Metal pipe part welding path intelligent planning and monitoring system

Abstract

The invention relates to the technical field of automatic manufacturing, and discloses a metal pipe part welding path intelligent planning and monitoring system which comprises a path planning module used for generating an optimal welding path according to the geometrical shape of a metal pipe part, welding process requirements and sensor feedback information; the sensor module monitors key parameters in the welding process in real time through a visual sensor, a thermal imaging sensor and a force sensor; the data processing and analyzing module is used for processing and analyzing the real-time data acquired by the sensor; the welding control module is used for controlling operation of welding equipment according to path planning and sensor feedback data; and the quality monitoring and feedback module is used for monitoring the quality in the welding process in real time. According to the system, the welding path is generated through the intelligent path planning module, and the path is dynamically optimized in combination with real-time sensor data, so that the accuracy of the welding process is improved, the system can adapt to changes in a complex welding environment, and the welding efficiency and quality are improved.

Description

Technical Field

[0001] The present invention relates to the field of automated manufacturing technology, and particularly to an intelligent planning and monitoring system for welding paths of metal pipe parts. Background Art

[0002] In the existing welding process of metal pipe parts, it usually relies on manual path planning and adjustment. When a welding robot executes a task, it mainly operates according to a preset path. To ensure the welding quality, the operator usually needs to manually adjust welding parameters such as welding current, voltage, speed, etc. according to the shape, size, and welding requirements of the parts.

[0003] In the prior art, welding path planning often relies on manual experience, and the accuracy and optimization degree of the path cannot be effectively guaranteed. The traditional path planning lacks a response mechanism to environmental changes (such as thermal expansion, stress changes, etc.) during the actual welding process, which easily leads to path deviation and thus affects the welding quality. In addition, once the path is set, it is very difficult to adjust according to the real-time changing environmental conditions during welding. The welding robot cannot make dynamic adjustments according to factors such as the change of the heat affected zone and stress concentration during the welding process, ultimately resulting in unstable welding quality. Summary of the Invention

[0004] To make up for the above deficiencies, the present invention provides an intelligent planning and monitoring system for welding paths of metal pipe parts, aiming to improve the problem that the traditional path planning lacks a response mechanism to environmental changes (such as thermal expansion, stress changes, etc.) during the actual welding process, which easily leads to path deviation and thus affects the welding quality.

[0005] In a first aspect, the present invention provides the following technical solution. An intelligent planning and monitoring system for welding paths of metal pipe parts includes: A path planning module, which is used to generate an optimal welding path according to the geometric shape, welding process requirements of metal pipe parts, and sensor feedback information, and optimize the path according to real-time feedback; A sensor module, which monitors key parameters during the welding process in real time through a vision sensor, a thermal imaging sensor, and a force sensor; A data processing and analysis module, which is used to process and analyze the real-time data collected by the sensor, and generate welding quality evaluation and defect prediction information; A welding control module, which controls the operation of the welding equipment according to the path planning and sensor feedback data; A quality monitoring and feedback module, which monitors the quality during the welding process in real time, detects welding defects, and provides adjustment feedback.

[0006] Preferably, the path planning module includes: A path generation unit that generates a preliminary welding path based on the geometric shape and welding position of metal pipe parts; A path optimization unit that optimizes the welding path according to real-time sensing data; A collision detection and avoidance unit that detects and avoids possible collisions in real time by monitoring the movement path of the welding robot and the environment.

[0007] Preferably, the sensor module includes: A vision sensor unit that captures images of the welding process through a high-definition camera or a three-dimensional depth camera, identifies the welding seam and surface conditions, and provides visual data; A thermal imaging sensor unit that uses infrared thermal imaging technology to monitor the temperature change in the welding area in real time and feedback the thermal distribution in the welding area; A force sensor unit that monitors the stress and deformation of metal parts during the welding process through piezoelectric sensors or strain gauge sensors to obtain mechanical data during the welding process.

[0008] Preferably, the data processing and analysis module includes: A data acquisition unit that collects data from each sensor in real time and performs preliminary signal processing to output valid information; A data fusion and analysis unit that fuses and analyzes data from multiple sensors through the Kalman filter algorithm and machine learning models to extract key parameters; A defect prediction and evaluation unit that predicts the type of welding defects and evaluates the welding quality through a deep learning model based on historical welding data and real-time data; The data processing and analysis module performs real-time data preprocessing and analysis through an edge computing device to reduce data transmission latency and improve processing efficiency.

