Welding quality monitoring system based on real-time acquisition and dynamic analysis

Abstract

The welding quality monitoring system based on real-time acquisition and dynamic analysis comprises a data real-time acquisition module used for acquiring current data and voltage data in a welding process in real time at a millisecond-level sampling frequency by adopting a high-speed and high-precision data acquisition card; and the data dynamic analysis module is used for performing real-time accurate analysis on the collected current and voltage fluctuation signals by adopting a deep learning algorithm and big data analysis so as to quickly identify various types of abnormal information in the welding process.

Description

Technical Field

[0001] The invention relates to a welding quality monitoring system based on real-time acquisition and dynamic analysis. Background Art

[0002] Welding, as a basic and critical process, can be widely used in many fields such as construction, machinery manufacturing, automotive industry, aerospace, etc.; however, traditional welding equipment has many drawbacks, which seriously restrict the efficiency and quality improvement of welding operations. Therefore, welding robots came into being to replace traditional welding equipment to improve the efficiency and quality of welding operations.

[0003] In the process of monitoring welding quality, most people rely on manual experience to make judgments, and lack real-time, accurate data collection and analysis methods. Fluctuations in key parameters such as current and voltage are often difficult to detect in a timely manner, resulting in welding defects that cannot be discovered and corrected in the early stages. At the same time, due to the lack of a complete process data recording system, once welding quality problems occur, it is difficult to trace the various parameters in the welding process, which is not conducive to process optimization and improvement. Summary of the Invention

[0004] The embodiment of the present invention provides a welding quality monitoring system based on real-time acquisition and dynamic analysis. The system has a reasonable structural design. Based on the mutual cooperation of the real-time data acquisition module and the dynamic data analysis module, the system collects the current and voltage data in the welding process in real time at a sampling frequency of milliseconds. The system uses deep learning algorithms and big data analysis technologies to perform real-time and accurate analysis on the collected current and voltage fluctuation signals. The system can quickly identify various types of abnormal information in the welding process and automatically select and record the coordinates of the abnormal points to facilitate subsequent in-depth analysis of the causes, suspend the welding operation and issue a shutdown warning. In this way, the system can monitor the welding quality in real time and accurately, detect and handle welding abnormalities in a timely manner, avoid the expansion of defects, ensure the stability and reliability of the welding quality, improve the product qualification rate, and solve the problems existing in the prior art.

[0005] The technical solution adopted by the present invention to solve the above technical problems is: A welding quality monitoring system based on real-time acquisition and dynamic analysis, comprising: A real-time data acquisition module, which is used to collect current data and voltage data during the welding process in real time at a sampling frequency of milliseconds using a high-speed and high-precision data acquisition card; The data dynamic analysis module is used to use deep learning algorithms and big data analysis to perform real-time and accurate analysis of the collected current and voltage fluctuation signals to quickly identify various types of abnormal information during the welding process.

[0006] The monitoring method of the monitoring system comprises the following steps: S1, set the weighting coefficient and influence ratio of DC gas shielded welding; S2, set the weighting coefficient and influence ratio of pulse gas shielded welding; S3, calculate the corresponding reference frequency; S4, calculate the corresponding defect tendency rate.

[0007] The weighted coefficients of the DC gas shielded welding and the pulse gas shielded welding both include current deviation, voltage deviation, power deviation, current frequency deviation, voltage frequency deviation and power frequency deviation; The current deviation is used to determine the melting speed and penetration of the welding wire; the voltage deviation is used to control the arc length and energy distribution; the power deviation is used to comprehensively reflect the heat input; the current frequency deviation is used to determine the droplet transfer frequency and heat input distribution; the voltage frequency deviation is used to affect the arc stability; and the power frequency deviation is used to indirectly affect the periodic fluctuation of the heat input. For the weighting coefficient of DC gas shielded welding, the influence ratio of the current deviation is 40%, the influence ratio of the voltage deviation is 30%, the influence ratio of the power deviation is 15%, the influence ratio of the current frequency deviation is 8%, the influence ratio of the voltage frequency deviation is 5%, and the influence ratio of the power frequency deviation is 2%. For the weighted coefficient of pulse gas shielded welding, the influence proportion of the current deviation is 25%, the influence proportion of the voltage deviation is 20%, the influence proportion of the power deviation is 15%, the influence proportion of the current frequency deviation is 15%, the influence proportion of the voltage frequency deviation is 10%, and the influence proportion of the power frequency deviation is 15%.

