Performance parameter acquisition method and system of industrial robot

Abstract

The invention relates to the technical field of artificial intelligence, in particular to a method and a system for acquiring performance parameters of an industrial robot. The method comprises the following steps that industrial robot design data are obtained, and three-dimensional robot model construction is carried out, so that a three-dimensional robot model is obtained; performing sensing monitoring on the three-dimensional robot model to obtain robot sensing data; carrying out repeated positioning precision analysis based on the robot sensing data so as to obtain repeated positioning precision data; carrying out load capacity analysis according to the three-dimensional robot model so as to obtain robot load capacity data; according to the robot load capacity data, wear part identification is conducted on the three-dimensional robot model, and thus robot wear part data are obtained; and life prediction is performed according to the robot wear part data to obtain robot life data. The motion precision of the industrial robot and the performance of the servo system are optimized based on artificial intelligence.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a method and system for acquiring performance parameters of an industrial robot. Background Art

[0002] Traditional methods often rely on manual inspection and manual operation, lacking real-time and high efficiency, resulting in delays and inaccuracies in the data collection process. The sensor accuracy of traditional systems is limited and cannot meet the needs of high-precision performance evaluation. Especially in complex environments and under high loads, sensors are easily affected by factors such as noise and temperature changes, resulting in data errors. Traditional methods mostly rely on single measurement data, ignoring the multi-dimensional fusion and intelligent analysis of data, and cannot fully utilize machine learning and artificial intelligence technologies for real-time optimization and prediction. Traditional systems have poor scalability and are difficult to cope with increasingly complex production environments and changing operational requirements. They are also not easy to integrate with other industrial systems, limiting their application in smart manufacturing and IoT environments. Traditional methods lack sufficient intelligent support in fault diagnosis and predictive maintenance, and often can only rely on regular maintenance and manual intervention, but cannot achieve automated fault warning and performance optimization. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a method and system for obtaining performance parameters of an industrial robot to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for obtaining performance parameters of an industrial robot comprises the following steps:

[0005] Step S1: Acquire industrial robot design data and construct a three-dimensional robot model to obtain a three-dimensional robot model;

[0006] Step S2: performing sensor monitoring on the three-dimensional robot model to obtain robot sensor data; performing repeated positioning accuracy analysis based on the robot sensor data to obtain repeated positioning accuracy data;

[0007] Step S3: performing load capacity analysis on the three-dimensional robot model to obtain robot load capacity data; performing wear part identification on the three-dimensional robot model based on the robot load capacity data to obtain robot wear part data;

[0008] Step S4: performing life prediction based on the robot wear part data, thereby obtaining the robot life data; performing motion performance testing based on the robot life data, thereby obtaining the robot motion performance data;

[0009] Step S5: performing accuracy statistics based on the repeated positioning accuracy data to obtain low repeated positioning accuracy data; adjusting the servo system performance parameters of the robot motion performance data based on the low repeated positioning accuracy data to obtain the servo system performance parameters.

[0010] The present invention can realize comprehensive evaluation and optimization of robot performance by constructing a three-dimensional robot model and combining sensor monitoring data. Through the analysis of robot sensor data, especially the evaluation of repeated positioning accuracy, it is possible to effectively identify and compensate for accuracy deviation, improve the operating accuracy and stability of the robot in a complex environment, and then optimize the robot's motion performance. Based on the robot load capacity analysis, high-load areas can be identified and wear parts can be accurately located, thereby providing data support for subsequent life prediction and effectively reducing the impact of excessive wear on robot performance. By combining robot life data for motion performance testing, real-time operating status feedback can be provided for the robot, providing a basis for subsequent performance adjustment, and avoiding performance degradation caused by hardware aging. Through precision statistics, combined with low repeated positioning accuracy data, the robot servo system can be accurately adjusted, thereby improving the system's dynamic response capability and precision control, and further enhancing the overall performance. This method avoids the inefficiency of traditional reliance on manual detection, reduces data errors caused by sensor accuracy limitations, and combines multi-dimensional data analysis, machine learning and artificial intelligence technology to effectively improve real-time and intelligent levels, and can achieve dynamic optimization and accurate prediction. The intelligent and automated features of this system have greatly improved the application efficiency and reliability of robots in complex production environments. At the same time, it has good scalability and can cope with the changing needs of future smart manufacturing and Internet of Things environments. It provides strong support for fault diagnosis and predictive maintenance and promotes the innovation and progress of industrial robot technology.

