Friction stir welding control method and system based on temperature dynamic predictive regulation

Abstract

The invention provides a friction stir welding control method and system based on temperature dynamic prediction regulation and control, and relates to the technical field of friction stir welding. The method comprises the steps that the temperature of a current welding area corresponding to the current position of a stirring head is collected in real time; based on the current welding area temperature, the moving speed of the stirring head and the preset welding area length, the corresponding predicted temperature value when the stirring head reaches the next welding area is predicted; a target rotation speed is calculated based on the predicted temperature value and the target temperature range, so that the temperature of the next welding area is within the target temperature range when the stirring head reaches the next welding area; according to the current rotating speed of the stirring head, the target rotating speed and the moving speed of the stirring head, the rotating speed change rate from the current rotating speed to the target rotating speed is calculated, and the rotating speed of the stirring head is controlled. By implementing the method, the temperature fluctuation of the welding area can be reduced, and the precision and stability of temperature control are improved, so that the welding quality is improved.

Description

Technical Field

[0001] The present application relates to the technical field of friction stir welding, and in particular, to a friction stir welding control method and system based on temperature dynamic prediction and regulation. Background Art

[0002] As a solid-phase connection process technology, friction stir welding has been widely used in the fields of aerospace, rail transit, and automobile manufacturing in recent years. This process realizes the high-quality connection of metal materials by friction heating and plastic stirring of the workpiece with a stirring head that rotates at high speed and moves along the weld seam. The heat input and temperature distribution during the welding process directly affect the mechanical properties and forming quality of the weld seam. Therefore, how to effectively control the temperature during the welding process has always been one of the core issues concerned in the field of friction stir welding.

[0003] In related technologies, to control the temperature of the welding area, temperature sensors are usually used to collect the temperature of the current position of the stirring head or the welding area in real time, and according to the collected temperature feedback signal, the rotation speed of the stirring head or the welding moving speed is directly adjusted to keep the temperature of the welding area within a predetermined range as much as possible. Existing systems mostly adopt a closed-loop feedback control method based on the on-site temperature signal, that is, according to the deviation between the current temperature and the target temperature, using conventional control algorithms such as proportional-integral-differential (PID), the parameters of the stirring head are adjusted in real time to meet the basic requirements of the heat input during the welding process.

[0004] However, due to the inertia of the movement of the stirring head and the lag of heat transfer, when related technologies rely on the current temperature signal to adjust parameters, it is easy to have a response lag, resulting in an increase in the temperature fluctuation of the welding area. Under working conditions with a high welding speed or significant changes in the thermal physical properties of materials, related technologies are prone to cause temperature control overshoot or hysteresis, thereby affecting the weld forming quality and tissue uniformity, and it is difficult to meet the higher requirements for fine dynamic control of the welding temperature. Summary of the Invention

[0005] The present application provides a friction stir welding control method and system based on temperature dynamic prediction and regulation, which are used to address the problem that it is difficult to finely and stably regulate the temperature of the welding area due to thermal inertia and control lag in related technologies under complex working conditions.

[0006] In a first aspect, the present application provides a friction stir welding control method based on temperature dynamic prediction and regulation, which is applied to a friction stir welding control system. The method includes: Real-time collecting the current welding area temperature corresponding to the current position of the stirring head through a temperature sensor arranged in the welding area; Predict a predicted temperature value corresponding to the next welding area when the stirring head reaches the next welding area based on the temperature of the current welding area, the moving speed of the stirring head, and a preset length of the welding area; Calculate a target rotation speed based on the predicted temperature value and a target temperature range corresponding to the next welding area, so that the temperature of the next welding area is within the target temperature range when the stirring head reaches the next welding area; Calculate a rotation speed change rate from the current rotation speed to the target rotation speed according to the current rotation speed of the stirring head, the target rotation speed, and the moving speed of the stirring head; Control the rotation speed of the stirring head according to the rotation speed change rate, so that the stirring head reaches the target rotation speed when moving to the next welding area.

[0007] Through the above embodiments, the system collects the temperature of the current position of the stirring head in real time through a temperature sensor arranged in the welding area, combines the moving speed of the stirring head and the preset length of the welding area, predicts the temperature when the stirring head reaches the next welding area, and dynamically calculates and adjusts the rotation speed of the stirring head based on the predicted temperature and the target temperature range. Compared with the traditional closed-loop control method that only relies on the current temperature feedback, this method predicts the temperature change trend in advance and combines the motion parameters of the stirring head for feedforward control to address the slow response problem caused by thermal inertia and temperature control hysteresis. It can reduce the temperature fluctuation in the welding area, improve the accuracy and stability of temperature control, thereby enhancing the uniformity of the weld formation and the welding quality, and meeting the higher requirements for the dynamic fine control of heat input in friction stir welding.

[0008] In some embodiments, before the step of collecting the temperature of the current welding area corresponding to the current position of the stirring head in real time through a temperature sensor arranged in the welding area, it further includes: Collect the temperature change data of the welding area under different combinations of welding materials, welding thicknesses, and welding conditions to obtain a historical temperature database; Screen at least one set of target historical temperature data with the highest similarity to the current welding condition from the historical temperature database; Calculate a predicted temperature correction value based on the target historical temperature data and the temperature of the current welding area, and the predicted temperature correction value is used to correct the predicted temperature value.

