Motorcycle silencer welding three-dimensional shrinkage rate control method and control system

Abstract

The invention belongs to the technical field of silencer welding, and relates to a motorcycle silencer welding three-dimensional shrinkage rate control method which comprises the following steps: (1) constructing a heat-force coupling model in a welding process; (2) determining a'heated stress reduction area '; (3) welding data are collected in real time through a sensor, and a welding temperature field and stress-strain changes are monitored; (4) monitoring data are processed and analyzed through a machine learning algorithm, and the mapping relation between welding parameters and three-dimensional shrinkage deformation is established; and (5) monitoring data are transmitted to a control system in real time and compared with a preset threshold value, and based on a model prediction result and real-time data feedback, welding process parameters and parameters of a heated stress reduction area are dynamically adjusted, so that welding stress is reduced, and three-dimensional shrinkage deformation is accurately controlled. The invention further provides a motorcycle silencer welding three-dimensional shrinkage rate control system. According to the control method and the control system provided by the invention, the three-dimensional shrinkage rate in the welding process can be accurately controlled.

Description

Technical Field

[0001] The invention belongs to the technical field of muffler welding and relates to a three-dimensional shrinkage control method and a control system for motorcycle muffler welding. Background Art

[0002] In the manufacturing process of motorcycle mufflers, welding is a key connection process. However, the thermal stress generated during the welding process often causes three-dimensional shrinkage deformation of the welded parts, affecting the dimensional accuracy and performance of the product. Traditional methods are difficult to accurately predict and control this three-dimensional shrinkage deformation, resulting in low production efficiency and unstable product quality.

[0003] In order to control the three-dimensional shrinkage deformation during welding, technicians have proposed a variety of control methods for muffler welding processes:

[0004] The first method mainly reduces welding deformation and residual stress by adjusting welding parameters (such as welding current, welding speed, etc.) and welding sequence. However, this method mainly relies on empirical adjustment, lacks accurate mathematical models and real-time data feedback, and it is difficult to achieve precise control of three-dimensional shrinkage.

[0005] The second method focuses on reducing welding stress by optimizing the welding sequence and welding path to reduce welding stress and thus reduce welding deformation. However, this method has limited control effect on three-dimensional shrinkage deformation and is difficult to adapt to motorcycle mufflers of different materials and specifications.

[0006] The third method combines numerical simulation and experimental verification, predicting welding deformation by building a numerical model of the welding process and adjusting the welding process accordingly. However, this method mainly relies on numerical simulation results and lacks real-time data feedback and dynamic adjustment mechanisms.

[0007] In view of this, it is very necessary to develop a new three-dimensional shrinkage rate control method for motorcycle muffler welding to achieve precise control of the three-dimensional shrinkage rate. Summary of the invention

[0008] In view of the deficiencies of the prior art, the present invention provides a method and a control system for controlling the three-dimensional shrinkage rate of motorcycle muffler welding, which can accurately control the three-dimensional shrinkage rate of the welding process.

[0009] In order to solve the above technical problems, the purpose of the present invention is achieved through the following technical solutions:

[0010] A method for controlling three-dimensional shrinkage rate of motorcycle muffler welding, comprising the following steps:

[0011] (1) Based on the geometric dimensions, material properties and welding process parameters of the muffler, a thermal-mechanical coupling model of the welding process was constructed using finite element analysis software;

[0012] (2) Identify the area in the model where stress concentration and deformation are most significant during welding, and determine this potential three-dimensional shrinkage deformation area as the "heated stress reduction zone";

[0013] (3) During the welding process, sensors are used to collect welding data in real time and monitor the changes in welding temperature field and stress-strain;

[0014] (4) Use machine learning algorithms to process and analyze monitoring data and establish a mapping relationship between welding parameters and three-dimensional shrinkage deformation;

[0015] (5) The monitoring data is transmitted to the control system in real time and compared with the preset threshold value. Based on the model prediction results and real-time data feedback, the welding process parameters and the parameters of the "heated stress reduction zone" are dynamically adjusted to reduce the welding stress and accurately control the three-dimensional shrinkage deformation. When adjusting the parameters of the "heated stress reduction zone", it is necessary to ensure that the heating process can cause the material to undergo an appropriate amount of plastic deformation to offset part of the shrinkage generated during the welding process.

