predict the temperature trend of the solder joints according to the temperature data and the workpiece parameters and give adjustment data; S6. Generate adjusted process parameters according to the adjustment data; S7. Control the actuator to operate according to the adjusted process parameters and jump to S2 until the welding is completed.

5. The intelligent welding method based on real-time temperature control monitoring according to claim 4, characterized in that, The more specific steps of S5 are: S51. Generate trend data according to the temperature data and make a prediction according to the trend data and the workpiece parameters to obtain predicted trend data; S52. Compare the predicted trend data and the anticipated trend data to generate adjustment data.

6. The intelligent welding method based on real-time temperature control monitoring according to claim 4, wherein, In S4, the generating temperature data according to the first temperature and the second temperature: means comparing the difference between the first temperature and the second temperature; If the difference is within the preset deviation, then use the second temperature as the temperature data; If the difference is outside the preset deviation, then calculate the average value of the first temperature and the second temperature and use the average value as the temperature data.

7. An intelligent welding method based on real-time temperature control monitoring according to claim 5, characterized in that, In S51, the predicted trend data: is calculated through a real-time temperature field prediction model of finite element analysis.

8. An intelligent welding method based on real-time temperature control monitoring according to claim 7, characterized in that, The formula of the real-time temperature field prediction model of finite element analysis is: ； Among them, is used to describe the change of temperature with time, is used to describe the diffusion of heat in space, ρ is the material density, c p is the specific heat capacity, T is the temperature data, t is the time, k is the thermal conductivity, and q is the heat source intensity.

9. The intelligent welding method based on real-time temperature control monitoring according to claim 8, characterized in that, In S52, the adjustment data: is obtained through a welding quality evaluation function.

10. The intelligent welding method based on real-time temperature control monitoring according to claim 9, characterized in that, The formula of the welding quality evaluation function is: ； where α is the peak temperature deviation weight coefficient, △T max is the maximum temperature deviation, β is the temperature fluctuation weight coefficient, T is the temperature data, T opt is to minimize the temperature deviation from the ideal value, γ is the microstructure change weight coefficient, σ micro is the standard deviation of microstructure change.

Images