[0009] Through the above embodiments, before real-time temperature acquisition, the system first acquires and establishes a historical temperature database under different welding materials, thicknesses, and working conditions, and based on this, screens the most similar historical temperature data under the current working conditions to correct the predicted temperature. This technical feature enables the system to not only rely on existing real-time measurements and parameter calculations when predicting temperature, but also combine historical experience data to further compensate for possible deviations in the prediction results. It can improve the accuracy and robustness of temperature prediction, reduce the impact of material or working condition fluctuations on the accuracy of the prediction model, and make the welding temperature control more precise and reliable.

[0010] In some embodiments, before the step of using a temperature sensor disposed in the welding area to acquire the current welding area temperature corresponding to the current position of the stirring head in real time, it further includes: Dispose at least one auxiliary temperature sensor in the moving direction of the stirring head in the welding path; Use the auxiliary temperature sensor to acquire auxiliary temperature data of the welding area in front of the stirring head in real time; Based on the auxiliary temperature data and the moving speed of the stirring head, calculate the temperature gradient between the current position of the stirring head and the next welding area; Calculate a feedforward compensation value based on the temperature gradient, and the feedforward compensation value is used to correct the predicted temperature value.

[0011] Through the above embodiments, the system acquires auxiliary temperature data of the welding area in front of the stirring head in real time, and based on this data and the moving speed of the stirring head, calculates the temperature gradient between the current position and the next area, and then obtains a feedforward compensation value to correct the predicted temperature. This method makes the temperature prediction not only limited to the current position of the stirring head, but also able to sense the temperature change trend in the front area in advance. Through the feedforward compensation mechanism, it can more effectively cope with complex situations such as local temperature changes and uneven heat diffusion of materials, improve the real-time performance and forward-looking nature of dynamic temperature prediction, effectively reduce temperature overshoot or lag phenomena, further enhance the stability of welding temperature control, and optimize the weld forming effect.

[0012] In some embodiments, before the step of using a temperature sensor disposed in the welding area to acquire the current welding area temperature corresponding to the current position of the stirring head in real time, it further includes: Before starting welding, obtain the geometric features of the welding path by scanning a predetermined welding area; Identify feature areas in the welding path based on the geometric features, and the feature areas include path curvature change areas and thickness change areas; Based on the feature areas and the geometric features of the welding path, pre-calculate temperature correction coefficients corresponding to the positions of the feature areas, and the temperature correction coefficients are used to correct the predicted temperature values.

[0013] Through the above embodiments, before the welding starts, the system obtains the geometric features of the welding path by scanning the predetermined welding area, and identifies the feature areas in the path (such as the curvature change area and the thickness change area), and introduces a targeted temperature correction coefficient when predicting the temperature. This method enables the system to pre-adjust the temperature prediction model for different geometric feature areas in the welding path, fully considering the influence of geometric factors on heat conduction and distribution. By introducing a special temperature correction in the path feature area, the adaptability and accuracy of temperature control can be improved, avoiding abnormal temperature fluctuations caused by path geometric changes, and ensuring the forming consistency and welding quality of the weld under complex paths.

[0014] In some embodiments, after the step of predicting the predicted temperature value corresponding to the next welding area when the stir head reaches the next welding area based on the temperature of the current welding area, the moving speed of the stir head, and the preset length of the welding area, the method further includes: Determine whether the current welding condition is a feature area of the welding path; If the current welding condition is a feature area, select the corresponding temperature correction coefficient to correct the predicted temperature value; If the current welding condition is not a feature area, determine whether the similarity between the current welding condition and the historical temperature data is greater than a preset similarity threshold; If so, select the predicted temperature correction value to correct the predicted temperature value; If not, select the feedforward compensation value to correct the predicted temperature value.

[0015] Through the above embodiments, when the system determines that the current welding condition is in a feature area, it selects the temperature correction coefficient to correct the predicted temperature; otherwise, it selects the predicted temperature correction value or the feedforward compensation value according to the similarity with the historical temperature data. This method realizes the intelligent fusion of multi-source correction information, dynamically switches the optimal correction method according to the actual working conditions, can reduce the temperature prediction error to the greatest extent, improve the adaptive ability of the temperature control system, keep the welding temperature within a reasonable range all the time, and thus ensure the high consistency and high quality of the welding process.

[0016] In some embodiments, before the step of calculating the target rotation speed based on the predicted temperature value and the target temperature range corresponding to the next welding area, so that the temperature of the next welding area is within the target temperature range when the stir head reaches the next welding area, the method further includes: Monitor the temperature changes of multiple welding areas within a preset past time period to obtain the temperature fluctuation value of the welding area; When the temperature fluctuation value exceeds the preset fluctuation threshold, obtain the deviation amplitude value; Reduce the width of the target temperature range of the next welding area according to the adjustment ratio corresponding to the deviation amplitude value.