[0016] In the above-mentioned method for controlling the three-dimensional shrinkage rate of motorcycle muffler welding, the material properties include thermal expansion coefficient, yield strength, etc.

[0017] In the above-mentioned motorcycle muffler welding three-dimensional shrinkage control method, according to the structural characteristics and material properties of the welded part, a suitable part is selected as the "heated stress reduction zone", and one or more "heated stress reduction zones" can be selected.

[0018] In the above-mentioned motorcycle muffler welding three-dimensional shrinkage rate control method, the finite element analysis software adopts ANSYS, Abaqus and other software.

[0019] In the above-mentioned motorcycle muffler welding three-dimensional shrinkage rate control method, the machine learning algorithm includes but is not limited to neural network, support vector machine, random forest, etc., which is used to establish a mapping relationship model between welding parameters and three-dimensional shrinkage deformation. Further, the processed data is trained using the machine learning algorithm to establish a mapping relationship model between welding parameters and three-dimensional shrinkage deformation, and the generalization ability of the model is evaluated through cross-validation.

[0020] In the above-mentioned method for controlling the three-dimensional shrinkage rate of motorcycle muffler welding, when the monitoring data exceeds the threshold, the automatic control system adjusts the welding parameters (such as reducing the welding speed, increasing the welding current, etc.) according to the preset algorithm or starts the heating equipment to heat the "heated stress reduction zone"; further, the monitoring data is processed and analyzed using a machine learning algorithm, and a mapping relationship between the welding parameters and the three-dimensional shrinkage deformation is established, so as to improve the accuracy and robustness of the control strategy through continuous learning and optimization.

[0021] In the above-mentioned motorcycle muffler welding three-dimensional shrinkage rate control method, the control method adopts a hybrid drive control strategy, including:

[0022] (1) Initial control strategy setting: Based on the model prediction results, the initial welding process parameters and the parameters of the "heated stress reduction zone" are set. Furthermore, the heating scheme of the "heated stress reduction zone" includes the heating temperature, heating time, heating method (such as resistance heating, induction heating, etc.) and the selection of heating equipment;

[0023] (2) Real-time adjustment and optimization: During the welding process, the welding process parameters are dynamically adjusted based on the real-time collected data and the prediction results of the machine learning model to maintain a stable temperature field and control the three-dimensional shrinkage deformation, and to improve the effectiveness of controlling the three-dimensional shrinkage deformation and improving the welding quality and product accuracy;

[0024] (3) Feedback and iteration: After each welding is completed, the actual 3D dimension data is compared with the predicted value to evaluate the effectiveness of the control strategy, and the model parameters and machine learning model are iteratively optimized based on the evaluation results.

[0025] In the above-mentioned three-dimensional shrinkage rate control method for motorcycle muffler welding, the three-dimensional thermal-mechanical coupling model includes a welding heat source model, a material property model and a boundary condition setting, which are used to simulate the temperature field distribution and thermal stress changes during the welding process.

[0026] In the above-mentioned method for controlling the three-dimensional shrinkage rate of motorcycle muffler welding, further, the construction of the three-dimensional thermal-mechanical coupling model includes: predicting the distribution of temperature field, stress field and deformation field during welding through simulation calculation, and generating a corresponding three-dimensional cloud map.

[0027] In the above-mentioned method for controlling the three-dimensional shrinkage rate of motorcycle muffler welding, the sensor includes an infrared thermometer or a thermocouple, a strain gauge, and a displacement sensor, and the temperature, stress strain, and three-dimensional size change data during the welding process are respectively obtained in real time through the sensors.

[0028] In the above-mentioned method for controlling the three-dimensional shrinkage rate of motorcycle muffler welding, the welding process parameter adjustment includes the adjustment of welding speed, current, voltage, and heating temperature. By adjusting the welding process parameters, a stable temperature field is maintained and welding stress is reduced.