[0009] Preferably, the welding control module includes: A welding parameter adjustment unit that automatically adjusts welding parameters according to real-time sensor feedback, including current, speed, and angle; A robot control unit that controls the precise movement of the welding robot on a predetermined path and executes path planning; A process control and compensation unit that compensates for errors during the welding process according to real-time sensor data and adjusts the welding path and parameters.

[0010] Preferably, the quality monitoring and feedback module includes: A real-time quality monitoring unit that monitors the welding quality in real time through vision and sensor data and detects welding defects, including cracks and pores; A welding defect detection unit that automatically identifies welding defects in the weld seam using a deep learning model and generates a defect report; A quality feedback and adjustment unit, when detecting welding defects, automatically sends feedback signals to the welding control module to adjust welding parameters or paths.

[0011] Preferably, it further includes a user interface and a control module, and the user interface and control module include: A status display unit for displaying status information, progress information, and quality monitoring information during the welding process; A parameter adjustment unit that allows an operator to manually adjust welding parameters to meet special welding requirements; An alarm and prompt unit that emits alarm signals and provides handling suggestions when abnormalities occur during the welding process.

[0012] In a second aspect, the present invention provides the following technical solution, a method for intelligent planning and monitoring of welding paths for metal pipe parts, including the following steps: S1. A path planning step, according to the geometric shape of metal pipe parts, welding process requirements, and real-time sensor feedback information, generates a preliminary welding path and optimizes the path so that the welding path meets the welding quality requirements; S2. A sensor data acquisition step, through vision sensors, thermal imaging sensors, and force sensors, real-time collects data of key parameters during the welding process to obtain the welding environment information of metal pipe parts; S3. A data processing and analysis step, preprocesses, fuses, and analyzes the data collected by the sensors, uses the Kalman filter algorithm and machine learning models to process the data, evaluates the welding quality, and predicts possible defects; S4. A welding control step, according to the path planning result and sensor feedback data, automatically adjusts welding parameters, controls the welding robot to move along the optimal path, and executes the welding task; S5. A quality monitoring and feedback step, real-time monitors the quality during the welding process, identifies welding defects through image processing and deep learning models, automatically generates defect reports, and transmits feedback information to the welding control module to adjust welding parameters or paths.

[0013] In a third aspect, the invention provides the following technical solution, a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, it implements the above-mentioned method for intelligent planning and monitoring of welding paths for metal pipe parts.

[0014] In a fourth aspect, the present invention provides the following technical solution, a readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the above-mentioned method for intelligent planning and monitoring of welding paths for metal pipe parts.

[0015] The present invention has the following beneficial effects: 1. In the present invention, the welding path is generated by the intelligent path planning module and dynamically optimized in combination with real-time sensor data. This automated path planning and real-time adjustment not only improve the accuracy of the welding process but also adapt to changes in complex welding environments (such as thermal expansion and stress changes), greatly enhancing the welding efficiency and quality. Traditional manual welding path planning relies on experience and is prone to errors, while this system can accurately calculate the path, avoiding welding unevenness and defects caused by human factors.

[0016] 2. In the present invention, key parameters during the welding process are monitored in real time through multiple sensors (such as vision, thermal imaging, and force sensors), and welding defects are predicted through the data processing and analysis module. This real-time quality monitoring and defect prediction mechanism enables welding defects to be detected before they occur and corrected in a timely manner. For example, common defects such as pores and cracks can be identified before they affect the final quality, and the welding parameters are automatically adjusted by the system for repair, significantly improving the welding stability and product quality.

[0017] 3. In the present invention, by combining machine vision and sensor data, this system can accurately control the welding robot to perform operations along the planned path. The system calibrates the robot's motion trajectory in real time to ensure the position accuracy during the welding process. This innovative technology makes the welding of complex-shaped and non-standard metal pipe fittings more efficient and precise. Especially in the welding of complex metal pipes, the robot can avoid collisions, adjust the path, improve the welding accuracy, reduce errors, and ensure the consistency of welding quality.