[0017] Through the above embodiments, the system monitors the temperature fluctuation values of multiple welding areas over a period of time in the past. When the fluctuation exceeds the threshold, the target temperature range of the next welding area is automatically narrowed. This method fully considers the heat input fluctuation during the welding process. By dynamically adjusting the width of the target temperature range, the response sensitivity of the system to abnormal temperature fluctuations is improved. When the temperature fluctuation is large, restricting the target temperature range helps to enhance the temperature control fineness and deviation correction ability, prevent the weld quality from deteriorating due to excessive temperature fluctuation, and thus improve the stability and reliability of the entire welding process.

[0018] In some embodiments, after the step of predicting the predicted temperature value corresponding to the next welding area when the stirring head reaches the next welding area based on the temperature of the current welding area, the moving speed of the stirring head, and the preset length of the welding area, the method further includes: Comparing the predicted temperature value with the target temperature range to obtain a temperature deviation; When the predicted temperature value is greater than the upper limit of the target temperature range, reducing the moving speed of the stirring head at a first preset ratio; When the predicted temperature value is less than or equal to the lower limit of the target temperature range, increasing the moving speed of the stirring head at a second preset ratio.

[0019] Through the above embodiments, the system compares the predicted temperature with the target temperature range. If the predicted temperature is higher than the upper limit, the moving speed of the stirring head is reduced; if it is lower than the lower limit, the moving speed is increased. This method further cooperates with the rotational speed regulation by adjusting the moving speed of the stirring head to achieve dual dynamic control of the temperature in the welding area. When the predicted temperature exceeds the target range, the speed is immediately adjusted to quickly restore the temperature range, effectively suppressing the temperature deviation and achieving flexible regulation of the welding heat input.

[0020] In a second aspect, the present application provides a friction stir welding control system, where the friction stir welding control system includes: one or more processors and a memory; The memory is coupled to the one or more processors. The memory is used to store computer program code, and the computer program code includes computer instructions. The one or more processors call the computer instructions so that the friction stir welding control system can implement a friction stir welding control method based on temperature dynamic prediction and regulation provided by the above embodiments, which will not be elaborated here.

[0021] In a third aspect, the present application provides a computer-readable storage medium, including instructions. When the instructions run on the friction stir welding control system, the friction stir welding control system can implement a friction stir welding control method based on temperature dynamic prediction and regulation provided by the above embodiments, which will not be elaborated here.

[0022] In a fourth aspect, the present application provides a computer program product. When the computer program product runs on a friction stir welding control system, the friction stir welding control system can implement a friction stir welding control method based on dynamic temperature prediction and regulation provided in the above embodiments, which will not be elaborated here.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Combining dynamic temperature prediction with a multi-source data fusion and correction mechanism, not only predicts the temperature based on the real-time temperature, the moving speed of the stirring head, and historical data in the current welding area, but also introduces the feed-forward compensation of an auxiliary temperature sensor, as well as the temperature correction coefficients of path geometric features and feature regions. By comprehensively using real-time data, historical databases, and environmental feature information, the accuracy and adaptability of temperature prediction are significantly improved, enabling the temperature control system to dynamically and accurately adjust parameters according to complex working conditions, solving the problems of lag and instability under single feedback control, and effectively ensuring a high degree of consistency in welding quality.

[0024] 2. An intelligent adaptive temperature control mechanism based on working condition recognition can intelligently select the optimal temperature correction method according to multiple factors such as feature regions, historical data similarity, and real-time temperature gradients, and perform dual dynamic regulation on the moving speed and rotational speed of the stirring head. When the temperature prediction deviates from the target range, the system can adjust the moving speed in real time to quickly restore the temperature to the ideal range, greatly improving the sensitivity and deviation correction ability of temperature control. This adaptive temperature control logic realizes a more refined and intelligent dynamic regulation of the welding process, significantly enhancing the robustness of the process and product consistency.

[0025] 3. Introducing an active temperature fluctuation management mechanism can monitor the temperature fluctuation conditions in multiple welding areas in real time, and automatically narrow the target temperature range when the fluctuation exceeds the limit, improving the system's ability to identify and respond to abnormal fluctuations. By adaptively adjusting the width of the target interval, the overshoot and hysteresis phenomena in the temperature control process are effectively reduced, making the heat input in the welding process more stable and controllable. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a flowchart of a friction stir welding control method based on dynamic temperature prediction and regulation in an embodiment of the present application; Figure 2 is another flowchart of a friction stir welding control method based on dynamic temperature prediction and regulation in an embodiment of the present application; Figure 3 is a schematic structural diagram of a physical device of a friction stir welding control system in an embodiment of the present application. Detailed implementation manner

[0027] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of the present application, the singular forms "a", "an", "the", "above", "said", "this" are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term " / or" used in the present application refers to any and all possible combinations of one or more of the listed items.

[0028] Hereinafter, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0029] For ease of understanding, the method provided in this embodiment is described in terms of a process below. Please refer to Figure 1 , which is a schematic flowchart of a friction stir welding control method based on temperature dynamic prediction and regulation in an embodiment of the present application.

[0030] S101. The current temperature of the current welding area corresponding to the current position of the stirring head is collected in real time through a temperature sensor arranged in the welding area.