[0011] Preferably, step S1 is specifically:

[0012] Step S11: Acquire industrial robot design data, and perform mechanical joint type feature extraction to obtain mechanical joint type data;

[0013] Step S12: performing joint degree of freedom identification according to the mechanical joint type data, thereby obtaining joint degree of freedom data;

[0014] Step S13: performing connection relationship analysis on the mechanical joint type data according to the joint degree of freedom data, thereby obtaining mechanical joint connection relationship data;

[0015] Step S14: constructing a three-dimensional robot model according to the mechanical joint connection relationship data and the mechanical type data, thereby obtaining a three-dimensional robot model.

[0016] The present invention realizes the identification of the degrees of freedom of each joint of the robot by systematically extracting and analyzing the data of mechanical joint types, and through the in-depth analysis of the data of joint degrees of freedom, can accurately identify and optimize the connection relationship of the robot joints, and finally build a three-dimensional robot model. Compared with traditional manual operation, this process can greatly improve the real-time and accuracy of data acquisition, and avoid the manual errors and operation delays in traditional methods. Through accurate mechanical joint type feature extraction and degree of freedom identification, a more accurate basis can be provided for the performance evaluation of the robot in different working environments, especially in complex and high-load environments, which can effectively reduce the performance problems caused by joint mismatch or insufficient degrees of freedom. In addition, through a comprehensive analysis of the connection relationship of mechanical joints, strong support is provided for the subsequent optimization design and efficient assembly of robots. Compared with the limitations of traditional methods that rely on single measurement data, this method fully considers the fusion and intelligent analysis of multi-dimensional data, and uses modern computer technology and artificial intelligence algorithms to achieve a high degree of automation and intelligence in the process of robot design, analysis and optimization. The application of this method enables robots to operate more efficiently in a changing production environment, and has stronger adaptability and scalability, providing real-time support for robot fault diagnosis, predictive maintenance and performance optimization, and promoting the intelligent manufacturing process of robot technology.

[0017] Preferably, step S2 specifically comprises:

[0018] Step S21: performing sensor monitoring on the three-dimensional robot model to obtain robot sensor data;

[0019] Step S22: performing repeated grasping simulation according to the three-dimensional robot model to obtain repeated grasping data;

[0020] Step S23: performing error capture calculation on the repeatedly captured data, thereby obtaining error capture data;

[0021] Step S24: performing environmental error interference analysis on the three-dimensional robot model according to the error capture data, thereby obtaining environmental error interference data;

[0022] Step S25: performing environmental interference optimization on the three-dimensional robot model according to the environmental error interference data, thereby obtaining a three-dimensional robot optimization model;

[0023] Step S26: performing repeated positioning and grasping simulation according to the three-dimensional robot optimization model, thereby obtaining repeated positioning and grasping simulation data;

[0024] Step S27: Calculate the accuracy of the repeated positioning grabbing simulation data to obtain repeated positioning accuracy data.

[0025] The present invention can collect the sensor data of the robot in actual operation in real time by performing comprehensive sensor monitoring and repeated grasping simulation on the three-dimensional robot model, and analyze and optimize the robot grasping accuracy by simulating the grasping process. By performing error calculation and environmental error interference analysis on the grasping data, the influence of environmental factors on the robot performance can be accurately identified, and environmental interference optimization can be performed, thereby improving the stability and accuracy of the robot in a complex environment. Compared with the traditional method, the present method can not only accurately evaluate the grasping accuracy of the robot in a simulated environment, but also perform intelligent optimization for environmental interference, reduce human intervention and operation delay, and avoid data errors caused by insufficient sensor accuracy or changes in the external environment. In this way, the present method can monitor the performance of the robot in real time and dynamically adjust its operation mode to ensure the accuracy and repeatability of the robot. In a changing production environment, the present method makes full use of multi-dimensional data fusion and intelligent analysis, combined with modern machine learning and artificial intelligence technology, to provide strong technical support for fault diagnosis, predictive maintenance and performance optimization of the robot. This intelligent and automated evaluation method provides industrial robots with more efficient and accurate operation capabilities, enabling them to achieve higher production efficiency and longer service life in a constantly changing working environment.

[0026] Preferably, step S24 is specifically as follows:

[0027] Step S241: performing time statistics according to the error capture data, thereby obtaining error capture time data;

[0028] Step S242: performing temperature statistics on the three-dimensional robot model according to the error grasping time data, thereby obtaining robot temperature data;

[0029] Step S243: performing semiconductor component thermal breakdown analysis on the three-dimensional robot model based on the robot temperature data, thereby obtaining semiconductor component thermal breakdown data;

[0030] Step S244: performing solder joint failure analysis based on the semiconductor element thermal breakdown data, thereby obtaining solder joint failure data;

[0031] Step S245: Perform environmental error interference integration according to the semiconductor element thermal breakdown data and the solder joint failure data to obtain environmental error interference data.