[0008] Calculating the corresponding reference frequency includes the following steps: S3.1, calculate the short-circuit transition frequency using the wire feed speed and arc voltage; the calculation formula is: Short circuit transition frequency = wire feeding speed / (arc length + short circuit burn-back length) × wire diameter; S3.2, determining the current frequency and voltage frequency according to the pulse transition frequency of the welding robot; S3.3, set the current to I s , set the voltage to U s , set the frequency to P s , set the pulse transition frequency to G s , obtain the reference frequency F through the relationship coefficient r =θ(U S , I s , G S ).

[0009] Set the real-time current of the welding robot at time t during the welding process to Ia (t), real-time voltage is U a (t), real-time power is P a (t); combined with the calculated current frequency F I (t), voltage frequency F U (t) and power frequency F P (t) The defect tendency rate δ(t) is obtained as: , Among them, a, b, c, d, e, and g are weight coefficients.

[0010] The monitoring system can record various data during the welding process in real time at an interval of 100ms, including current, voltage, swing frequency, swing amplitude and welding speed. It can obtain trajectory path information of the welding task by interacting with the motion control system of the navigation wheeled mobile platform and the industrial robot arm; organize the data according to chronological order and task number, and store it in a solid-state hard drive to establish a dual indexing mechanism based on chronological order and task number.

[0011] The navigation wheeled mobile platform is a mobile platform equipped with high-precision laser navigation and visual navigation functions, and is equipped with a path planning algorithm. In actual work scenarios, the welding robot first scans the surrounding environment through a laser radar to build a map model, and then combines the image information obtained by the visual sensor to identify obstacles and target locations in the workplace in real time. According to preset task instructions and map information, the path planning algorithm automatically generates the optimal movement path and controls the navigation wheeled mobile platform to move to the designated workstation at a stable speed and precise direction, ensuring that the robot can operate efficiently and safely in complex working environments.

[0012] The load capacity, repeatability, joint angle, movement speed, and acceleration of the industrial robotic arm can all be adjusted to achieve precise motion control of the robotic arm.

[0013] The monitoring system can establish a stable communication connection with the MES system via Ethernet or wireless local area network; when the MES system issues a work task scheduling instruction, the system first parses and verifies the received instruction to ensure the accuracy and completeness of the instruction; then, according to the content of the instruction, the system controls the navigation wheeled mobile platform to move to the designated workstation, and at the same time adjusts the industrial robot arm to a specific posture and park in place; receives and parses the robot operation program from the MES system, sends the program parameters to the control system of the welding system and the industrial robot arm, starts and executes the operation program; during the operation process, the robot's working status and task progress are fed back to the MES system in real time, realizing real-time monitoring and management of the production process.

[0014] The present invention adopts the above-mentioned structure, and uses a high-speed and high-precision data acquisition card through the real-time data acquisition module to collect the current data and voltage data in the welding process in real time at a sampling frequency of milliseconds; uses a deep learning algorithm and big data analysis through the data dynamic analysis module to perform real-time and accurate analysis on the collected current and voltage fluctuation signals to quickly identify various types of abnormal information in the welding process; through the analysis and mining of a large amount of welding process data, it is possible to have an in-depth understanding of the impact of various factors in the welding process on the welding quality, thereby optimizing the welding process parameters and improving production efficiency; through a large-capacity, high-reliability solid-state hard drive as a data storage medium, it records the current, voltage, swing frequency, swing amplitude, welding speed and other key information in the welding process in real time, and through the establishment of a unique data index and association mechanism, accurately corresponds this information to the trajectory path of the welding task, which has the advantages of accuracy, efficiency, stability and practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a structural schematic diagram of the present invention.

[0016] Figure 2 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0017] In order to clearly illustrate the technical features of this solution, the present invention is described in detail below through specific implementation methods and in conjunction with the accompanying drawings.

[0018] like Figure 1-2 As shown in the figure, a welding quality monitoring system based on real-time acquisition and dynamic analysis includes: A real-time data acquisition module, which is used to collect current data and voltage data during the welding process in real time at a sampling frequency of milliseconds using a high-speed and high-precision data acquisition card; The data dynamic analysis module is used to use deep learning algorithms and big data analysis to perform real-time and accurate analysis of the collected current and voltage fluctuation signals to quickly identify various types of abnormal information during the welding process.

[0019] The monitoring method of the monitoring system comprises the following steps: S1, set the weighting coefficient and influence ratio of DC gas shielded welding; S2, set the weighting coefficient and influence ratio of pulse gas shielded welding; S3, calculate the corresponding reference frequency; S4, calculate the corresponding defect tendency rate.