[0031] Among them, the welding area refers to the part of the workpiece currently acted on by the stirring head, that is, the local area where the stirring head rotates and frictions to generate heat and realizes material connection; the stirring head refers to the tool used to generate frictional heat and plastic stirring in friction stir welding, usually composed of a shoulder and a stirring pin, and its rotation and movement directly affect the welding heat input.

[0032] Specifically, the temperature sensor detects the temperature under the shoulder of the stirring head or on the surface of the workpiece in real time according to a preset sampling frequency (such as 10 times per second), converts the temperature signal into an electrical signal (such as a voltage or current signal), and then transmits it to the signal processing module of the control system. The signal processing module performs preprocessing such as filtering and amplifying on the original signal, removes noise interference, obtains a value accurately reflecting the current temperature of the welding area, and marks the corresponding time stamp and the position information of the stirring head, and stores it in the system temporary database for subsequent steps to call.

[0033] S102. Based on the current temperature of the current welding area, the moving speed of the stirring head, and the preset length of the welding area, predict the predicted temperature value corresponding to the next welding area when the stirring head reaches the next welding area.

[0034] Among them, the preset length of the welding area refers to dividing the entire welding path into several continuous temperature control intervals. The length of each interval is preset according to the welding process requirements (such as 50mm / interval) and is used to determine the distance from the current position to the next area; the next welding area refers to the next temperature control interval in the moving direction of the stirring head, which is adjacent to the current area and has no overlap.

[0035] Specifically, the control system first obtains the temperature of the current welding area from the temporary database , the current moving speed v of the stirring head, and the preset length L of the welding area. According to the heat conduction theory, assuming that the heat input is uniform during the welding process and the thermal physical properties of the material (such as thermal conductivity, specific heat capacity) are constant, calculate the time t = L / v required for the stirring head to move from the current position to the next area. Then, based on the first-order inertia link model (where K is the temperature change rate coefficient, obtained by fitting historical data or calculating through the heat transfer equation), the temperature of the next welding area . In addition, the system can also introduce the influence factor of the rotation speed of the stirring head on the heat input, for example (ω is the current rotation speed, α is the heat input coefficient) to improve the prediction accuracy.

[0036] Optionally, the system can adopt an analytical method based on the heat transfer differential equation. Establish a heat conduction model of the welding area, regard the stirring head as a moving heat source, use the Fourier heat conduction equation to solve the temperature distribution of the next area, and calculate the predicted temperature value in combination with the boundary conditions (such as the initial temperature of the workpiece, environmental heat dissipation).

[0037] Optionally, the system can also adopt a data-driven machine learning model. Use the sample data of "current temperature - moving speed - area length - actual temperature" in the historical temperature database to train a neural network (such as an LSTM network) or a regression model, and directly input the current parameters to output the predicted temperature value; the model can be updated regularly with new data to improve the generalization ability.

[0038] It can be understood that other methods can also be used to achieve temperature prediction. For example, the real-time calculation of the thermal field distribution is combined with the finite element simulation, and the temperature of the next area is predicted according to the moving trajectory of the stirring head, which is not limited here.

[0039] S103. Calculate the target rotation speed based on the predicted temperature value and the target temperature range corresponding to the next welding area, so that the temperature of the next welding area is within the target temperature range when the stirring head reaches the next welding area.

[0040] Among them, the target temperature range refers to the temperature interval ([T_min, T_max)) preset according to the welding process requirements to ensure that the weld microstructure properties and forming quality meet the standards; the target rotation speed refers to the rotation speed ω that the stirring head needs to reach when arriving at a certain area to make the temperature of the next area fall within the target range.

[0041] Specifically, the system first compares the predicted temperature value with the target temperature range (T_min < t < T_max). If the predicted temperature value is greater than the target temperature range, it is determined that the predicted temperature is too high, and the rotation speed needs to be reduced to reduce the heat input. The target rotation speed ωt is calculated by the proportional control algorithm, such as where k1 is the adjustment coefficient and ωc is the current rotation speed. Similarly, if the predicted temperature value is less than the target temperature range, it is determined that the predicted temperature is too low, and the rotation speed needs to be increased to increase the heat input. At this time .

[0042] Optionally, the system can adopt the model predictive control (MPC) algorithm to establish a dynamic model of the stirring head rotation speed and temperature, optimize the rotation speed sequence of multiple future sampling periods within a finite time domain to ensure that the predicted temperature is within the target range, and at the same time meet the actuator rate limit; update the control input in real time through the rolling optimization strategy.

[0043] It can be understood that other methods can also be used to calculate the target rotation speed. For example, the rule-based fuzzy control algorithm generates the target rotation speed by setting a fuzzy rule table of "reduce speed when the temperature is high" and "increase speed when the temperature is low", combined with the predicted temperature deviation and the deviation change rate. No limitation is made here.