[0018] 4. In the present invention, the welding control module can automatically adjust welding parameters (such as current, speed, and angle) according to sensor data to ensure that key factors such as temperature and stress during the welding process are within the optimal range. In the traditional welding process, operators often need to adjust parameters based on experience, while this system optimizes the parameters in real time during the welding process through an automated mechanism, avoiding the uncertainty in the manual adjustment process. This real-time feedback and adjustment not only reduce manual intervention but also significantly improve production efficiency and welding consistency. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is the overall architecture diagram of the intelligent welding path planning and monitoring system for metal pipe parts proposed by the present invention; Figure 2 is the architecture diagram of the path planning module of the intelligent welding path planning and monitoring system for metal pipe parts proposed by the present invention; Figure 3 is the architecture diagram of the sensor module of the intelligent welding path planning and monitoring system for metal pipe parts proposed by the present invention; Figure 4 It is the architecture diagram of the data processing and analysis module of the intelligent welding path planning and monitoring system for metal pipe parts proposed by the present invention; Figure 5 It is the architecture diagram of the welding control module of the intelligent welding path planning and monitoring system for metal pipe parts proposed by the present invention; Figure 6 It is the architecture diagram of the quality monitoring and feedback module of the intelligent welding path planning and monitoring system for metal pipe parts proposed by the present invention; Figure 7 It is the architecture diagram of the user interface and control module of the intelligent welding path planning and monitoring system for metal pipe parts proposed by the present invention; Figure 8 It is the flow chart of the intelligent welding path planning and monitoring method for metal pipe parts proposed by the present invention. Specific embodiments

[0020] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0021] Embodiment 1 Refer to Figures 1 - 7 , in the first embodiment of the present invention, the present invention provides an intelligent welding path planning and monitoring system for metal pipe parts, including: A path planning module, which is used to generate an optimal welding path according to the geometric shape, welding process requirements and sensor feedback information of metal pipe parts, and optimize the path according to real-time feedback; A sensor module, which uses visual sensors, thermal imaging sensors and force sensors to monitor key parameters during the welding process in real time; A data processing and analysis module, which is used to process and analyze the real-time data collected by the sensors to generate welding quality evaluation and defect prediction information; A welding control module, which controls the operation of the welding equipment according to the path planning and sensor feedback data; A quality monitoring and feedback module, which monitors the quality during the welding process in real time, detects welding defects and provides adjustment feedback.

[0022] Specifically, the intelligent planning and monitoring system for the welding path of metal pipe parts significantly improves the accuracy and stability of the welding process through automated path planning and real-time optimization, real-time quality monitoring and defect prediction, high-precision welding path and position control, and an automated feedback and parameter adjustment mechanism. The system can dynamically optimize the welding path based on real-time sensor data, adapt to complex changes in the welding environment, avoid the occurrence of welding defects, and precisely control the position and path of the welding robot to ensure the accuracy of each welding point. The automated parameter adjustment mechanism eliminates the errors of manual adjustment, ensures that the welding process is always in the best state, thereby improving the consistency and stability of welding quality, reducing the rework rate, and enhancing the overall production efficiency.

[0023] The path planning module includes: A path generation unit that generates a preliminary welding path based on the geometric shape and welding position of metal pipe parts; A path optimization unit that optimizes the welding path according to real-time sensing data; A collision detection and avoidance unit that monitors the movement path of the welding robot and the environment, and detects and avoids possible collisions in real time.

[0024] Specifically, the core task of the path planning module is to generate a suitable welding path according to the geometric characteristics of metal pipe parts and the requirements of the welding task, ensure welding quality, and optimize the path through real-time adjustment. Its main functions include path generation, path optimization, and collision detection.

[0025] The path generation unit generates a preliminary welding path by collecting the geometric data of metal pipe parts and according to their welding requirements, using graphic processing algorithms (such as the A* algorithm or Dijkstra algorithm). The path generation unit not only considers the geometric shape of the parts, but also sets details such as path spacing and welding direction according to the welding process requirements to ensure the optimization of the welding process.

[0026] This process ensures the rationality of the welding path and lays the foundation for subsequent welding operations.

[0027] The path optimization unit relies on real-time sensor data, especially temperature, welding speed, and stress information, to dynamically adjust the welding path. This unit uses adaptive optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms) to ensure that the welding path can maximize its adaptation to environmental changes. For example, when there is thermal expansion, deformation, or inconsistent welding progress, it can automatically adjust the welding trajectory.

[0028] The optimized path can improve welding quality, reduce welding defects, avoid the generation of heat-affected zones, increase welding efficiency and the service life of parts.