[0029] In the above-mentioned method for controlling the three-dimensional shrinkage rate of motorcycle muffler welding, the parameter adjustment of the "heated stress reduction zone" includes heating temperature, heating time, and heating position. The three-dimensional shrinkage deformation during welding is controlled by adjusting the parameters of the "heated stress reduction zone".

[0030] The present invention also provides a three-dimensional shrinkage rate control system for motorcycle muffler welding, comprising a control hardware system and a control software system, wherein the control hardware system comprises a computer, a sensor system, an integrated control system, a human-computer interaction interface and a welding manipulator, and the control software system comprises a machine learning algorithm software, a motion control software and a human-computer interaction interface software;

[0031] The integrated control system receives the output results of the data processing and analysis module, and dynamically adjusts the welding process parameters according to the output results of the machine learning algorithm to maintain a stable temperature field and reduce welding stress, dynamically adjusts the heating parameters of the "heated stress reduction zone", and sends control instructions to the welding equipment to achieve precise control.

[0032] In the above-mentioned motorcycle muffler welding three-dimensional shrinkage rate control system, the computer is a high-performance industrial computer or server; the machine learning algorithm software processes and analyzes data from sensors to predict the three-dimensional shrinkage rate during welding.

[0033] In the above-mentioned motorcycle muffler welding three-dimensional shrinkage rate control system, the sensor system is installed near the robot welding gun or on the workpiece and uses high temperature and corrosion resistant materials such as ceramics to encapsulate and protect the temperature sensor, and shielded cables are used for signal transmission.

[0034] In the above-mentioned motorcycle muffler welding three-dimensional shrinkage rate control system, the welding process parameters include welding speed, current, voltage, and heating temperature; the "heated stress reduction zone" heating parameters include heating temperature, heating time, and heating position.

[0035] In the above-mentioned motorcycle muffler welding three-dimensional shrinkage rate control system, the human-computer interaction interface enables the user to interact with the control system, and the human-computer interaction interface displays the real-time temperature, stress strain, three-dimensional shrinkage deformation and other data during the welding process in real time, and provides a real-time adjustment interface for welding process parameters, allowing the user to make dynamic adjustments as needed;

[0036] When abnormal conditions occur during welding, such as excessive temperature or excessive stress, the human-computer interaction interface provides an alarm prompt so that the user can take timely measures;

[0037] The human-computer interaction interface provides historical data query function, allowing users to view past welding records for data analysis and quality control.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] 1. The present invention combines the advantages of model-driven and data-driven, predicts the three-dimensional shrinkage deformation during welding by constructing a three-dimensional thermal-mechanical coupling model, and dynamically adjusts it in combination with real-time data feedback. This hybrid drive control strategy can more accurately predict and control the three-dimensional shrinkage rate during welding.

[0040] 2. The present invention collects the welding temperature field, stress-strain field and three-dimensional size change data of the welded parts in real time during the welding process, and uses a machine learning algorithm to establish a mapping relationship model between welding parameters and three-dimensional shrinkage deformation. By adjusting the welding process parameters and the "heated stress reduction zone" parameters in real time, precise control of the welding process is achieved.

[0041] 3. The parameter adjustment of the present invention adopts a combination of adjusting the welding process parameters and the "heated stress reduction zone" parameters. By selecting a suitable "heated stress reduction zone" through a three-dimensional thermal-mechanical coupling model and adjusting the "heated stress reduction zone" parameters, it can be ensured that the heating process can cause the material to undergo an appropriate amount of plastic deformation to offset part of the shrinkage generated during the welding process. In combination with the adjustment of the welding process parameters, the three-dimensional shrinkage rate during the welding process can be more accurately controlled.