[0032] The present invention can comprehensively monitor and evaluate the environmental interference and failure risks that occur during the operation of industrial robots through multi-level data processing and analysis. By performing time statistics on error capture data, the distribution and change trend of errors during the capture process can be accurately evaluated, thereby optimizing the robot operation process and ensuring its accuracy and stability. Further, through statistical analysis of the robot temperature, the potential risks brought by temperature changes can be identified in a timely manner, especially for the thermal breakdown problem of semiconductor components. Through the analysis of the thermal breakdown data of semiconductor components, the health status of the electronic components inside the robot can be predicted to avoid failures such as solder joint failure caused by overheating, and ensure the reliability of the robot under long-term high-load work. After identifying and analyzing the solder joint failure, the environmental error is further integrated and optimized, which can accurately identify and process the interference of external environmental factors on the robot, ensuring the efficient operation of the robot in different working environments. Compared with traditional methods, this multi-level and multi-dimensional data integration and intelligent analysis not only improves the accuracy of fault warning, but also avoids human errors and reduces data delays through real-time optimization, so that the system can operate more stably in complex and changeable industrial environments. This method can effectively improve the long-term operating efficiency, accuracy and overall performance of the robot, while reducing human intervention, improving the level of automation, and providing strong technical support for smart manufacturing and Internet of Things integration.

[0033] Preferably, step S243 is specifically as follows:

[0034] Performing temperature mapping on the three-dimensional robot model based on the robot temperature data, thereby obtaining a three-dimensional robot temperature model;

[0035] Perform semiconductor component location recognition on the three-dimensional robot temperature model to obtain a semiconductor component temperature model;

[0036] Obtain semiconductor thermal breakdown temperature data and semiconductor thermal conductivity;

[0037] Inputting the semiconductor thermal conductivity into the semiconductor element temperature model and performing thermal accumulation effect calculation to obtain thermal accumulation effect data;

[0038] Perform instantaneous temperature detection on the thermal accumulation effect data according to the semiconductor thermal breakdown temperature data, so as to obtain the semiconductor instantaneous over-temperature data;

[0039] Based on the semiconductor instantaneous overtemperature data, the instantaneous thermal breakdown risk assessment is performed to obtain the semiconductor component thermal breakdown data.

[0040] The present invention can comprehensively monitor the temperature distribution of the robot through multi-dimensional mapping and analysis of the robot temperature data, and deeply identify the key parts that affect the performance of the robot, especially the temperature changes of semiconductor components. By generating a three-dimensional robot temperature model, the thermal state of the robot can be understood in real time, and the temperature abnormality area can be identified, so as to effectively prevent overheating and other problems. Further analyzing the temperature of the semiconductor component, through the input of the thermal conductivity coefficient and the calculation of the heat accumulation effect, the impact of the temperature change of the semiconductor component on the overall system can be accurately evaluated to avoid the risk of failure caused by temperature accumulation. In addition, by combining the thermal breakdown temperature data of the semiconductor component for instantaneous temperature detection, any overtemperature situation can be identified in real time during the operation of the robot, thereby avoiding equipment damage. Through the risk assessment of instantaneous overtemperature data, predictive maintenance of the semiconductor components of the robot can be achieved, and adjustments can be made in time to avoid thermal breakdown damage in long-term operation, greatly improving the reliability of the system. Compared with traditional methods, this data model-based analysis method can not only provide more accurate real-time monitoring, but also predict potential fault risks through intelligent algorithms, reduce manual intervention, improve the automation level of fault diagnosis, greatly enhance the robot's ability to continue working under high load and complex environments, and improve the overall performance of the intelligent manufacturing system.

[0041] Preferably, step S244 is specifically as follows:

[0042] Perform temperature fluctuation statistics based on the thermal breakdown data of semiconductor components to obtain thermal breakdown temperature fluctuation data;

[0043] Perform solder joint thermal cycle stress analysis based on thermal breakdown temperature fluctuation data to obtain solder joint thermal cycle stress data;

[0044] Solder point fatigue identification is performed based on solder point thermal cycle stress data to obtain solder point fatigue data;

[0045] Fatigue solder joint images are collected based on solder joint fatigue data, thereby obtaining fatigue solder joint images;

[0046] Performing weld edge detection on the fatigue weld image to obtain weld edge data, and marking weld edge coordinates to obtain weld edge coordinate data;

[0047] Performing polar coordinate system spatial mapping on the weld point edge coordinate data, thereby obtaining the weld point polar coordinate system spatial data;

[0048] Crack line identification is performed based on the spatial data of the polar coordinate system of the welding point, thereby obtaining crack line data;

[0049] The crack area of ​​the fatigue solder joint image is marked according to the crack line data, so as to obtain the crack area data;

[0050] The weld failure analysis is performed based on the crack area data to obtain the weld failure data.

