Data line automatic welding control system

Abstract

The invention relates to the technical field of automatic control of data line welding, in particular to a data line automatic welding control system. The system comprises an image collecting device, a feature recognition device, a motion control device, a parameter monitoring device, a power adjusting device and a closed-loop control system, all the devices communicate with one another through communication interfaces and work cooperatively, and efficient and stable welding operation is achieved. In addition, the system is further provided with an illumination compensation device, an infrared auxiliary light source, a data storage device and a parameter configuration device, and the adaptability and the welding quality of the system are further improved. According to the welding device, the efficient, accurate and stable welding effect is achieved, and the rate of unqualified products is remarkably reduced.

Description

Technical Field

[0001] The present application relates to the technical field of automatic control of data line welding, and in particular to a data line automatic welding control system. Background Art

[0002] With the miniaturization and complexity of electronic products, traditional manual welding can no longer meet the requirements of high precision and high speed, and automated welding has become a key technology to improve production efficiency and product quality. Especially in the data line welding section in the fields of smart phones, automotive electronic accessories and consumer electronic products, the application of automated welding has not only greatly improved production efficiency, but also effectively solved the quality problems existing in manual welding. However, although automated welding technology has made significant progress, accurate positioning and quality consistency are still urgent problems to be solved in the automated welding process of precision electronic components. Most of the existing data line automatic welding control methods rely on preset path planning and fixed pattern matching. These methods guide the welding process through pre-set welding paths and fixed welding parameters, thereby increasing the welding speed. In addition, some systems also integrate simple image recognition functions for preliminary positioning of the data line port. However, these methods usually require manual intervention for initial calibration, and in actual operation, due to the diversity of data line specifications and layouts, these methods have obvious deficiencies in adaptability and flexibility. The current automated control system is difficult to adjust when faced with changing data line specifications and layouts, lacks flexibility, and lacks a real-time feedback correction mechanism. It is unable to effectively monitor and correct minor deviations during the welding process, thus affecting welding quality and stability. Therefore, how to achieve efficient and stable welding control in a changing production environment is a technical problem that needs to be solved urgently. Summary of the invention