[0042] 4. The present invention further provides a three-dimensional shrinkage rate control system for motorcycle muffler welding, which, combined with the above control method, can better control the three-dimensional shrinkage rate during the welding process. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a framework diagram of the present invention; DETAILED DESCRIPTION

[0044] The present invention will be further described below with reference to specific embodiments of the present invention. Figure 1 : Embodiment 1

[0045] A three-dimensional shrinkage rate control method for motorcycle muffler welding based on model and data hybrid drive, comprising the following steps:

[0046] Step (1) Model establishment

[0047] A typical motorcycle muffler was selected as the research object, and its material properties (such as thermal conductivity, specific heat capacity, elastic modulus, yield strength, etc.) and geometric size information were collected. A three-dimensional thermal-mechanical coupling model of the muffler was established using finite element analysis software (such as ANSYS, Abaqus, etc.). The model simulates the movement path, power distribution, and preheating and cooling process of the welding heat source (such as TIG welding gun, laser beam, etc.) in detail. Through small-scale welding experiments, the temperature field, stress field, and deformation field data of the actual welding process were obtained. These data were used to calibrate and verify the model to ensure that the model can accurately predict the three-dimensional shrinkage deformation during welding.

[0048] Step (2) Determine the "heated stress reduction zone"

[0049] Step 2.1: Based on the verified model, analyze the areas of stress concentration and potential large deformation during welding. These areas are usually located near the weld and where the structural stiffness changes.

[0050] Step 2.2: Based on the analysis results, select one or more suitable areas as "heated stress reduction areas". These areas should be easy to heat and can effectively relieve welding stress.

[0051] Step 2.3: Determine the optimal heating parameter combination by simulating the effects of different heating temperatures, heating times and heating methods on the “heated stress reduction zone”.

[0052] Step (3) Data-driven optimization control strategy

[0053] Step 3.1: Install infrared thermometers, thermocouples, and other sensors during the welding process to monitor the welding temperature field and stress-strain changes in real time.

[0054] Step 3.2: Compare the real-time monitoring data with the preset threshold value, and adjust the welding parameters (such as welding speed, current size) and the heating parameters of the "heated stress reduction zone" (such as heating temperature, heating time) in real time through the automatic control system.

[0055] Step 3.3: Use professional finite element analysis software (such as ANSYS, Abaqus, etc.) to build a three-dimensional thermal-mechanical coupling model based on the muffler's geometric dimensions, material properties (such as thermal conductivity, specific heat capacity, elastic modulus, thermal expansion coefficient, etc.) and welding process parameters (welding current, voltage, speed, preheating temperature, etc.). Set the welding heat source in the model, simulate the temperature field distribution during the welding process, and calculate the resulting thermal stress and three-dimensional shrinkage deformation.

[0056] Step (4) Adjustment of model parameters

[0057] Through actual welding experiments, the temperature data, stress-strain data during welding, and three-dimensional size data after welding are obtained. The experimental data are compared with the model prediction results to evaluate the accuracy of the model. According to the comparison results, the model parameters, such as material properties and heat source model, are adjusted to improve the prediction accuracy of the model.

[0058] Step (5) Data-driven optimization of data collection and analysis

[0059] During the welding process, infrared thermometers, strain gauges, displacement sensors and other equipment are used to collect welding temperature field, stress strain field and three-dimensional dimensional change data of welded parts in real time. Record the changes in welding process parameters, such as welding speed, current, heating temperature, etc. Preprocess the collected data, including filtering, denoising, interpolation, etc., to improve data quality. Use machine learning algorithms (such as neural networks, support vector machines, random forests, etc.) to train the processed data and establish a mapping relationship model between welding parameters and three-dimensional shrinkage deformation. Evaluate the generalization ability of the model through cross-validation and other methods to ensure that the model can accurately predict three-dimensional shrinkage deformation under different welding conditions.

[0060] Step (6) Hybrid drive control strategy

[0061] Initial control strategy setting: Based on the model prediction results, set the initial welding process parameters and "heated stress reduction zone" parameters (such as heating temperature, heating time, heating position, etc.).

[0062] Real-time adjustment and optimization: During the welding process, the welding process parameters are dynamically adjusted based on the real-time collected data and the prediction results of the machine learning model. For example, when the welding temperature field is detected to deviate from the preset range, the welding speed or current is automatically adjusted to maintain a stable temperature field. According to the prediction results of three-dimensional shrinkage deformation, the heating parameters of the "heated stress reduction zone" are adjusted in time to reduce welding stress and control three-dimensional shrinkage deformation.