[0051] The present invention can effectively evaluate and monitor the thermal stability of semiconductor components under different working environments by statistically analyzing the temperature fluctuations of semiconductor component thermal breakdown data, and timely discover potential problems that lead to thermal breakdown. These fluctuation data are used for solder joint thermal cycle stress analysis, which helps to reveal the stress changes that solder joints bear in an environment of alternating high and low temperatures, and then predict the damage of solder joints. Based on these data, solder joint fatigue identification can deeply analyze whether the solder joint is in a fatigue state, providing a reliable basis for early detection of solder joint failure. Through fatigue solder joint image acquisition, the physical changes of the solder joint during the fatigue process can be intuitively displayed, thereby assisting in determining whether the solder joint has potential cracks or other damage problems. Through solder joint edge detection and edge coordinate marking, not only more accurate solder joint morphological data is provided, but also necessary spatial data support is provided for subsequent crack analysis, ensuring the accuracy of the analysis. Polar coordinate system spatial mapping provides a standardized coordinate system for crack identification, making the identification of crack lines more efficient and accurate. Through further analysis of crack line data, the specific location and morphology of the crack can be identified, providing a more accurate basis for crack area identification, thereby timely detecting the damage process of the solder joint. Finally, the solder joint detachment failure analysis can detect the potential failure risk of solder joints in advance by comprehensively evaluating the crack area data, effectively preventing system failures caused by solder joint detachment. This series of steps significantly improves the efficiency and accuracy of traditional manual detection methods through precise image recognition, data analysis and modeling, and can provide real-time and comprehensive fault warnings in complex environments, effectively avoiding delayed detection and potential operational risks, and greatly improving the reliability and safety of the system.

[0052] Preferably, step S3 specifically comprises:

[0053] Step S31: performing dynamic load simulation on the three-dimensional robot model to obtain dynamic load data;

[0054] Step S32: meshing the three-dimensional robot model according to the dynamic load data, thereby obtaining a three-dimensional robot meshing model;

[0055] Step S33: performing grid stress node calculation on the three-dimensional robot grid division model to obtain grid stress node data;

[0056] Step S34: drawing a stress distribution diagram based on the grid stress node data, thereby obtaining a stress distribution diagram;

[0057] Step S35: identifying high stress areas according to the stress distribution diagram, thereby obtaining high stress area data;

[0058] Step S36: performing robot load structure deformation analysis on the high stress area data, thereby obtaining robot load structure deformation data;

[0059] Step S37: evaluating the load capacity according to the robot load structure deformation data, thereby obtaining the robot load capacity data;

[0060] Step S38: Identify wear parts of the three-dimensional robot model according to the robot load capacity data, so as to obtain robot wear part data.

[0061] The present invention can accurately obtain various load conditions encountered by the robot in the actual working process by performing dynamic load simulation on the three-dimensional robot model, thereby providing accurate basic data for subsequent analysis. The grid division model can subdivide the robot structure into multiple small units to ensure that the analysis is more refined and comprehensive. Through grid stress node calculation, stress distribution data can be obtained at each small unit to help identify the stress state of each part of the robot, and further draw a stress distribution map to provide a visual basis for the identification of high stress areas. The identification of high stress areas helps to discover the potential weak links of the robot and provide data support for timely design improvements. Through the analysis of the load structure deformation of high stress areas, the deformation trend of these areas under high loads can be understood, thereby revealing potential damage or fatigue problems. Load capacity assessment helps to fully understand the load-bearing capacity of the robot under different loads, ensuring that it can maintain stable performance in actual work. Finally, by identifying the wear parts of the robot, it can be predicted which parts will wear out during long-term use, thereby providing a scientific basis for subsequent maintenance and maintenance. This series of analyses combines multi-dimensional data and can provide comprehensive support for robot performance evaluation, optimized design, fault prediction and intelligent maintenance, making up for the defects of untimely and inaccurate manual inspection in traditional methods and improving the reliability, accuracy and efficiency of the robot system.