[0044] Specifically, before calculating the target rotation speed based on the predicted temperature value and the target temperature range, the system also needs to dynamically evaluate and regulate the temperature stability during the welding process: First, continuously monitor the temperature change data of multiple welding areas within a preset time period in the past (such as 5 minutes), and obtain the temperature fluctuation value by calculating the range (the difference between the maximum value and the minimum value) of the temperature sequence. For example, collect temperature data 10 times per second, and take 1000 data points within the most recent 300 seconds to calculate ΔT = T_max - T_min; when this fluctuation value exceeds the preset threshold (such as 20°C), further calculate the deviation amplitude value (ΔT_excess = ΔT - threshold). If the measured fluctuation value of 25°C exceeds the threshold of 5°C, then ΔT_excess = 5°C, and according to the preset deviation amplitude - adjustment ratio mapping relationship (such as for every 1°C exceeded, the width of the target range is reduced by 2%), calculate the shrinkage amount of the target temperature range for the next welding area. For example, when the original range width is 40°C and the adjustment ratio is 5%, the new width becomes 38°C, so as to improve the temperature control accuracy by narrowing the width of the target temperature interval, suppress the temperature fluctuation caused by unstable heat input, and ensure the fineness and stability of the temperature control in the subsequent welding areas.

[0045] S104. Calculate the rotation speed change rate from the current rotation speed to the target rotation speed based on the current rotation speed of the stirring head, the target rotation speed, and the moving speed of the stirring head.

[0046] Specifically, the control system first obtains the current rotation speed, the target rotation speed, and the moving speed, and obtains the preset welding area length from step S102. According to the kinematic relationship, calculate the time t = L / v required for the stirring head to move from the current position to the next area, and then calculate the rotation speed change rate k = Δω / L according to the rotation speed change amount Δω = ωt - ωc.

[0047] S105. Control the rotation speed of the stirring head according to the rotation speed change rate so that the stirring head reaches the target rotation speed when moving to the next welding area.

[0048] Specifically, the control system dynamically calculates the current rotation speed ωc1 = ωc + ks according to the rotation speed change rate k and the real - time moving distance s of the stirring head. The system adjusts the motor drive signal through a closed - loop control algorithm (such as PID control) to make the actual rotation speed track the calculated value. For example, when the moving distance of the stirring head is s1, the target rotation speed is ωc + ks1. If the actual rotation speed is lower than this value, the system increases the motor drive power; if it is higher than this value, it decreases the drive power until the rotation speed of the stirring head exactly matches ωt when it reaches the next area.

[0049] In the above embodiments, the system collects the temperature at the current position of the stirring head in real time through the temperature sensor set in the welding area, combines the moving speed of the stirring head and the preset length of the welding area, predicts the temperature when the stirring head reaches the next welding area, and dynamically calculates and adjusts the rotation speed of the stirring head based on the predicted temperature and the target temperature range. Compared with the traditional closed-loop control method that only relies on the current temperature feedback, this method predicts the temperature change trend in advance and combines the motion parameters of the stirring head for feedforward regulation to cope with the slow response caused by thermal inertia and temperature control lag. It can reduce the temperature fluctuation in the welding area, improve the accuracy and stability of temperature control, thereby enhancing the uniformity of the weld formation and the welding quality, and meeting the higher requirements for the dynamic and fine regulation of heat input in friction stir welding.

[0050] After combining the above scenarios, the following is a further and more specific process description of the method provided in this embodiment. Please refer to Figure 2 , which is another process schematic diagram of a friction stir welding control method based on temperature dynamic prediction and regulation in the embodiments of the present application.

[0051] S201. Obtain the current welding working condition.

[0052] Specifically, the system collects and integrates multi-dimensional data during the welding process in real time through the integrated sensor network and data interface to form a complete description of the current working condition. The data sources include: the real-time temperature detected by the temperature sensor, the moving speed and rotation speed of the stirring head feedback by the encoder, the workpiece material and thickness parameters (obtained by scanning the code or manual input), the welding path coordinate information (from the pre-imported CAD model or real-time scanning data), etc. The system encapsulates these data into a working condition data packet in a preset format and stores it in the buffer for subsequent logic calls.

[0053] S202. Determine whether the current welding working condition is a characteristic area of the welding path.

[0054] Specifically, the system first obtains the current position coordinates of the stirring head (through the servo motor encoder or the vision positioning system), and extracts the corresponding curvature radius and thickness value at the current position from the pre-stored geometric feature data of the welding path. It is judged by a preset threshold: if the curvature radius is less than 100 mm or the thickness change amount exceeds 1 mm / 50 mm, it is determined as a characteristic area (such as a bend or a thickness mutation section). For example, if the curvature radius of the path corresponding to the current position coordinates is 80 mm (<100 mm threshold), it is marked as a curvature change area; if the thickness suddenly changes from 3 mm to 5 mm and the change distance is 40 mm (<50 mm threshold), it is marked as a thickness change area.

[0055] Further, when the system determines that the current welding condition is a characteristic area of the welding path, it enters step S203 to correct the predicted temperature value using the temperature correction coefficient; conversely, when the system determines that the current welding condition is not a characteristic area of the welding path, it skips step S203 and directly executes step S204 to determine the similarity threshold.

[0056] S203. Select the corresponding temperature correction coefficient to correct the predicted temperature value.

[0057] Before starting welding, the system obtains the geometric features of the welding path by scanning a predetermined welding area; identifies the characteristic areas in the welding path based on the geometric features; and pre-calculates the temperature correction coefficient corresponding to the position of the characteristic area based on the characteristic area and the geometric features of the welding path to correct the predicted temperature value.