[0063] Feedback and iteration: After each welding is completed, the actual three-dimensional size data is compared with the predicted value to evaluate the effectiveness of the control strategy. According to the evaluation results, the model parameters and machine learning model are iteratively optimized to improve the control accuracy and stability in the subsequent welding process. If the adjustment of model parameters cannot significantly improve the prediction effect, it may be necessary to consider optimizing the machine learning model itself. Introducing more advanced algorithms, increasing the amount of training data, and improving feature extraction methods are all effective ways to optimize the machine learning model. Select multiple silencers of different specifications and materials for welding experiments, and apply the above hybrid drive control strategy for welding. The three-dimensional size of the muffler after welding is detected and compared with the welded parts that do not adopt this control strategy. Analyze the experimental results to verify the effectiveness of the hybrid drive control strategy in reducing welding stress, controlling three-dimensional shrinkage deformation, and improving welding quality and product accuracy.

[0064] Example 2

[0065] A three-dimensional shrinkage rate control system for motorcycle muffler welding comprises a control hardware system and a control software system.

[0066] The control hardware system includes:

[0067] Computer: The computer is a high-performance industrial computer or server to ensure sufficient computing power to run complex machine learning algorithms and finite element analysis software.

[0068] Sensor system: The sensor system is installed near the robot welding gun or on the workpiece and uses high-temperature and corrosion-resistant materials such as ceramics to encapsulate and protect the temperature sensor. Shielded cables are used to reduce signal attenuation and interference during transmission.

[0069] Integrated control system: The integrated control system receives the output results of the data processing and analysis module, and dynamically adjusts the welding process parameters such as welding speed, current, voltage, heating temperature, etc. according to the output results of the machine learning algorithm to maintain a stable temperature field and reduce welding stress, adjusts the heating temperature, heating time, heating position and other parameters to adjust the parameters of the "heated stress reduction zone", and sends control instructions to the welding equipment to achieve precise control.

[0070] Human-machine interaction interface: The human-machine interaction interface enables the user to interact with the control system. The human-machine interaction interface displays real-time temperature, stress and strain, three-dimensional shrinkage deformation and other data during the welding process, and provides a real-time adjustment interface for welding process parameters, allowing users to make dynamic adjustments as needed; when abnormal conditions occur during the welding process, such as excessive temperature, excessive stress, etc., the human-machine interaction interface should provide an alarm prompt so that the user can take timely measures; provide historical data query function, allowing users to view past welding records for data analysis and quality control.

[0071] Welding robot, execution unit for muffler welding.

[0072] The control software system includes machine learning algorithm software, motion control software and human-computer interaction interface software; the machine learning algorithm software processes and analyzes data from sensors to predict the three-dimensional shrinkage rate during welding. The algorithm is trained by a sufficiently large amount of temperature, stress and strain data during the welding process of mufflers of different models, and the prediction accuracy reaches more than 92%.

[0073] The above embodiments are only preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for controlling the three-dimensional shrinkage rate of motorcycle muffler welding, characterized in that: The following steps are involved: (1) Based on the geometric dimensions, material properties and welding process parameters of the muffler, a thermal-mechanical coupling model of the welding process was constructed using finite element analysis software; (2) Identify the area in the model where stress concentration and deformation are most significant during welding, and determine this potential three-dimensional shrinkage deformation area as the "heated stress reduction zone"; (3) During the welding process, sensors are used to collect welding data in real time and monitor the changes in welding temperature field and stress-strain; (4) Use machine learning algorithms to process and analyze monitoring data and establish a mapping relationship between welding parameters and three-dimensional shrinkage deformation; (5) The monitoring data is transmitted to the control system in real time and compared with the preset threshold value. Based on the model prediction results and real-time data feedback, the welding process parameters and the parameters of the "heated stress reduction zone" are dynamically adjusted to reduce the welding stress and accurately control the three-dimensional shrinkage deformation. When adjusting the parameters of the "heated stress reduction zone", it is necessary to ensure that the heating process can cause an appropriate amount of plastic deformation of the material to offset part of the shrinkage generated during the welding process.