[0020] The weighted coefficients of the DC gas shielded welding and the pulse gas shielded welding both include current deviation, voltage deviation, power deviation, current frequency deviation, voltage frequency deviation and power frequency deviation; The current deviation is used to determine the melting speed and penetration of the welding wire; the voltage deviation is used to control the arc length and energy distribution; the power deviation is used to comprehensively reflect the heat input; the current frequency deviation is used to determine the droplet transfer frequency and heat input distribution; the voltage frequency deviation is used to affect the arc stability; and the power frequency deviation is used to indirectly affect the periodic fluctuation of the heat input. For the weighting coefficient of DC gas shielded welding, the influence ratio of the current deviation is 40%, the influence ratio of the voltage deviation is 30%, the influence ratio of the power deviation is 15%, the influence ratio of the current frequency deviation is 8%, the influence ratio of the voltage frequency deviation is 5%, and the influence ratio of the power frequency deviation is 2%. For the weighted coefficient of pulse gas shielded welding, the influence proportion of the current deviation is 25%, the influence proportion of the voltage deviation is 20%, the influence proportion of the power deviation is 15%, the influence proportion of the current frequency deviation is 15%, the influence proportion of the voltage frequency deviation is 10%, and the influence proportion of the power frequency deviation is 15%.

[0021] Calculating the corresponding reference frequency includes the following steps: S3.1, calculate the short-circuit transition frequency using the wire feed speed and arc voltage; the calculation formula is: Short circuit transition frequency = wire feeding speed / (arc length + short circuit burn-back length) × wire diameter; S3.2, determining the current frequency and voltage frequency according to the pulse transition frequency of the welding robot; S3.3, set the current to I s , set the voltage to U s , set the frequency to P s , set the pulse transition frequency to G s , obtain the reference frequency F through the relationship coefficient r =θ(U S , I s , G S ).

[0022] Set the real-time current of the welding robot at time t during the welding process to I a (t), real-time voltage is U a (t), real-time power is P a (t); combined with the calculated current frequency F I (t), voltage frequency F U (t) and power frequency F P (t) The defect tendency rate δ(t) is obtained as: , Among them, a, b, c, d, e, and g are weight coefficients.

[0023] The monitoring system can record various data during the welding process in real time at an interval of 100ms, including current, voltage, swing frequency, swing amplitude and welding speed. It can obtain trajectory path information of the welding task by interacting with the motion control system of the navigation wheeled mobile platform and the industrial robot arm; organize the data according to chronological order and task number, and store it in a solid-state hard drive to establish a dual indexing mechanism based on chronological order and task number.

[0024] The navigation wheeled mobile platform is a mobile platform equipped with high-precision laser navigation and visual navigation functions, and is equipped with a path planning algorithm. In actual work scenarios, the welding robot first scans the surrounding environment through a laser radar to build a map model, and then combines the image information obtained by the visual sensor to identify obstacles and target locations in the workplace in real time. According to preset task instructions and map information, the path planning algorithm automatically generates the optimal movement path and controls the navigation wheeled mobile platform to move to the designated workstation at a stable speed and precise direction, ensuring that the robot can operate efficiently and safely in complex working environments.

[0025] The load capacity, repeatability, joint angle, movement speed, and acceleration of the industrial robotic arm can all be adjusted to achieve precise motion control of the robotic arm.

[0026] The monitoring system can establish a stable communication connection with the MES system via Ethernet or wireless local area network; when the MES system issues a work task scheduling instruction, the system first parses and verifies the received instruction to ensure the accuracy and completeness of the instruction; then, according to the content of the instruction, the system controls the navigation wheeled mobile platform to move to the designated workstation, and at the same time adjusts the industrial robot arm to a specific posture and park in place; receives and parses the robot operation program from the MES system, sends the program parameters to the control system of the welding system and the industrial robot arm, starts and executes the operation program; during the operation process, the robot's working status and task progress are fed back to the MES system in real time, realizing real-time monitoring and management of the production process.

[0027] The working principle of the welding quality monitoring system based on real-time acquisition and dynamic analysis in the embodiment of the present invention is: based on the mutual cooperation of the real-time data acquisition module and the dynamic data analysis module, the current and voltage data in the welding process are collected in real time at a sampling frequency of milliseconds, and the deep learning algorithm and big data analysis technology are used to perform real-time and accurate analysis on the collected current and voltage fluctuation signals. It can quickly identify various types of abnormal information in the welding process, and automatically select and record the coordinates of the abnormal points to facilitate subsequent in-depth analysis of the causes, suspend the welding operation and issue a shutdown warning, so as to monitor the welding quality in real time and accurately, promptly discover and handle welding abnormalities, avoid the expansion of defects, ensure the stability and reliability of welding quality, and improve the product qualification rate.