[0029] The path planning module also includes a collision detection unit, which is used to ensure that the robot does not collide with the working environment or other equipment when executing the welding path. This unit uses spatial geometry algorithms (such as the fast convex hull algorithm) to detect the path and combines the robot kinematic model to predict and avoid possible collisions.

[0030] Through collision detection, damage to machine equipment and path planning failures are avoided, ensuring the smooth progress of the welding operation.

[0031] The sensor module includes: A vision sensor unit that captures images of the welding process through a high-definition camera or a 3D depth camera, identifies the welding seam and surface conditions, and provides visual data; An infrared thermal imaging sensor unit that uses infrared thermal imaging technology to monitor the temperature changes in the welding area in real time and feedback the thermal distribution of the welding area; A force sensor unit that monitors the stress and deformation of metal parts during the welding process through piezoelectric sensors or strain gauge sensors to obtain mechanical data during the welding process.

[0032] Specifically, the sensor module uses high-precision sensors to monitor multiple parameters during the welding process in real time, providing reliable real-time data support for path planning and quality monitoring. The sensor module includes a vision sensor, an infrared thermal imaging sensor, and a force sensor.

[0033] The vision sensor unit includes a high-definition RGB camera and a 3D depth sensor (such as Kinect, Intel RealSense). These sensors are used to collect image data during the welding process, identify the welding seam, the welding surface conditions, and defects. The depth camera can capture the three-dimensional shape of an object, further improving the accuracy of path planning.

[0034] High-precision image acquisition can detect minute deviations during the welding process in real time and assist in defect diagnosis, such as pores, cracks, etc., improving the welding quality.

[0035] The infrared thermal imaging sensor uses infrared thermal imaging technology to monitor the temperature distribution in the welding area, providing thermodynamic information of the welding area in a timely manner. This information can help the welding control system optimize the heat input during the welding process, thereby reducing the heat-affected zone and deformation.

[0036] Through temperature monitoring, situations of overheating or too rapid cooling can be avoided, ensuring the stability of the welding process, and thus effectively improving the welding quality.

[0037] The force sensing sensor unit monitors the mechanical changes of the parts during the welding process through piezoelectric sensors or strain gauge sensors. These sensors can sense the stress changes on the surface of the parts and the minute deformations at the welding positions in real time, providing data support for dynamic path adjustment and welding compensation.

[0038] This sensor helps detect stress concentration and deformation problems that may occur during the welding process in real time, enabling the system to adjust the welding parameters in real time and avoid defects caused by mechanical changes.

[0039] The data processing and analysis module includes: The data acquisition unit collects data from each sensor in real time and performs preliminary signal processing, outputting effective information; The data fusion and analysis unit fuses and analyzes data from multiple sensors through the Kalman filter algorithm and machine learning models, extracting key parameters; The defect prediction and evaluation unit predicts the types of welding defects and evaluates the welding quality based on historical welding data and real-time data through a deep learning model; The data processing and analysis module performs real-time data preprocessing and analysis through an edge computing device to reduce data transmission latency and improve processing efficiency.

[0040] Specifically, the data processing and analysis module is the brain of the system. It is responsible for collecting and fusing various data from each sensor, performing intelligent analysis, defect prediction, and welding quality evaluation. The key technologies of this module include data acquisition, data fusion, and defect prediction.

[0041] The data acquisition unit collects raw data from vision, thermal imaging, and force sensing sensors in real time, and removes noise through filtering and preprocessing techniques to ensure the accuracy and reliability of the data. This unit is also responsible for converting the sensor data into a unified format for subsequent analysis.

[0042] Efficient acquisition and preprocessing ensure that the system can obtain the data of the welding environment in real time and accurately, providing support for subsequent analysis and decision-making.

[0043] The data fusion and analysis unit uses the Kalman filter algorithm to integrate data from different sensors, eliminating redundancy and improving data accuracy. Through deep learning and machine learning algorithms (such as support vector machines, neural networks), key parameters during the welding process are analyzed to evaluate the welding quality in real time.

[0044] Data fusion and analysis can improve the accuracy of defect prediction, detect potential problems in welding in advance, and provide real-time adjustment suggestions for the system, improving the welding quality.

[0045] The defect prediction and evaluation unit uses machine learning models to analyze real-time sensor data and predict possible defects (such as porosity, cracks, incomplete penetration, etc.) during the welding process. This unit can dynamically adjust welding parameters based on historical data and real-time feedback to prevent welding defects.