[0003] The purpose of this application is to overcome the above technical problems and provide a data line automatic welding control system A data line automatic welding control system includes: an image acquisition module for acquiring image information of a data line to be welded; a deep learning module for identifying the position and shape characteristics of a data line port and determining the coordinates of a welding point; a robot arm control module for dynamically adjusting the motion trajectory of a robot arm according to the coordinates of the welding point to ensure accurate alignment with the welding position; a temperature and pressure monitoring module for real-time monitoring of the temperature and pressure parameters during the welding process; a closed-loop adjustment module for dynamically adjusting the power of a welding head to ensure the quality of the welding point; wherein the modules communicate with each other through a network interface and work in coordination to achieve efficient and stable welding operations. By adopting the above technical solution, the consistency and reliability of welding of data lines of different specifications can be significantly improved. The combination of the image acquisition module and the deep learning module can accurately identify the position and shape characteristics of the data line port and ensure accurate determination of the coordinates of the welding point. The robot arm control module dynamically adjusts the motion trajectory of the robot arm according to the coordinates of the welding point to ensure accurate alignment with the welding position, thereby improving the welding accuracy. The temperature and pressure monitoring module monitors the key parameters in the welding process in real time, and the closed-loop adjustment module dynamically adjusts the power of the welding head to ensure the stability of the quality of the welding point. The network interface communication between each module realizes efficient collaborative work, further improving the overall performance and stability of the system. Preferably, it also includes: an ambient light compensation module, which is used to measure the current lighting conditions and adjust the camera exposure time according to the measurement results; an infrared auxiliary light source, which is used to enhance the contrast of the target object in a low-light or strong light interference environment and improve the image recognition accuracy. By adopting the above technical solution, the image recognition ability of the system in a complex environment is significantly enhanced. The ambient light compensation module can adjust the camera exposure time according to the current lighting conditions to ensure image clarity; the infrared auxiliary light source enhances the contrast of the target object in a low-light or strong light interference environment, further improving the image recognition accuracy. These measures work together to enable the system to stably and reliably identify the data line port under various light conditions, ensure the accuracy of the welding point coordinates, and thus improve the welding quality and yield. Preferably, the ambient light compensation module is also used to automatically turn on or off the infrared auxiliary light source when a change in lighting conditions is detected. By adopting the above technical solution, it is possible to automatically turn on or off the infrared auxiliary light source when a change in lighting conditions is detected, thereby enhancing the contrast of the target object and improving the accuracy and robustness of image recognition, especially in low-light or strong light interference environments, which can effectively improve the recognition accuracy of welding point coordinates and ensure the stability and quality of the welding process. Preferably, it also includes: a database module for storing historical welding parameters; an adaptive parameter configuration module for intelligently predicting and setting the welding parameters that best suit the current material characteristics based on historical welding data, including welding current, time and pressure level.By adopting the above technical solution, the welding quality consistency of different batches of data lines can be significantly improved, the welding failure rate caused by material differences can be greatly reduced, the time and cost of manual debugging can be reduced, and highly intelligent production management can be realized. Preferably, the adaptive parameter configuration module is also used to retrieve the historical records of similar materials from the database before each welding task starts, and adjust the welding parameters accordingly. By adopting the above technical solution, it can be realized that before each welding task starts, the system retrieves the historical records of similar materials from the database, and intelligently predicts and sets the welding parameters that are most suitable for the current material characteristics, including welding current, time and pressure level, so as to minimize the generation of defective products. This measure not only improves the consistency and reliability of welding quality, but also greatly reduces the risk of welding failure caused by material variation, and realizes highly customized high-quality batch production. Preferably, the robot arm control module is also used to preheat the welding head before welding to ensure the stability of the welding process. By adopting the above technical solution, preheating the welding head can ensure the stability of the welding process, avoid welding quality problems caused by too low temperature of the welding head, and thus improve the first-time qualified rate of welding. Preferably, the temperature and pressure monitoring module is also used to immediately send out an alarm signal when it is detected that the welding temperature or pressure exceeds the preset range. By adopting the above technical solution, the temperature and pressure parameters in the welding process are monitored in real time, and when the welding temperature or pressure is detected to be beyond the preset range, an alarm signal can be immediately issued to promptly remind the operator to take measures to prevent the occurrence of welding defects, thereby improving the reliability of the welding process and the quality of the weld. Preferably, the closed-loop adjustment module is also used to dynamically adjust the moving speed of the welding head during the welding process to adapt to different welding requirements. By adopting the above technical solution, it is possible to dynamically adjust the moving speed of the welding head during the welding process to adapt to different welding requirements and ensure the welding quality. This not only improves the flexibility of welding, but also effectively copes with data cables of different specifications and materials, thereby further improving the consistency and reliability of welding. Preferably, the image acquisition module includes a plurality of cameras, which are respectively used to collect image information of the data cable to be welded from different angles. By adopting the above technical solution, it is possible to comprehensively collect image information of the data cable to be welded from multiple angles, improve the integrity and accuracy of the image, thereby improving the reliability of subsequent feature recognition and the accuracy of the welding point coordinates. Preferably, the deep learning module includes a convolutional neural network for extracting characteristic information of the data line port. By adopting the above technical solution, the convolutional neural network can efficiently and accurately extract the characteristic information of the data line port, thereby improving the accuracy and reliability of image recognition. This helps to ensure the accurate generation of welding point coordinates, thereby improving welding accuracy and quality consistency.

[0004] In summary, the present application includes at least one of the following beneficial technical effects: 1. By dynamically adjusting the motion trajectory and welding parameters of the robot arm, the consistency and reliability of welding of data cables of different specifications are significantly improved, and the problem of insufficient adaptability and flexibility in the prior art is solved; 2. Real-time monitoring of temperature and pressure parameters during the welding process, and dynamic adjustment of the welding head power through a closed-loop control system to ensure the quality of the weld, effectively avoid slight deviations in the welding process, and improve welding stability and quality; 3. The introduction of ambient light compensation algorithm and infrared auxiliary light source enhances the image recognition capability in complex environments, and maintains a high welding yield even under poor lighting conditions, solving the problem that the image recognition accuracy in the prior art is greatly affected by light. DETAILED DESCRIPTION