[0028] In the overall solution, the monitoring system includes: a real-time data acquisition module, which uses a high-speed and high-precision data acquisition card to collect current data and voltage data in the welding process in real time at a sampling frequency of milliseconds; a data dynamic analysis module, which uses deep learning algorithms and big data analysis to perform real-time and accurate analysis of the collected current and voltage fluctuation signals to quickly identify various types of abnormal information in the welding process; due to the lack of real-time and accurate data acquisition and analysis methods in actual welding operations, fluctuations in key parameters such as current and voltage are often difficult to detect in a timely manner, resulting in welding defects that cannot be discovered and corrected in the early stages. Therefore, the monitoring system of this application can accurately solve the above problems.

[0029] Correspondingly, the monitoring method of the monitoring system includes the following steps: setting the weighting coefficient and influence ratio of DC gas shielded welding; setting the weighting coefficient and influence ratio of pulse gas shielded welding; calculating the corresponding reference frequency; and calculating the corresponding defect tendency rate.

[0030] Specifically, the weighted coefficients of DC gas shielded welding and pulse gas shielded welding both include current deviation, voltage deviation, power deviation, current frequency deviation, voltage frequency deviation, and power frequency deviation; The current deviation is used to determine the melting speed and penetration of the welding wire; the voltage deviation is used to control the arc length and energy distribution; the power deviation is used to comprehensively reflect the heat input; the current frequency deviation is used to determine the droplet transfer frequency and heat input distribution; the voltage frequency deviation is used to affect the arc stability; the power frequency deviation is used to indirectly affect the periodic fluctuation of heat input; for the weighting coefficient of DC gas shielded welding, the influence proportion of the current deviation is 40%, the influence proportion of the voltage deviation is 30%, the influence proportion of the power deviation is 15%, the influence proportion of the current frequency deviation is 8%, the influence proportion of the voltage frequency deviation is 5%, and the influence proportion of the power frequency deviation is 2%; for the weighting coefficient of pulse gas shielded welding, the influence proportion of the current deviation is 25%, the influence proportion of the voltage deviation is 20%, the influence proportion of the power deviation is 15%, the influence proportion of the current frequency deviation is 15%, the influence proportion of the voltage frequency deviation is 10%, and the influence proportion of the power frequency deviation is 15%.

[0031] The current directly determines the melting speed and penetration of the welding wire. Too high a current will lead to overheating of the molten pool, increased spatter, and burn-through. Too low a current will cause incomplete fusion, insufficient penetration, and porosity. In pulse welding, current deviation will also destroy the stability of the droplet transfer, resulting in uneven welds or cracks.

[0032] Voltage controls the arc length and energy distribution; too high a voltage will make the arc too long, resulting in undercutting and a wide weld; too low a voltage will cause arc instability, a short circuit between the wire and the workpiece, and spatter and porosity; the matching of voltage and current is crucial to the balance of heat input, and the superposition of deviations between the two will significantly increase the risk of defects.

[0033] Power, the product of current and voltage, comprehensively reflects heat input. Power deviations can be caused by deviations in either current or voltage alone. These deviations manifest as temperature fluctuations in the melt pool, leading to overheating or uneven cooling, which can cause cracks, deformation, or structural defects.

[0034] Generally speaking, since the dominant factors of specific defect types may be different, they need to be adjusted in combination with process conditions. In actual applications, parameter sensitivity needs to be verified through process evaluation and weight distribution needs to be dynamically optimized.

[0035] Preferably, calculating the corresponding reference frequency includes the following steps: calculating the short-circuit transition frequency by wire feeding speed and arc voltage; the calculation formula is: short-circuit transition frequency = wire feeding speed / (arc length + short-circuit burn-back length) × wire diameter; determining the current frequency and voltage frequency according to the pulse transition frequency of the welding robot; setting the current to I s , set the voltage to U s , set the frequency to P s , set the pulse transition frequency to G s, obtain the reference frequency F through the relationship coefficient r =θ(U S , I s , G S ).

[0036] The pulse transition frequency is set by the welding robot, and the short circuit transition frequency is set by measuring the number of short circuits or referring to the default parameters of the welding machine.