[0046] By predicting defects in advance, the system can make intelligent adjustments to avoid the occurrence of defects, improve welding quality and reduce the rework rate.

[0047] The welding control module includes: The welding parameter adjustment unit automatically adjusts welding parameters according to real-time sensor feedback, including current, speed, and angle; The robot control unit controls the precise movement of the welding robot along a predetermined path and executes path planning; The process control and compensation unit compensates for errors during the welding process based on real-time sensor data and adjusts the welding path and parameters.

[0048] Specifically, the welding control module adjusts welding parameters and controls the welding robot to execute tasks based on path planning, sensor feedback, and data analysis results. The functions of the welding control module include welding parameter adjustment, robot control, and process compensation.

[0049] The welding parameter adjustment unit adjusts parameters such as welding current, welding speed, and welding angle in real time according to sensor feedback (such as temperature, stress, etc.) to ensure the stability of the welding process. This unit uses a PID control algorithm or a fuzzy control algorithm for precise adjustment during the welding process.

[0050] By automatically adjusting welding parameters, welding defects such as insecure welding and uneven heat input can be effectively avoided, ensuring welding quality.

[0051] The robot control unit precisely controls the movement of the welding robot to ensure that the robot accurately executes the welding task along the preset path. This unit performs real-time path correction and dynamic adjustment based on the robot kinematic model.

[0052] High-precision robot control can ensure the stability and accuracy of the welding process, avoid welding defects caused by inaccurate paths, and significantly improve production efficiency and welding quality.

[0053] The process control and compensation unit detects errors during the welding process through real-time sensor data and automatically compensates for path deviations and parameter errors to ensure the efficiency and stability of the welding process.

[0054] This unit can perform real-time error correction during the welding process, avoid quality problems, reduce the defect rate, and ensure the stability of welding quality.

[0055] The quality monitoring and feedback module includes: A real-time quality monitoring unit that monitors the welding quality in real time through visual and sensor data, and detects welding defects, including cracks and pores; A welding defect detection unit that uses a deep learning model to automatically identify defects in the weld seam and generate a defect report; A quality feedback and adjustment unit that automatically sends a feedback signal to the welding control module when a welding defect is detected, and adjusts the welding parameters or path.

[0056] Specifically, the quality monitoring and feedback module ensures the welding quality by monitoring the welding process in real time, detecting and feedbacking welding defects in a timely manner.

[0057] The real-time quality monitoring unit monitors the appearance state of the weld seam in real time through visual sensors and other sensor data, and detects common defects such as pores and cracks.

[0058] Real-time monitoring can detect welding defects in a timely manner, avoid the expansion of defects, and thus improve the welding quality.

[0059] The welding defect detection unit analyzes the images and sensor data collected during the welding process through deep learning algorithms, and automatically identifies and classifies defects.

[0060] Through automatic defect detection, the need for manual intervention can be reduced, and the accuracy and efficiency of welding defect detection can be improved.

[0061] When the quality feedback and adjustment unit detects a welding defect, it immediately sends an adjustment signal to the welding control module for automatic adjustment or alarm processing to ensure that the welding quality meets the standards.

[0062] This feedback mechanism can respond quickly when defects occur, make timely adjustments, reduce the impact of defects on production, and improve the overall production efficiency and welding quality.

[0063] It also includes a user interface and control module, and the user interface and control module includes: A status display unit for displaying status information, progress information, and quality monitoring information during the welding process; A parameter adjustment unit that allows the operator to manually adjust the welding parameters to meet special welding requirements; An alarm and prompt unit that sends an alarm signal and provides handling suggestions when an abnormality occurs during the welding process.

[0064] Specifically, the user interface and control module provides an interactive interface for the system operator with the intelligent welding path planning and monitoring system. This module not only displays real-time welding data, but also allows the operator to manually adjust welding parameters, monitor welding progress and quality, obtain alerts in a timely manner and make corresponding adjustments. The design of the user interface and control module makes the welding operation more intelligent and user-friendly, and provides a convenient operation experience for the operator through a simple and easy-to-understand interface.

[0065] The status display unit shows various key parameters during the welding process to the operator through a graphical interface, such as welding path, welding temperature, welding current, welding speed, etc. The real-time display function is achieved through data visualization technology, including the real-time trajectory of the welding path, temperature change graph, real-time detection of welding defects, etc. This unit can present data in the form of real-time charts, curves or 3D models, helping the operator quickly understand the welding process and make decisions.