2. A motorcycle muffler welding three-dimensional shrinkage control method according to claim 1, characterized in that: The control method adopts a hybrid drive control strategy, including: (1) Initial control strategy setting: Based on the model prediction results, the initial welding process parameters and the "heated stress reduction zone" parameters are set; (2) Real-time adjustment and optimization: During the welding process, the welding process parameters are dynamically adjusted based on the real-time collected data and the prediction results of the machine learning model to maintain a stable temperature field and control the three-dimensional shrinkage deformation, and to improve the effectiveness of controlling the three-dimensional shrinkage deformation and improving the welding quality and product accuracy; (3) Feedback and iteration: After each welding is completed, the actual 3D dimension data is compared with the predicted value to evaluate the effectiveness of the control strategy, and the model parameters and machine learning model are iteratively optimized based on the evaluation results.

3. The method for controlling the three-dimensional shrinkage rate of motorcycle muffler welding according to claim 1, characterized in that: The three-dimensional thermal-mechanical coupling model includes a welding heat source model, a material property model, and a boundary condition setting, which is used to simulate the temperature field distribution and thermal stress changes during the welding process; The construction of the three-dimensional thermal-mechanical coupling model includes: predicting the distribution of temperature field, stress field and deformation field during the welding process through simulation calculation, and generating a corresponding three-dimensional cloud map.

4. A motorcycle muffler welding three-dimensional shrinkage control method according to claim 1, characterized in that: The sensors include infrared thermometers or thermocouples, strain gauges, and displacement sensors, and the temperature, stress strain, and three-dimensional size change data during the welding process are respectively acquired in real time through the sensors.

5. The method for controlling the three-dimensional shrinkage rate of motorcycle muffler welding according to claim 1, characterized in that: The welding process parameter adjustment includes the adjustment of welding speed, current, voltage and heating temperature, and the welding process parameters are adjusted to maintain a stable temperature field and reduce welding stress; The parameter adjustment of the "heated stress reduction zone" includes heating temperature, heating time, and heating position. The three-dimensional shrinkage deformation during welding is controlled by adjusting the parameters of the "heated stress reduction zone".

6. A three-dimensional shrinkage rate control system for motorcycle muffler welding, characterized in that: It includes a control hardware system and a control software system. The control hardware system includes a computer, a sensor system, an integrated control system, a human-computer interaction interface and a welding manipulator. The control software system includes a machine learning algorithm software, a motion control software and a human-computer interaction interface software. The integrated control system receives the output results of the data processing and analysis module, dynamically adjusts the welding process parameters according to the output results of the machine learning algorithm to maintain a stable temperature field and reduce welding stress, dynamically adjusts the heating parameters of the "heated stress reduction zone", and sends control instructions to the welding equipment to achieve precise control.

7. A motorcycle muffler welding three-dimensional shrinkage rate control system according to claim 6, characterized in that: The computer is a high-performance industrial computer or server; the machine learning algorithm software processes and analyzes data from sensors to predict the three-dimensional shrinkage rate during welding.

8. A motorcycle muffler welding three-dimensional shrinkage rate control system according to claim 6, characterized in that: The sensor system is installed near the robot welding gun or on the workpiece, and the temperature sensor is encapsulated and protected using high temperature resistant and corrosion resistant materials, and the signal transmission adopts a shielded cable.

9. A motorcycle muffler welding three-dimensional shrinkage rate control system according to claim 6, characterized in that: The welding process parameters include welding speed, current, voltage, and heating temperature; the "heated stress reduction zone" heating parameters include heating temperature, heating time, and heating position.

10. A motorcycle muffler welding three-dimensional shrinkage rate control system according to claim 6, characterized in that: The human-machine interaction interface enables the user to interact with the control system. The human-machine interaction interface displays the real-time temperature, stress strain, three-dimensional shrinkage deformation and other data during the welding process in real time, and provides a real-time adjustment interface for welding process parameters, allowing the user to make dynamic adjustments as needed; When abnormal conditions occur during welding, the human-computer interaction interface provides an alarm prompt; The human-computer interaction interface provides historical data query function.

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