[0062] Preferably, step S36 is specifically as follows:

[0063] Step S361: Obtaining robot material yield standard data;

[0064] Step S362: performing a preliminary deformation determination on the high stress area data according to the robot material yield standard data, thereby obtaining the robot load deformation data;

[0065] Step S363: identifying the displacement direction of the robot load deformation data, thereby obtaining displacement direction data;

[0066] Step S364: calculating the deformation amount of the displacement direction data, thereby obtaining deformation amount data;

[0067] Step S365: determining the robot load structure based on the dynamic load data, thereby obtaining the robot load structure;

[0068] Step S365: determining the structural deformation of the robot load structure according to the deformation data, thereby obtaining the robot load structure deformation data.

[0069] By obtaining the yield standard data of the robot material, the present invention can ensure that the strength of the selected material meets the requirements and avoid deformation or failure caused by insufficient material performance. On this basis, a preliminary judgment of deformation in the high stress area can be made, and the area where the robot will encounter severe deformation can be quickly identified, providing an accurate basis for subsequent analysis. By identifying the displacement direction of the load deformation data, the specific direction of deformation in these areas can be further clarified, which is helpful to analyze the specific situation of the structural force and ensure that unreasonable load distribution can be effectively avoided during design optimization. The deformation calculation can quantify the degree of deformation and provide data support for evaluating load capacity and optimizing design. Load structure judgment based on dynamic load data ensures that the robot can withstand the design load and effectively distribute stress to avoid failure caused by overload. Finally, by further analyzing the deformation data and determining the structural deformation in combination with the robot load structure, the deformation state of the robot under high load can be understood in detail, thereby providing a basis for performance optimization and preventive maintenance. The implementation of this series of steps effectively avoids the problems of manual detection delay, insufficient accuracy and lack of real-time intelligent optimization in traditional methods, improves the reliability, accuracy and efficiency of the system, and ensures the safe operation of the robot in a complex production environment.

[0070] Preferably, step S38 is specifically as follows:

[0071] Step S381: drawing a load distribution diagram according to the robot load capacity data, thereby obtaining a load distribution diagram;

[0072] Step S382: Identify uneven parts of the load distribution diagram, so as to obtain data of uneven parts of the robot load;

[0073] Step S383: performing long-term load location identification on the robot load uneven location data, thereby obtaining long-term load location data;

[0074] Step S384: performing joint periodic impact position analysis on the long-term load position data, thereby obtaining joint periodic impact position data;

[0075] Step S385: Calculate the wear based on the periodic impact site data of the joint, so as to obtain the wear site data of the robot.

[0076] The present invention can intuitively understand the load distribution of the robot by drawing a load distribution diagram according to the robot load capacity data, and help identify the area where there is uneven load. This step provides data support for subsequent optimization design, helps to ensure that the load capacity of the robot is fully utilized, and avoids overload or fatigue problems in certain areas. Identifying uneven parts of the load distribution diagram can accurately locate the potential high-risk areas of the robot, provide a basis for timely maintenance measures, and avoid equipment failures caused by local overload. Further identifying long-term load parts can find those areas that continue to bear high loads during long-term work, which helps to evaluate the long-term use reliability of the robot and find fault points in advance. By analyzing the periodic impact of the joints in the long-term load parts, the parts that are subjected to repetitive impacts can be accurately identified, providing a basis for improving mechanical design and reducing wear. Finally, wear calculation is performed based on these periodic impact parts, and the wear of each component of the robot can be quantified, providing a scientific basis for maintenance and replacement, and avoiding failure shutdowns caused by excessive wear. This method improves the real-time, intelligent and accurate performance of the traditional system, can effectively prevent problems such as overload and fatigue, reduce manual intervention, and ensure the efficient operation and safety of the robot in a complex environment.

[0077] Preferably, the present specification also provides a performance parameter acquisition system for an industrial robot, which is used to execute the performance parameter acquisition method for an industrial robot as described above, and the performance parameter acquisition system for an industrial robot includes:

[0078] A model building module is used to obtain industrial robot design data and build a three-dimensional robot model to obtain a three-dimensional robot model;

[0079] The repeatable positioning accuracy analysis module is used to perform sensor monitoring on the three-dimensional robot model to obtain robot sensor data; perform repeatable positioning accuracy analysis based on the robot sensor data to obtain repeatable positioning accuracy data;

[0080] The wear part identification module is used to perform load capacity analysis based on the three-dimensional robot model to obtain the robot load capacity data; and to identify the wear parts of the three-dimensional robot model based on the robot load capacity data to obtain the robot wear part data;

[0081] The motion performance test module is used to predict the life of the robot based on the wear part data of the robot, so as to obtain the robot life data; and to perform motion performance test based on the robot life data, so as to obtain the robot motion performance data;

[0082] The parameter adjustment module is used to perform accuracy statistics based on the repeated positioning accuracy data to obtain low repeated positioning accuracy data; and to adjust the servo system performance parameters of the robot motion performance data based on the low repeated positioning accuracy data to obtain the servo system performance parameters.