[0066] Through intuitive data display and graphical interface, the operator can clearly understand the various parameters during the welding process, reduce the possibility of human error, and improve production efficiency.

[0067] The parameter adjustment unit allows the operator to manually or automatically adjust welding parameters according to the actual needs of the welding task, such as welding current, welding speed, welding angle, welding sequence, etc. This unit provides an intuitive control panel, allowing the operator to quickly modify parameters, and predicting the impact of different parameter adjustments on welding quality through software simulation and real-time feedback. In the automatic mode, this unit can automatically optimize parameters according to the data analysis results and adjust them in real time.

[0068] This unit provides a high degree of flexibility for the operator, enabling quick adjustment according to different welding conditions and actual needs, which not only improves the flexibility of the welding process, but also improves the stability of welding quality.

[0069] The alarm and prompt unit monitors the abnormalities of welding quality and process parameters during the welding process and issues alarms to the operator in a timely manner. This unit can detect welding defects based on real-time data, such as welding cracks, pores, uneven welding, etc., and automatically trigger alarms. The alarm forms include sound, graphic or text warnings, and detailed information about the problem is provided through the interface, instructing the operator to make corrections. The system can also provide the location information of the defect to help the operator locate the problem area more quickly.

[0070] The alarm and prompt unit can issue abnormal warnings in a timely manner, avoiding quality degradation caused by problems during the welding process. Its real-time feedback function can improve production efficiency, reduce welding defects, and ensure that the welding quality meets the standards.

[0071] Embodiment 2: Refer to Figure 8, in the second embodiment of the present invention, the present invention provides an intelligent planning and monitoring method for the welding path of metal pipe parts, including the following steps: S1. Path planning step: According to the geometric shape of the metal pipe parts, welding process requirements, and real-time sensor feedback information, generate a preliminary welding path and optimize the path so that the welding path meets the welding quality requirements; S2. Sensor data acquisition step: Through vision sensors, thermal imaging sensors, and force sensors, collect data of key parameters during the welding process in real time to obtain the welding environment information of the metal pipe parts; S3. Data processing and analysis step: Preprocess, fuse, and analyze the data collected by the sensors. Use the Kalman filter algorithm and machine learning models to process the data, evaluate the welding quality, and predict possible defects; S4. Welding control step: According to the path planning results and sensor feedback data, automatically adjust the welding parameters, control the welding robot to move along the optimal path, and perform the welding task; S5. Quality monitoring and feedback step: Monitor the quality during the welding process in real time. Identify welding defects through image processing and deep learning models, automatically generate defect reports, and transmit the feedback information to the welding control module to adjust the welding parameters or path.

[0072] Specifically, S1: Path planning and generation, metal pipe geometric data acquisition: Use a 3D scanner to scan each metal pipe part to obtain its accurate geometric data. Assume that the diameter of the scanned pipe fittings is between 50 mm and 80 mm, and the length is between 300 mm and 600 mm.

[0073] Path generation: Based on the part geometry and welding requirements, the system generates a preliminary welding path through the A* algorithm. Assume that the welding path spacing is set to 2 mm, and the preliminary path length of each pipe fitting is 1500 mm (calculated based on the actual scanned data).

[0074] Path optimization: According to the temperature (through the thermal imaging sensor) and stress data (through the force sensor) feedback in real time, the system performs path optimization, adjusts the path length and welding angle. Assume that the optimized path length is adjusted to 1450 mm, reducing the unnecessary heat affected zone.

[0075] S2: Sensor data acquisition and analysis, vision sensor unit: During the welding process, the system monitors the welding seam and surface state through a high-definition camera, and obtains the image data of each welding point in real time. 100 frames of image data are collected per second, which can accurately detect the deviation of the weld seam.

[0076] Thermal imaging sensor unit: It monitors the temperature distribution of the welding area in real time. The system records the temperature data and ensures that the welding temperature is maintained between 1400°C and 1500°C. Assuming that at the start of welding, the temperature of the welding area is 1200°C, after optimization, the temperature is precisely adjusted to 1450°C.

[0077] Force sensor unit: During the welding process, it monitors the mechanical changes on the surface of the part in real time. The feedback data from the force sensor is between 0.15 N and 0.25 N. Assuming that when the force change in the stress concentration area during welding is greater than 0.3 N, the system will issue a warning and automatically adjust the path.