[0058] Specifically, before the control system collects the temperature of the welding area at the current position of the stirring head in real time through the temperature sensor, it needs to complete the preprocessing of the geometric features of the welding path: First, before starting welding, use devices such as a 3D laser scanner and a structured light camera to scan the predetermined welding area to obtain the point cloud data of the welding path, and generate a three-dimensional geometric model after noise reduction and meshing processing. Extract geometric features such as the path curvature radius and workpiece thickness distribution (such as a bend with a curvature radius less than 100 mm and an area with a thickness mutation exceeding 1 mm) from it; then, identify the characteristic areas in the welding path based on the geometric features, and mark the path curvature change area (such as a continuous bending section) and the thickness change area (such as a sudden thickness transition section) by setting a curvature radius threshold (such as 100 mm) and a thickness change threshold (such as 1 mm / 50 mm), and store the position of the characteristic area in the form of a coordinate interval; finally, according to the type and geometric parameters of the characteristic area (such as the curvature radius and the thickness change amount), fit the temperature correction coefficient through a preset formula (such as the curvature coefficient calculation formula) or finite element simulation. For example, when the curvature radius is 50 mm, the curvature coefficient is 0.9, and the curvature coefficient ratio of a thickness of 5 mm relative to 3 mm is 1.07. Associate and store the temperature correction coefficient with the position of the characteristic area for targeted correction of the predicted temperature value during the welding process to compensate for the influence of geometric factors on heat conduction.

[0059] S204. Determine whether the similarity between the current welding condition and the historical temperature data is greater than the preset similarity threshold.

[0060] Specifically, the system extracts key parameters from the current working conditions: material type (such as aluminum alloy 6061), thickness (4 mm), moving speed (60 mm / min), rotational speed (1000 rpm), and encodes them as the feature vector V_c = [6061, 4, 60, 1000]. Records of the same material type are screened in the historical temperature database, and the thickness and speed parameters are normalized (for example, the normalized thickness value = 4 / 6 = 0.67, the normalized moving speed value = 60 / 100 = 0.6). The cosine similarity algorithm is used to calculate the similarity values with each historical record. For example, for a historical record V_h = [6061, 3, 50, 1200], after normalization, Sim = 0.85 > 0.8 threshold, then it is determined as reference data.

[0061] Further, if the detected similarity is greater than the preset similarity threshold, then proceed to step S205 to correct the predicted temperature value using the predicted temperature correction value; otherwise, proceed to step S206 to directly correct the predicted temperature value using the feedforward compensation value.

[0062] S205. Select the predicted temperature correction value to correct the predicted temperature value.

[0063] The system collects the temperature change data of the welding area under different combinations of welding materials, welding thicknesses, and welding working conditions to obtain a historical temperature database; screens at least one set of target historical temperature data with the highest similarity to the current welding working condition from the historical temperature database; calculates the predicted temperature correction value based on the target historical temperature data and the current welding area temperature to correct the predicted temperature value.

[0064] Specifically, before the control system collects the temperature of the welding area at the current position of the stirring head in real time through the temperature sensor, it is necessary to first construct a historical temperature database and screen and correct the data based on this. Through multiple groups of welding tests, for different welding materials (such as aluminum alloy, titanium alloy, etc.), welding thicknesses (such as 2 mm, 5 mm, etc.) and welding condition combinations (including parameters such as the rotation speed, moving speed, and shoulder pressure of the stirring head), the temperature sensor is used to collect the temperature change data of the welding area at a high frequency (such as 20 times per second), and the process parameters and timestamps are recorded synchronously. After denoising and normalization processing, they are classified and stored according to the "material - thickness - condition" dimension, forming a historical temperature database that includes temperature curves, process parameters, and weld quality evaluations. Subsequently, after welding is started, the current welding condition parameters (such as material type, workpiece thickness, moving speed, etc.) are obtained, encoded into feature vectors, and the records with the closest thickness and speed parameters under the same material type are retrieved in the historical database through algorithms such as Euclidean distance and cosine similarity. At least one set of target historical temperature data with the highest similarity to the current condition is selected (such as selecting the top 3 groups in descending order of similarity and excluding outliers). Finally, the initial temperature value corresponding to the current position in the target historical data is extracted, compared with the currently measured initial temperature of the workpiece to calculate the temperature deviation, and combined with the change trend of the historical temperature curve over time and the current temperature change rate, a predicted temperature correction value is obtained (such as calculating the compensation amount through linear deviation or dynamic curve error), which is used to calibrate the temperature value predicted based on real-time temperature and motion parameters in the subsequent process to make up for the prediction deviation caused by factors such as material differences and environmental changes.

[0065] S206. Select the feedforward compensation value to correct the predicted temperature value.

[0066] Set at least one auxiliary temperature sensor in the moving direction of the stirring head in the welding path; collect the auxiliary temperature data of the welding area in front of the stirring head in real time through the auxiliary temperature sensor; calculate the temperature gradient between the current position of the stirring head and the next welding area based on the auxiliary temperature data and the moving speed of the stirring head; calculate the feedforward compensation value based on the temperature gradient to correct the predicted temperature value.