[0005] The technical solutions in the embodiments of the present invention will be described clearly and completely below. The described embodiments are only possible technical implementations of the present invention, not all possible implementations. Those skilled in the art can fully combine the embodiments of the present invention to obtain other embodiments without creative work, and these embodiments are also within the scope of protection of the present invention. The inventors of the present application found that the traditional data line automatic welding control method lacks sufficient flexibility and real-time feedback mechanism when facing the variable data line specifications and layouts, resulting in the problem of unstable welding quality. For this reason, the present application mainly adopts the following data line automatic welding control method to achieve the effect of significantly improving the welding consistency and reliability of data lines of different specifications. The present application is further described in detail below. Embodiment 1 The data line automatic welding control system provided in the embodiment of the present application includes an image acquisition device, a feature recognition device, a motion control device, a parameter monitoring device, a power adjustment device and a closed-loop control system, wherein the devices communicate with each other through a communication interface and work together to achieve efficient and stable welding operations. Specifically, the image acquisition device may include multiple cameras, for example, two or three cameras, which respectively collect image information of the data line to be welded from different angles. These cameras can be high-definition cameras with high resolution and fast response time, and can clearly capture the details of the data line port. The installation position of the camera can be flexibly adjusted to cover a wider viewing angle and ensure the comprehensiveness of the image information. The feature recognition device is mainly used to process image information, identify the position and shape characteristics of the data line port, and generate the coordinates of the welding point. The device can use a convolutional neural network to extract the feature information of the data line port through a deep learning algorithm. The convolutional neural network can continuously optimize the recognition accuracy by training a large amount of sample data. In addition, other types of neural networks, such as recurrent neural networks or support vector machines, can also be used to adapt to different application scenarios. The motion control device dynamically adjusts the motion trajectory of the robot arm according to the coordinates of the welding point to ensure accurate alignment with the welding position. The device may include a servo motor and a transmission mechanism, the servo motor is responsible for driving the movement of the robot arm, and the transmission mechanism ensures the smoothness and accuracy of the movement. The transmission mechanism can be gear drive, belt drive or screw drive, and the specific selection depends on the actual application requirements. In addition, the motion control device also has a preheating function, which preheats the welding head to a preset temperature before welding to ensure the stability of the welding process. The parameter monitoring device is used to monitor the temperature and pressure parameters in the welding process in real time. The device may include a temperature sensor and a pressure sensor, the temperature sensor is used to monitor the temperature of the welding head, and the pressure sensor is used to monitor the pressure of the contact surface between the welding head and the data line. These sensors can be installed on the welding head and transmit data to the closed-loop control system in real time. The temperature sensor can be a thermocouple or an infrared sensor, and the pressure sensor can be a piezoresistive or piezoelectric sensor. The specific choice depends on the requirements of monitoring accuracy and response speed.The power regulating device dynamically adjusts the power of the welding head according to the monitored temperature and pressure parameters to ensure the quality of the weld. The device may include a power amplifier and a controller. The power amplifier is responsible for adjusting the power output of the welding head, and the controller adjusts the output of the power amplifier in real time according to the temperature and pressure data fed back by the sensor. The power regulating device also has an overload protection function. When it is detected that the welding temperature or pressure exceeds the preset range, an alarm signal is immediately issued, and the closed-loop control system is triggered to make corresponding adjustments. The closed-loop control system is used to coordinate the work of each device to ensure the stability and accuracy of the welding process. The system can use a PLC or an industrial computer to communicate with each device through a network interface to monitor and adjust the status of each device in real time. The closed-loop control system can dynamically adjust the working status of each device according to the preset welding parameters and the real-time feedback data to ensure the continuity and stability of the welding process. The implementation principle of this embodiment is: through the cooperation of the image acquisition device and the feature recognition device, the system can accurately identify the position and shape characteristics of the data line port and generate the welding point coordinates. The motion control device dynamically adjusts the motion trajectory of the robot arm according to the welding point coordinates to ensure accurate alignment of the welding position. The parameter monitoring device monitors the temperature and pressure parameters in the welding process in real time, and the power adjustment device dynamically adjusts the welding head power according to these parameters to ensure the quality of the weld. The closed-loop control system coordinates the work of each device to ensure the stability and accuracy of the welding process. This dynamic adjustment and real-time feedback mechanism significantly improves the welding accuracy and quality consistency, and is suitable for welding of data lines of various specifications and layouts. Embodiment 2 The difference between this embodiment and the above embodiment is that an illumination compensation device and an infrared auxiliary light source are added to further improve the robustness of image recognition. Specifically, the illumination compensation device is used to measure the current illumination conditions and adjust the exposure time of the image acquisition device according to the measurement results. The device may include a photoresistor and a controller, the photoresistor is used to detect the current light intensity, and the controller adjusts the exposure time of the camera according to the detection results. When the system is in a low-light or strong light interference environment, the illumination compensation device will automatically adjust the exposure time to make the image information clearer. In addition, the illumination compensation device also has the function of automatically turning on or off the infrared auxiliary light source. When a change in the illumination condition is detected, the infrared auxiliary light source is automatically turned on or off to enhance the contrast of the target object. The infrared auxiliary light source is used to enhance the contrast of the target object in a low-light or strong light interference environment and improve the image recognition accuracy. The light source can be an infrared LED array, installed near the camera, and work synchronously with the camera. The infrared LED array can provide sufficient illumination under low light conditions, making the data line port features more distinct, and improving the recognition success rate and accuracy of the deep learning model. The implementation principle of this embodiment is: by adding an illumination compensation device and an infrared auxiliary light source, the system can maintain a high image recognition accuracy in a low light or strong light interference environment.The illumination compensation device adjusts the exposure time according