[0037] For the defect tendency rate, the real-time current of the welding robot at time t during the welding process is set to I a (t), real-time voltage is U a (t), real-time power is P a (t); combined with the calculated current frequency F I (t), voltage frequency F U (t) and power frequency F P (t) The defect tendency rate δ(t) is obtained as: , Among them, a, b, c, d, e, and g are weight coefficients.

[0038] Relevant data is calculated based on the multi-type electrical parameters collected in real time, so as to accurately identify the corresponding fluctuation signals. Combined with the preset control strategy, the coordinates of abnormal points can be automatically selected and recorded, and shutdown warning measures can be directly taken to ensure the stability and reliability of welding quality.

[0039] Furthermore, the monitoring system can record various data during the welding process in real time at an interval of 100ms, including current, voltage, swing frequency, swing amplitude and welding speed. It can obtain trajectory path information of the welding task by interacting with the motion control system of the navigation wheeled mobile platform and the industrial robot arm; organize the data according to chronological order and task number, and store them in the solid-state hard drive to establish a dual indexing mechanism based on chronological order and task number to facilitate data query and traceability.

[0040] To improve the monitoring and control efficiency of the system, the monitoring system can establish a stable communication connection with the MES system via Ethernet or wireless local area network. When the MES system issues a work task scheduling instruction, the system first parses and verifies the received instruction to ensure the accuracy and completeness of the instruction. Then, according to the content of the instruction, the system controls the navigation wheeled mobile platform to move to the designated workstation, and adjusts the industrial robot arm to a specific posture and park in place. The system receives and parses the robot operation program from the MES system, sends the program parameters to the control system of the welding system and the industrial robot arm, starts and executes the operation program. During the operation, the system provides real-time feedback to the MES system on the robot's working status and task progress, realizing real-time monitoring and management of the production process.

[0041] It should be noted that the monitoring system of the present application can collect data at a sampling frequency of more than 2MkHz and convert it into digital signals; use deep learning algorithms (such as convolutional neural networks) to extract features and recognize patterns of digital signals; and judge whether there are any abnormalities in the welding process by comparing them with the data model under normal welding conditions.

[0042] In this application, the welding quality can be monitored in real time and accurately, and welding anomalies can be discovered and handled in a timely manner, so that the detection rate of welding anomalies reaches more than 95%. Measures can be taken in time at the early stage of welding defects to avoid the expansion of defects and improve the product qualification rate.

[0043] In summary, the welding quality monitoring system based on real-time acquisition and dynamic analysis in the embodiment of the present invention is based on the mutual cooperation of the real-time data acquisition module and the dynamic data analysis module, and collects the current and voltage data in the welding process in real time at a sampling frequency of milliseconds. It uses deep learning algorithms and big data analysis technology to perform real-time and accurate analysis on the collected current and voltage fluctuation signals, and can quickly identify various types of abnormal information in the welding process. At the same time, it automatically selects and records the coordinates of the abnormal points, which is convenient for subsequent in-depth analysis of the causes, suspends the welding operation and issues a shutdown warning, so as to monitor the welding quality in real time and accurately, promptly discover and handle welding abnormalities, avoid the expansion of defects, ensure the stability and reliability of welding quality, and improve the product qualification rate.

[0044] The above specific implementation manner cannot be used as a limitation on the protection scope of the present invention. For those skilled in the art, any replacement, improvement or transformation made to the implementation manner of the present invention falls within the protection scope of the present invention.

[0045] Any matters not described in detail in the present invention are well-known technologies to those skilled in the art.

Claims

1. The welding quality monitoring system based on real-time acquisition and dynamic analysis is characterized by: The monitoring system comprises: A real-time data acquisition module, which is used to use a high-speed and high-precision data acquisition card to collect current data and voltage data in the welding process in real time at a sampling frequency of milliseconds; The data dynamic analysis module is used to use deep learning algorithms and big data analysis to perform real-time and accurate analysis of the collected current and voltage fluctuation signals to quickly identify various types of abnormal information during the welding process.

2. The welding quality monitoring system based on real-time acquisition and dynamic analysis according to claim 1 is characterized in that: The monitoring method of the monitoring system comprises the following steps: S1, set the weighting coefficient and influence ratio of DC gas shielded welding; S2, set the weighting coefficient and influence ratio of pulse gas shielded welding; S3, calculate the corresponding reference frequency; S4, calculate the corresponding defect tendency rate.