[0067] Specifically, before the control system collects the temperature of the welding area at the current position of the stirring head in real time through the temperature sensor, it is necessary to complete the preprocessing of auxiliary temperature sensing and feedforward compensation. First, according to the welding process requirements, at least one auxiliary temperature sensor (such as an infrared sensor or a thermocouple) is installed on the welding path within a range of 50-200 mm in front of the moving direction of the stirring head. The sensors can be arranged singly or form an equally spaced array (such as a 3-sensor group with a spacing of 30 mm), and are connected to the control system through high-temperature resistant cables or wireless transmission to ensure that their detection area covers the next welding area that the stirring head is about to reach; during the welding process, the auxiliary temperature sensor collects the temperature data of the front welding area in real time at a frequency of 10-50 Hz. After filtering, amplifying and analog-to-digital conversion by the signal conditioning module, auxiliary temperature data with time stamps and position marks is generated (such as the temperature at 100 mm in front is 200 °C); then, the control system combines the current moving speed of the stirring head (such as 60 mm / min) and the auxiliary temperature data to calculate the temperature gradient between the current position and the next area - if it is a single sensor, the gradient G = (front temperature - current temperature) / distance, and if it is multiple sensors, G (unit °C / mm) is obtained by linearly fitting the slope of the position-temperature curve; finally, according to the temperature gradient G and the preset welding area length L (such as 50 mm), the feedforward compensation value ΔT_ff = G×L (such as when G = 2 °C / mm, ΔT_ff = 100 °C) is calculated and superimposed on the temperature value predicted based on the real-time temperature and motion parameters to compensate for the heat conduction lag and temperature change trend in advance and improve the accuracy of the predicted temperature.

[0068] Specifically, after the system calculates the predicted temperature value of the next welding area based on the current welding area temperature, the moving speed of the stirring head and the preset welding area length, it will compare the predicted temperature value with the target temperature range. If the predicted temperature value is greater than the upper limit of the target temperature range, calculate the upper deviation and reduce the moving speed of the stirring head according to the first preset ratio (such as reducing the speed by 5% when the deviation ≤ 10 °C and by 10% when the deviation > 10 °C) to reduce the heat input per unit time. For example, the current speed of 100 mm / min is reduced by 5% to 95 mm / min; if the predicted temperature value is less than or equal to the lower limit of the target temperature range, calculate the lower deviation and increase the moving speed of the stirring head according to the second preset ratio (such as increasing the speed by 4% when the lower deviation ≤ 10 °C and by 8% when the lower deviation > 10 °C) to increase the heat input per unit length. For example, the current speed of 80 mm / min is increased by 4% to 83.2 mm / min. Through this two-way dynamic adjustment mechanism, the system combines the rotational speed control to achieve precise control of the welding heat input and ensure that the temperature of the welding area is stable within the process requirements.

[0069] The friction stir welding control system of the embodiment of the present invention is applied to electronic devices.Figure 3 The schematic structural diagram of an electronic device suitable for implementing the embodiments of the present invention is shown.

[0070] It should be noted that Figure 3 The shown electronic device is only an example and should not impose any limitations on the functions and scope of use of the embodiments of the present invention.

[0071] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions (computer programs), or the relevant hardware can be controlled by instructions (computer programs). The instructions can be stored in a computer-readable storage medium and loaded and executed by a processor. The electronic device of this embodiment includes a storage medium and a processor. Among them, multiple instructions are stored in the storage medium, and these instructions can be loaded by the processor to execute any step of the method provided by the embodiments of the present invention.

[0072] Specifically, the storage medium and the processor are electrically connected directly or indirectly to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more signal lines. The computer-executable instructions for implementing the data access control method are stored in the storage medium, including at least one software function module that can be stored in the storage medium in the form of software or firmware. The processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium. The storage medium can be, but is not limited to, a random access storage medium (Random Access Memory, abbreviated as RAM), a read-only storage medium (Read Only Memory, abbreviated as ROM), a programmable read-only storage medium (Programmable Read-Only Memory, abbreviated as PROM), an erasable read-only storage medium (Erasable Programmable Read-Only Memory, abbreviated as EPROM), an electrically erasable read-only storage medium (Electric Erasable Programmable Read-Only Memory, abbreviated as EEPROM), etc. Among them, the storage medium is used to store programs, and the processor executes the programs after receiving the execution instructions.

[0073] Furthermore, the software programs and modules in the above storage medium may further include an operating system, which may include various software components and / or drivers for managing system tasks (such as memory management, storage device control, power management, etc.), and may communicate with various hardware or software components to provide a running environment for other software components. The processor can be an integrated circuit chip with signal processing capabilities. The above-mentioned processor can be a general-purpose processor, including a central processing unit (Central Processing Unit, abbreviated as CPU), a network processor (Network Processor, abbreviated as NP), etc., which can implement or execute the various methods, steps, and logic flow block diagrams disclosed in this embodiment. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0074] Since the instructions stored in this storage medium can execute the steps in any of the methods provided by the embodiments of the present invention, the beneficial effects of any of the methods provided by the embodiments of the present invention can be achieved. For details, please refer to the previous embodiments and will not be repeated here.