to the current illumination conditions, and the infrared auxiliary light source enhances the contrast of the target object. The two work together to improve the quality of the image information. This not only helps the feature recognition device to accurately identify the position and shape characteristics of the data line port, but also improves the robustness and adaptability of the entire system, and is suitable for data line welding in various complex environments. Embodiment 3 The difference between this embodiment and the above embodiment is that a data storage device and a parameter configuration device are added to realize adaptive parameter configuration based on historical data analysis. Specifically, the data storage device is used to store historical welding parameters, which may include welding current, time, and pressure level. The data storage device can be a database stored in a server or a local storage device. After each welding task is completed, the system automatically saves the welding parameters to the database for reference for subsequent tasks. The parameter configuration device is used to intelligently predict and set the welding parameters that best suit the current material properties based on historical welding data. The device may include a machine learning algorithm and a controller, and the machine learning algorithm is used to analyze historical welding data and establish a relationship model between material and optimal welding parameters. The controller intelligently predicts and sets the welding parameters that best suit the current material properties based on the model. Before each welding task begins, the parameter configuration device retrieves the historical records of similar materials from the data storage device, and intelligently predicts and sets the welding parameters, including welding current, time and pressure level, based on this, to minimize the generation of defective products. The implementation principle of this embodiment is: by adding a data storage device and a parameter configuration device, the system can adaptively configure the welding parameters according to the historical welding data to adapt to the differences in data lines of different materials and batches. The data storage device stores a large number of historical welding parameters, and the parameter configuration device uses a machine learning algorithm to analyze these data and establish a relationship model between the material and the optimal welding parameters. This adaptive parameter configuration mechanism not only reduces the debugging cost, but also reduces the risk of welding failure caused by material variation, and realizes highly customized high-quality batch production. Example 4 The difference between this embodiment and the above embodiment is that a preheating function before welding is added to ensure the stability of the welding process. Specifically, in addition to dynamically adjusting the motion trajectory of the robot arm, the motion control device also has a preheating function. Before welding, the motion control device preheats the welding head to a preset temperature to ensure the stability of the welding process. The preheating function can be realized by a heating element and a temperature controller. The heating element can be a resistance wire or a ceramic heating plate, and the temperature controller adjusts the power output of the heating element according to the preset temperature value. The preheating time can be adjusted according to actual needs to ensure that the welding head reaches the ideal welding temperature. The implementation principle of this embodiment is: by adding a preheating function before welding, the system can preheat the welding head to a preset temperature before welding to ensure the stability of the welding process. The preheating function can prevent the welding head from being directly welded in a cold state and reduce the impact of temperature fluctuations during the welding process.This not only improves the welding quality, but also extends the service life of the welding head, and is suitable for a production environment with long-term continuous work. Embodiment 5 The difference between this embodiment and the above embodiment is that the function of dynamically adjusting the moving speed of the welding head during welding is added to adapt to different welding requirements. Specifically, in addition to dynamically adjusting the welding head power according to the monitored temperature and pressure parameters, the power regulating device also has the function of dynamically adjusting the moving speed of the welding head. This function can adjust the moving speed of the welding head in real time according to the welding requirements to ensure the welding quality. The dynamic adjustment of the moving speed of the welding head can be achieved by a servo motor and a controller. The servo motor is responsible for driving the movement of the welding head. The controller dynamically adjusts the speed output of the servo motor according to the welding requirements and real-time feedback data. This dynamic adjustment mechanism can adapt to the welding of data lines of different thicknesses and materials to ensure the quality of each section of welding. The implementation principle of this embodiment is: by adding the function of dynamically adjusting the moving speed of the welding head during welding, the system can adjust the moving speed of the welding head in real time according to the welding requirements to ensure the welding quality. Dynamic adjustment of the moving speed of the welding head can avoid the influence of too fast or too slow speed during welding, and improve the uniformity and stability of welding. This flexible adjustment mechanism is suitable for a variety of welding scenarios and improves the adaptability and reliability of the system. The present application also provides a data line automatic welding control method, comprising the following steps: S1, collecting image information of the data line to be welded, using a deep learning algorithm to identify the position and shape characteristics of the data line port, and determining the coordinates of the welding point. S2, dynamically adjusting the motion trajectory of the robot arm according to the coordinates of the welding point to ensure accurate alignment of the welding position. S3, preheating the welding head to a preset temperature before welding to ensure the stability of the welding process. S4, starting the welding process, and monitoring the temperature and pressure parameters during the welding process in real time. S5, dynamically adjusting the welding head power according to the monitored temperature and pressure parameters to ensure the quality of the weld. S6, if it is detected that the welding temperature or pressure exceeds the preset range, immediately issue an alarm signal and trigger the closed-loop control system to make corresponding adjustments. S7, after welding is completed, save the welding parameters to a data storage device for reference in subsequent tasks. S8, before each welding task starts, retrieve the historical records of similar materials from the data storage device, and intelligently predict and set the welding parameters that best suit the current material characteristics, including welding current, time and pressure level, to minimize the generation of defective products. The implementation principle of this embodiment is: through the above steps, the system can realize the full process automation control from image acquisition, feature recognition, motion control to parameter monitoring and power regulation, ensuring the stability and quality consistency of the welding process. Especially in the face of changing data line specifications and layouts, the system significantly improves the welding accuracy and reliability by dynamically adjusting the motion trajectory and welding parameters.In addition, through the real-time feedback mechanism and adaptive parameter configuration, the system can adapt to various complex environments and data line welding of different materials, and achieve efficient and stable mass production. The above are all preferred embodiments of this application, and the protection scope of this application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of this application should be covered within the protection scope of this application.