3. The welding quality monitoring system based on real-time acquisition and dynamic analysis according to claim 2 is characterized in that: The weighted coefficients of the DC gas shielded welding and the pulse gas shielded welding both include current deviation, voltage deviation, power deviation, current frequency deviation, voltage frequency deviation and power frequency deviation; The current deviation is used to determine the melting speed and penetration of the welding wire; the voltage deviation is used to control the arc length and energy distribution; the power deviation is used to comprehensively reflect the heat input; the current frequency deviation is used to determine the droplet transfer frequency and heat input distribution; the voltage frequency deviation is used to affect the arc stability; and the power frequency deviation is used to indirectly affect the periodic fluctuation of the heat input. For the weighting coefficient of DC gas shielded welding, the influence ratio of the current deviation is 40%, the influence ratio of the voltage deviation is 30%, the influence ratio of the power deviation is 15%, the influence ratio of the current frequency deviation is 8%, the influence ratio of the voltage frequency deviation is 5%, and the influence ratio of the power frequency deviation is 2%. For the weighted coefficient of pulse gas shielded welding, the influence proportion of the current deviation is 25%, the influence proportion of the voltage deviation is 20%, the influence proportion of the power deviation is 15%, the influence proportion of the current frequency deviation is 15%, the influence proportion of the voltage frequency deviation is 10%, and the influence proportion of the power frequency deviation is 15%.

4. The welding quality monitoring system based on real-time acquisition and dynamic analysis according to claim 2 is characterized in that: Calculating the corresponding reference frequency includes the following steps: S3.1, calculate the short-circuit transition frequency using the wire feed speed and arc voltage; the calculation formula is: Short circuit transition frequency = wire feeding speed / (arc length + short circuit burn-back length) × wire diameter; S3.2, determining the current frequency and voltage frequency according to the pulse transition frequency of the welding robot; S3.3, set the current to I s , set the voltage to U s , set the frequency to P s , set the pulse transition frequency to G s , obtain the reference frequency F through the relationship coefficient r =θ(U S , I s , G S ).

5. The welding quality monitoring system based on real-time acquisition and dynamic analysis according to claim 4 is characterized in that: Set the real-time current of the welding robot at time t during the welding process to I a (t), real-time voltage is U a (t), real-time power is P a (t); combined with the calculated current frequency F I (t), voltage frequency F U (t) and power frequency F P (t) The defect tendency rate δ(t) is obtained as: ， Among them, a, b, c, d, e, and g are weight coefficients.

6. The welding quality monitoring system based on real-time acquisition and dynamic analysis according to claim 1 is characterized in that: The monitoring system can record various data during the welding process in real time at an interval of 100ms, including current, voltage, swing frequency, swing amplitude and welding speed. It can obtain trajectory path information of the welding task by interacting with the motion control system of the navigation wheeled mobile platform and the industrial robot arm; organize the data according to chronological order and task number, and store it in a solid-state hard drive to establish a dual indexing mechanism based on chronological order and task number.

7. The welding quality monitoring system based on real-time acquisition and dynamic analysis according to claim 6 is characterized in that: The navigation wheeled mobile platform is a mobile platform equipped with high-precision laser navigation and visual navigation functions, and is equipped with a path planning algorithm. In actual work scenarios, the welding robot first scans the surrounding environment through a laser radar to build a map model, and then combines the image information obtained by the visual sensor to identify obstacles and target locations in the workplace in real time. According to preset task instructions and map information, the path planning algorithm automatically generates the optimal movement path and controls the navigation wheeled mobile platform to move to the designated workstation at a stable speed and precise direction, ensuring that the robot can operate efficiently and safely in complex working environments.

8. The welding quality monitoring system based on real-time acquisition and dynamic analysis according to claim 6 is characterized in that: The load capacity, repeatability, joint angle, movement speed, and acceleration of the industrial robotic arm can all be adjusted to achieve precise motion control of the robotic arm.

9. The welding quality monitoring system based on real-time data acquisition and dynamic analysis according to claim 1, characterized in that: The monitoring system can establish a stable communication connection with the MES system via Ethernet or wireless local area network; when the MES system issues a work task scheduling instruction, the system first parses and verifies the received instruction to ensure the accuracy and completeness of the instruction; then, according to the content of the instruction, the system controls the navigation wheeled mobile platform to move to the designated workstation, and at the same time adjusts the industrial robot arm to a specific posture and park in place; receives and parses the robot operation program from the MES system, sends the program parameters to the control system of the welding system and the industrial robot arm, starts and executes the operation program; during the operation process, the robot's working status and task progress are fed back to the MES system in real time, realizing real-time monitoring and management of the production process.

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