[0078] S3: Data processing and analysis, data fusion: The data collected from different sensors (visual data, temperature data, mechanical data) is subjected to Kalman filtering to eliminate noise and fuse information. Assuming that after data fusion, the welding path deviation is reduced by 0.5 mm, improving the path accuracy.

[0079] Defect prediction and quality analysis: The real-time sensor data is analyzed through a deep learning model. Assuming that the model predicts that the probability of welding defects occurring is 5% (such as cracks or pores). The system adjusts the welding current and speed in a timely manner during the welding process to prevent defects from occurring.

[0080] S4: Welding control, welding parameter adjustment: The system automatically adjusts the welding current according to the real-time sensor feedback data (such as welding temperature, mechanical stress). Assuming that the current before adjustment is 200 A, after adjustment, the current stabilizes at 180 A, and the welding speed is adjusted from 15 mm / s to 12 mm / s.

[0081] Robot control: Controls the welding robot to execute precisely according to the optimized path. Assuming that the speed of the robot during welding is adjusted from the preset 250 mm / s to 200 mm / s to ensure the stability of the welding process.

[0082] S5: Quality monitoring and feedback, real-time quality monitoring: Monitors the welding quality in real time. The system detects the welding seam through image recognition technology. Assuming that 1000 pixel points are subjected to quality detection per second, the probability of detecting welding defects (such as pores, cracks) is less than 0.2%.

[0083] Feedback adjustment: When a defect is detected, the system sends a feedback signal to the welding control module to adjust the welding current or speed. For example, when a pore is detected, the system immediately reduces the welding current to 150 A and extends the welding time by 5 seconds to ensure welding integrity.

[0084] Data Storage: Key data for each welding process, such as welding path, welding parameters, quality monitoring results, etc., are stored in the system. The data is updated every 10 seconds, and the system generates a welding quality report. Assume the welding quality report contains the following data: Welding path length: 1450mm Welding current: 180A Welding temperature: 1450℃ Detected defect rate: 0.1% Report Generation: The generated welding report is uploaded to the quality management system for managers and engineers to view and analyze. Assume this report is the average welding data for 100 parts in this batch, among which the welding quality of 98 parts is completely qualified, and the defect rate is 2%.

[0085] Welding Precision: The optimized path and adjustment of welding parameters in the system reduce the deviation during the welding process. The welding path deviation is reduced from ±2mm in traditional manual welding to ±0.5mm, significantly improving the welding precision.

[0086] Welding Defect Rate: Through real-time monitoring and automatic adjustment, the welding defect rate is reduced from 5% in the traditional method to 0.1%, effectively avoiding common defects such as cracks and pores, and ensuring the welding quality.

[0087] Production Efficiency: The optimized adjustment of welding speed and current improves the production efficiency. Assume the welding time is reduced from 60 seconds per part to 50 seconds, and the overall production efficiency is increased by 16.7%.

[0088] Quality Stability: The real-time quality monitoring and feedback mechanism helps to detect problems in a timely manner and make adjustments, greatly improving the stability of welding quality and reducing the rework rate and scrap rate.

[0089] Example 3 In the third embodiment of the present invention, based on the same inventive concept, a computer-readable storage medium is proposed. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it realizes the steps of the method for intelligent planning and monitoring of the welding path of metal pipe parts in the above embodiment.

[0090] Example 4 In the fourth embodiment of the present invention, based on the same inventive concept, a computer device is proposed. The terminal includes: a processor and a memory; the processor and the memory communicate with each other; the memory is used to store instructions; the processor is used to execute the instructions in the memory to execute the method for intelligent planning and monitoring of the welding path of metal pipe parts in the above embodiment.

[0091] It should be understood that each part of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following techniques well known in the art or a combination thereof can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0092] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. Intelligent planning and monitoring system for welding paths of metal pipe parts, characterized by: include: Path planning module, which is used to generate the optimal welding path according to the geometry of metal tube parts, welding process requirements and sensor feedback information, and optimize the path according to real-time feedback; Sensor module, which monitors key parameters of the welding process in real time through visual sensors, thermal imaging sensors and force sensors; Data processing and analysis module, used to process and analyze the real-time data collected by sensors to generate welding quality assessment and defect prediction information; The welding control module controls the operation of the welding equipment according to the path planning and sensor feedback data; The quality monitoring and feedback module monitors the quality of the welding process in real time, detects welding defects and provides adjustment feedback.