[0083] The performance parameter acquisition system of the industrial robot of the present invention can implement the performance parameter acquisition method of any industrial robot of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the performance parameter acquisition method of the industrial robot. The internal modules of the system cooperate with each other to optimize the motion accuracy of the industrial robot and the performance of the servo system. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0085] Figure 1 A schematic diagram of the steps of the method for obtaining performance parameters of an industrial robot according to the present invention;

[0086] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0087] Figure 3 Detailed step flow diagram of step S2 in the present invention;

[0088] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0089] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0090] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0091] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0092] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for obtaining performance parameters of an industrial robot, the method comprising the following steps:

[0093] Step S1: Acquire industrial robot design data and construct a three-dimensional robot model to obtain a three-dimensional robot model;

[0094] In the present embodiment, various design data including mechanical structure, joint type, range of motion, load capacity, etc. are extracted from the robot design file. These data are interpreted in detail by industrial robot design software, such as CAD (computer-aided design) system or robot-specific design software. Use data acquisition tools to input joint geometry, robot arm length, connecting rod angle, and joint degree of freedom into the system to ensure that the data is accurate. Then, based on these design data, a three-dimensional modeling software (such as SolidWorks or Autodesk Inventor) is used to build a robot three-dimensional model. The model should include components such as each joint of the robot, end effector, transmission system, etc., and an accurate three-dimensional shape is created through accurate joint and connecting rod connection relationships. The data input in this process requires strict dimensional verification to ensure that the constructed three-dimensional model is consistent with the actual robot and avoid errors that affect subsequent analysis.

[0095] Step S2: performing sensor monitoring on the three-dimensional robot model to obtain robot sensor data; performing repeated positioning accuracy analysis based on the robot sensor data to obtain repeated positioning accuracy data;

[0096] In this embodiment, after obtaining a three-dimensional robot model, a sensor monitoring process is simulated by setting a sensor interface on the model. These sensors include position sensors (such as encoders) and torque sensors, which are used to collect the robot's motion data. The robot's working state is monitored by installing virtual sensors or actual sensors, and real-time data such as position changes, torque changes, and acceleration are recorded. After completing the collection of the sensor data, the sensor data is processed using an algorithm to calculate the repeatability of the positioning. The positioning accuracy data is obtained by calculating the deviation of the robot's multiple positioning at the same position. For example, when the robot repeats the same positioning task 10 times, the standard deviation of the 10 positionings is calculated, and the standard deviation is used as a specific indicator of repeatability of the positioning accuracy. If the standard deviation exceeds 0.05 mm, it is considered low precision, otherwise it is considered high precision.

[0097] Step S3: performing load capacity analysis on the three-dimensional robot model to obtain robot load capacity data; performing wear part identification on the three-dimensional robot model based on the robot load capacity data to obtain robot wear part data;

[0098] In this embodiment, load capacity analysis is performed in finite element analysis software (such as ANSYS) based on the three-dimensional robot model. The load parameters designed for the robot, including the maximum load weight, the load capacity of the robot joints, etc., are input to simulate the load conditions under different working conditions and obtain the force distribution of each component of the robot. By applying the maximum load in the simulation, the stress and deformation of each joint and connecting rod of the robot are analyzed. Further, the stress distribution results are used to identify those parts that are subjected to large loads for a long time, which are usually the key areas of wear. For example, if a joint of the robot arm shows excessive deformation or stress concentration in the simulation, the joint is judged as a wear part. These wear part data will provide a basis for subsequent life prediction and maintenance decisions.

[0099] Step S4: performing life prediction based on the robot wear part data, thereby obtaining the robot life data; performing motion performance testing based on the robot life data, thereby obtaining the robot motion performance data;

[0100] In this embodiment, based on the wear part data identified above, the fatigue analysis method is used to predict the life of the robot. The fatigue life prediction formula (such as Miners' law) is used to combine the material properties, workload and working cycle of each component of the robot to calculate the expected life of each component. In this process, it is necessary to input the material yield strength, fatigue limit and operating conditions (such as working time, temperature, etc.) of each component of the robot. The fatigue damage data of each component is accumulated to finally obtain the total life data of the entire robot. After obtaining the robot life prediction result, the robot is actually tested through the motion performance test to evaluate the robot's motion ability when it is close to the end of its life. For example, during the test, parameters such as load conditions, motion speed and temperature are set, and data such as the actual motion accuracy and load capacity of the robot are recorded to ensure that the robot can still maintain its motion performance within the predicted life cycle.