Claims

1. A data line automatic welding control system, characterized in that: include: An image acquisition device, used to obtain image information of the data wire to be welded; A feature recognition device is used to process image information, identify the position and shape characteristics of the data line port, and generate welding point coordinates; a motion control device is used to dynamically adjust the motion trajectory of the robot arm according to the welding point coordinates to ensure accurate alignment of the welding position; a parameter monitoring device is used to monitor the temperature and pressure parameters during the welding process in real time; a power adjustment device is used to dynamically adjust the welding head power according to the monitored temperature and pressure parameters to ensure the quality of the weld; a closed-loop control system is used to coordinate the work of each device to ensure the stability and accuracy of the welding process; each device communicates with each other through a communication interface and works together to achieve efficient and stable welding operations.

2. The data line automatic welding control system according to claim 1, characterized in that: Also includes: An illumination compensation device, used to measure the current illumination conditions and adjust the exposure time of the image acquisition device according to the measurement results; Infrared auxiliary light source is used to enhance the contrast of target objects in low-light or strong light interference environments and improve image recognition accuracy.

3. The data line automatic welding control system according to claim 2, characterized in that: The illumination compensation device is also used to automatically turn on or off the infrared auxiliary light source when a change in illumination conditions is detected.

4. The data line automatic welding control system according to claim 1, characterized in that: Also includes: A data storage device is used to store historical welding parameters; a parameter configuration device is used to intelligently predict and set the welding parameters that best suit the current material characteristics based on historical welding data, including welding current, time, and pressure level, so as to minimize the generation of defective products.

5. The data line automatic welding control system according to claim 4, characterized in that: The parameter configuration device is also used to retrieve the historical records of similar materials from the data storage device before each welding task begins, and intelligently predict and set the welding parameters that best suit the current material properties, including welding current, time and pressure level, so as to minimize the generation of defective products.

6. The data line automatic welding control system according to claim 1, characterized in that: The motion control device is also used to preheat the welding head to a preset temperature before welding to ensure the stability of the welding process.

7. The data line automatic welding control system according to claim 1, characterized in that: The parameter monitoring device is also used to immediately send out an alarm signal when it detects that the welding temperature or pressure exceeds a preset range, and trigger the closed-loop control system to make corresponding adjustments.

8. The data line automatic welding control system according to claim 1, characterized in that: The power regulating device is also used to dynamically adjust the moving speed of the welding head during the welding process to adapt to different welding requirements and ensure the welding quality.

9. The data line automatic welding control system according to claim 1, characterized in that: The image acquisition device includes a plurality of cameras, which are respectively used to acquire image information of the data wire to be welded from different angles.

10. The data line automatic welding control system according to claim 1, characterized in that: The feature recognition device includes a convolutional neural network, which is used to extract feature information of the data line port.