2. The intelligent planning and monitoring system for welding paths of metal pipe parts according to claim 1 is characterized in that: The path planning module includes: A path generation unit generates a preliminary welding path according to the geometric shape and welding position of metal pipe parts; Path optimization unit, which optimizes the welding path based on real-time sensor data; The collision detection and avoidance unit detects and avoids possible collisions in real time by monitoring the motion path and environment of the welding robot.

3. The intelligent planning and monitoring system for welding paths of metal pipe parts according to claim 1 is characterized in that: The sensor module comprises: The visual sensor unit uses a high-definition camera or a 3D depth camera to capture images of the welding process, identify welding gaps and surface conditions, and provide visual data; Thermal imaging sensor unit, which uses infrared thermal imaging technology to monitor the temperature changes in the welding area in real time and provide feedback on the heat distribution of the welding area; The force sensor unit monitors the stress and deformation of metal parts during the welding process through piezoelectric sensors or strain gauge sensors to obtain mechanical data during the welding process.

4. The intelligent planning and monitoring system for welding paths of metal pipe parts according to claim 1 is characterized in that: The data processing and analysis module includes: The data acquisition unit collects data from each sensor in real time and performs preliminary signal processing to output effective information; The data fusion and analysis unit fuses and analyzes data from multiple sensors through the Kalman filter algorithm and machine learning model to extract key parameters; Defect prediction and evaluation unit, which predicts welding defect types and evaluates welding quality through deep learning models based on historical welding data and real-time data; The data processing and analysis module performs real-time data preprocessing and analysis through edge computing devices to reduce data transmission delays and improve processing efficiency.

5. The intelligent planning and monitoring system for welding paths of metal pipe parts according to claim 1 is characterized in that: The welding control module comprises: Welding parameter adjustment unit, which automatically adjusts welding parameters including current, speed and angle based on real-time sensor feedback; The robot control unit controls the welding robot to move precisely on a predetermined path and execute path planning; The process control and compensation unit compensates for errors in the welding process and adjusts the welding path and parameters based on real-time sensor data.

6. The intelligent planning and monitoring system for welding paths of metal pipe parts according to claim 1 is characterized in that: The quality monitoring and feedback module includes: Real-time quality monitoring unit, which monitors welding quality in real time through visual and sensor data, and detects welding defects, including cracks and pores; Welding defect detection unit, which uses deep learning models to automatically identify weld defects and generate defect reports; The quality feedback and adjustment unit automatically sends a feedback signal to the welding control module to adjust the welding parameters or path when a welding defect is detected.

7. The intelligent planning and monitoring system for welding paths of metal pipe parts according to claim 1 is characterized in that: It also includes a user interface and a control module, wherein the user interface and the control module include: Status display unit, used to display status information, progress information and quality monitoring information during welding; Parameter adjustment unit allows the operator to manually adjust welding parameters to meet special welding requirements; The alarm and prompt unit sends out an alarm signal when an abnormality occurs during the welding process and provides treatment suggestions.

8. Intelligent planning and monitoring method for welding path of metal pipe parts, characterized in that: The intelligent planning and monitoring system for welding paths of metal pipe parts according to any one of claims 1 to 7 comprises the following steps: S1. Path planning step, based on the geometric shape of metal tube parts, welding process requirements and real-time sensor feedback information, generate a preliminary welding path and optimize the path so that the welding path meets the welding quality requirements; S2. Sensor data collection step, collecting data of key parameters in the welding process in real time through visual sensors, thermal imaging sensors and force sensors, and obtaining welding environment information of metal pipe parts; S3. Data processing and analysis step: preprocessing, fusing and analyzing the data collected by the sensor, using the Kalman filter algorithm and machine learning model to process the data, evaluate welding quality and predict possible defects; S4. Welding control step, automatically adjusting welding parameters according to path planning results and sensor feedback data, controlling the welding robot to move along the optimal path, and performing welding tasks; S5. Quality monitoring and feedback step: monitor the quality of the welding process in real time, identify welding defects through image processing and deep learning models, automatically generate defect reports and pass feedback information to the welding control module to adjust welding parameters or paths.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for intelligent planning and monitoring of welding paths for metal pipe parts as described in claim 8 is implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by the processor, the method for intelligent planning and monitoring of welding paths for metal pipe parts as claimed in claim 8 is implemented.

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