[0101] Step S5: performing accuracy statistics based on the repeated positioning accuracy data to obtain low repeated positioning accuracy data; adjusting the servo system performance parameters of the robot motion performance data based on the low repeated positioning accuracy data to obtain the servo system performance parameters.

[0102] In this embodiment, according to the repeatability data, the positioning error is analyzed using a statistical method to calculate whether the repeatability of the robot meets the predetermined standard. If the positioning accuracy is lower than a predetermined threshold (such as a standard deviation greater than 0.05 mm), the servo system of the robot system needs to be adjusted. The specific operation includes improving the responsiveness of the robot when performing tasks by adjusting the control parameters of the servo motor (such as the proportional coefficient, integral coefficient, and differential coefficient in the PID controller). In addition, by analyzing the performance of the robot under a specific load and speed, the current and voltage settings of the servo system are optimized, thereby improving the accuracy of the robot. According to the analysis results, the settings of the servo system are adjusted in real time to ensure that the robot achieves the required motion accuracy and load capacity.

[0103] Preferably, step S1 is specifically:

[0104] Step S11: Acquire industrial robot design data, and perform mechanical joint type feature extraction to obtain mechanical joint type data;

[0105] In this embodiment, the type information of mechanical joints is extracted from the robot design drawings. Joint types generally include revolute joints, translational joints, spherical joints, etc. Use industrial CAD tools (such as SolidWorks or AutoCAD) to open the design file and extract the geometric data, kinematic characteristics, and connection relationship of each joint. The type data of the joint is determined by analyzing the structure and function of each joint of the robot. For example, if the joint allows rotational motion, it is classified as a revolute joint; if linear motion is allowed, it is classified as a translational joint. The relevant data of each type of joint, such as the maximum rotation angle, range of motion, and load capacity of the joint, will be recorded and used as joint type data. In the process of joint data extraction, it is necessary to ensure that the definitions and physical properties of all joint types are clear to avoid errors in subsequent analysis.

[0106] Step S12: performing joint degree of freedom identification according to the mechanical joint type data, thereby obtaining joint degree of freedom data;

[0107] In this embodiment, the degree of freedom refers to the number of directions in which a joint can move independently. Rotary joints usually have 1 degree of freedom, while translational joints have 1 degree of freedom, and spherical joints usually have 3 degrees of freedom. During the analysis process, the motion characteristics of each joint are first identified, and the number of degrees of freedom is determined based on its structure. If a joint can rotate around an axis, the degree of freedom of the joint is 1; if the joint can translate in one direction, the degree of freedom is 1. According to the type of joint and the motion constraints, the joint degree of freedom data is extracted, and the degree of freedom corresponding to each joint is recorded. The distribution information of all degrees of freedom will help with the subsequent kinematic analysis of the robot. For joints with multiple degrees of freedom, special attention should be paid to the interaction between the degrees of freedom to avoid misjudgment.

[0108] Step S13: performing connection relationship analysis on the mechanical joint type data according to the joint degree of freedom data, thereby obtaining mechanical joint connection relationship data;

[0109] In the present embodiment, connection relationship analysis refers to analyzing how each joint is connected to each other and how the motion is transmitted. First, identify the connection mode of each joint with the adjacent joints and record the connection parts. Then, according to the number and type of joint degrees of freedom, analyze the motion coupling relationship between each joint and other joints. For example, when two rotating joints are connected by an axis, their motions will affect each other and form a motion chain. Utilize the degree of freedom data between these joints to determine the relative position and connection mode of each joint. By analyzing the connection mode of all joints, a complete mechanical joint connection relationship data can be obtained to form a joint connection diagram. This data will provide a basis for subsequent robot kinematic modeling and simulation analysis.

[0110] Step S14: constructing a three-dimensional robot model according to the mechanical joint connection relationship data and the mechanical type data, thereby obtaining a three-dimensional robot model.

[0111] In the present embodiment, three-dimensional modeling software (such as SolidWorks or CATIA) is used to input joint type data and connection relationship data to determine the geometric shape, relative position and motion mode of each joint. According to the connection relationship data, each joint is correctly connected in three-dimensional space to form a complete kinematic chain. The shape, range of motion and degree of freedom of each joint will be accurately expressed in the three-dimensional model. In the construction process, modeling is required to be carried out in strict accordance with the type and degree of freedom of the joint to ensure that the direction of motion and the connection mode of each joint are accurate. In this process, data such as the size, degree of freedom and load capacity of all joints will help determine the accuracy of modeling and the rationality of the structure. After the three-dimensional model is built, preliminary verification is carried out to ensure that the model can be correctly operated in physical simulation.