[0075] The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A friction stir welding control method based on dynamic temperature prediction and regulation, which is applied to a friction stir welding control system, and is characterized in that, The method includes: Collecting, by a temperature sensor disposed in the welding area, the current temperature of the current welding area corresponding to the current position of the stirring head in real time; Predicting, based on the current temperature of the current welding area, the moving speed of the stirring head, and a preset length of the welding area, the predicted temperature value corresponding to the next welding area when the stirring head reaches the next welding area; Calculating a target rotation speed based on the predicted temperature value and a target temperature range corresponding to the next welding area, such that the temperature of the next welding area is within the target temperature range when the stirring head reaches the next welding area; Calculating, based on the current rotation speed of the stirring head, the target rotation speed, and the moving speed of the stirring head, the rotation speed change rate from the current rotation speed to the target rotation speed; Controlling the rotation speed of the stirring head according to the rotation speed change rate, such that the stirring head reaches the target rotation speed when moving to the next welding area.

2. The method according to claim 1, wherein Before the step of collecting, by a temperature sensor disposed in the welding area, the current temperature of the current welding area corresponding to the current position of the stirring head in real time, it further includes: Collecting temperature change data of the welding area under combinations of different welding materials, different welding thicknesses, and different welding working conditions to obtain a historical temperature database; Selecting at least one group of target historical temperature data with the highest similarity to the current welding working condition from the historical temperature database; Calculating a predicted temperature correction value based on the target historical temperature data and the current temperature of the current welding area, and the predicted temperature correction value is used to correct the predicted temperature value.

3. The method according to claim 2, wherein Before the step of collecting, by a temperature sensor disposed in the welding area, the current temperature of the current welding area corresponding to the current position of the stirring head in real time, it further includes: Setting at least one auxiliary temperature sensor in the moving direction of the stirring head in the welding path; Collecting, by the auxiliary temperature sensor, auxiliary temperature data of the welding area in front of the stirring head in real time; Calculating, based on the auxiliary temperature data and the moving speed of the stirring head, the temperature gradient between the current position of the stirring head and the next welding area; Calculating a feedforward compensation value based on the temperature gradient, and the feedforward compensation value is used to correct the predicted temperature value.

4. The method according to claim 3, characterized in that Before the step of collecting, by a temperature sensor disposed in the welding area, the current temperature of the current welding area corresponding to the current position of the stirring head in real time, it further includes: Before starting welding, obtaining the geometric features of the welding path by scanning a predetermined welding area; Identifying, based on the geometric features, feature areas in the welding path, and the feature areas include a path curvature change area and a thickness change area; Pre-calculating, based on the feature areas and the geometric features of the welding path, a temperature correction coefficient corresponding to the position of the feature areas, and the temperature correction coefficient is used to correct the predicted temperature value.

5. The method according to claim 4, wherein After the step of predicting, based on the current temperature of the current welding area, the moving speed of the stirring head, and a preset length of the welding area, the predicted temperature value corresponding to the next welding area when the stirring head reaches the next welding area, it further includes: Judging whether the current welding working condition is a feature area of the welding path; If the current welding working condition is a feature area, selecting the corresponding temperature correction coefficient to correct the predicted temperature value; If the current welding condition is not the characteristic area, determine whether the similarity between the current welding condition and the historical temperature data is greater than the preset similarity threshold; If so, select the predicted temperature correction value to correct the predicted temperature value; If not, select the feed-forward compensation value to correct the predicted temperature value.

6. The method according to claim 1, characterized in that, Before the step of calculating the target rotation speed based on the predicted temperature value and the target temperature range corresponding to the next welding area, so that the temperature of the next welding area is within the target temperature range when the stirring head reaches the next welding area, further includes: Monitoring the temperature changes of multiple welding areas within a preset past time period to obtain the temperature fluctuation value of the welding area; When the temperature fluctuation value exceeds the preset fluctuation threshold, obtain the deviation amplitude value; Reduce the width of the target temperature range of the next welding area according to the adjustment ratio corresponding to the deviation amplitude value.

7. The method according to claim 1, wherein After the step of predicting the predicted temperature value corresponding to the next welding area when the stirring head reaches the next welding area based on the temperature of the current welding area, the moving speed of the stirring head, and the preset length of the welding area, further includes: Compare the predicted temperature value with the target temperature range to obtain a temperature deviation; When the predicted temperature value is greater than the upper limit of the target temperature range, reduce the moving speed of the stirring head at a first preset ratio; When the predicted temperature value is less than or equal to the lower limit of the target temperature range, increase the moving speed of the stirring head at a second preset ratio.

8. A friction stir welding control system, characterized in that, The friction stir welding control system includes: one or more processors and a memory; The memory is coupled to the one or more processors, and the memory is used to store computer program code. The computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the friction stir welding control system to execute the method according to any one of claims 1-7.

9. A computer-readable storage medium, comprising instructions, characterized in that, When the instruction runs on the friction stir welding control system, enable the friction stir welding control system to execute the method according to any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product runs on the friction stir welding control system, enable the friction stir welding control system to execute the method according to any one of claims 1-7.

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