[0112] Preferably, step S2 specifically comprises:

[0113] Step S21: performing sensor monitoring on the three-dimensional robot model to obtain robot sensor data;

[0114] In this embodiment, firstly, appropriate sensors are configured for the three-dimensional robot model. These sensors generally include force sensors, position sensors, temperature sensors and acceleration sensors, etc., which can monitor the working state of the robot and its interaction with the environment. By embedding sensors into various joints or working areas of the robot model, it is ensured that the real-time data of each sensor can be accurately collected. The force sensor is used to monitor the force applied when the robot arm contacts the workpiece, the position sensor is used to accurately measure the position change of the robot end effector, the temperature sensor is used to monitor the working temperature of each component of the robot, and the acceleration sensor monitors the dynamic response of the robot. The collected data is transmitted to the computer in real time through a data acquisition system (such as NIDataAcquisition system) for processing and storage. The sampling frequency of each sensor is set to 100Hz to ensure the real-time and accuracy of the data. The maximum effective range of the data needs to be set during the collection process, such as the maximum bearing capacity of the force sensor does not exceed 500N, and the error range of the position sensor should be controlled within 0.01mm.

[0115] Step S22: performing repeated grasping simulation according to the three-dimensional robot model to obtain repeated grasping data;

[0116] In this embodiment, a three-dimensional robot model is used to simulate repeated grasping operations. Use virtual simulation software (such as VREP or Gazebo) to simulate the robot's repeated grasping tasks. First, match the robot model with the size and material of the workpiece to ensure that the physical properties in the simulation environment are consistent with the actual application. Then, perform several repeated grasping operations, and the starting position, grasping path and speed parameters of each grasping operation are consistent. To ensure accurate simulation, set the same environmental conditions for each grasping, including the friction coefficient of the work platform (such as set to 0.3), and the grasping force limit of the robot. The changes in the position and posture of the end effector of each grasping are recorded through the simulation process to form repeated grasping data. During the simulation process, it is necessary to set the target error control range of the grasping, such as the deviation of the target position does not exceed ±1mm, and ensure that the robot can stably complete the task during each repeated grasping process.

[0117] Step S23: performing error capture calculation on the repeatedly captured data, thereby obtaining error capture data;

[0118] In this embodiment, based on the repeated grasping data, the error calculation is performed on the data. The error grasping calculation is mainly aimed at the deviation between the actual end effector position and the ideal position during each grasping process. First, set the maximum allowable error threshold (for example, ±0.5mm) between the target position and the actual grasping position. Then, use data analysis tools (such as Numpy and Pandas libraries in MATLAB or Python) to compare the actual position in each grasping operation with the predetermined target position. Calculate the error value of each grasp, including position error and angle error. For each operation, the error value is calculated according to the following formula:

[0119] Position error = √[(X actual - X target)2 + (Y actual - Y target)2 + (Z actual - Z target)2];

[0120] Angle error = |θactual-θtarget|;

[0121] Finally, the error values ​​in all grasping operations are counted, and the average error value, standard deviation and maximum error value are calculated to obtain the error grasping data.

[0122] Step S24: performing environmental error interference analysis on the three-dimensional robot model according to the error capture data, thereby obtaining environmental error interference data;

[0123] In this embodiment, the purpose of the analysis is to determine the impact of external environmental factors (such as temperature changes, mechanical wear, load changes, etc.) on the robot's grasping accuracy. First, collect parameters such as temperature, humidity, and airflow in the laboratory environment, and monitor the changes in these parameters in real time by installing environmental sensors (such as temperature and humidity sensors). Then, compare and analyze the changes in environmental parameters with the error grasping data, and use regression analysis or Kalman filtering algorithm to analyze the source of the error. Pay special attention to the effect of temperature on the thermal expansion of the robot, set the temperature change threshold to ±5°C, and evaluate the interference of this change on the robot's joint accuracy. Based on these analyses, identify the main sources of interference, and quantify the impact of each source of interference on the grasping accuracy to form environmental error interference data.

[0124] Step S25: performing environmental interference optimization on the three-dimensional robot model according to the environmental error interference data, thereby obtaining a three-dimensional robot optimization model;

[0125] In this embodiment, the optimization process includes two aspects: one is to adjust the compensation algorithm of the robot control system, and the other is to improve the hardware design of the robot. First, based on the environmental error interference data, adjust the control system of the robot, especially the PID parameters in the servo system, and reduce the errors caused by environmental factors by adjusting the gain value. Secondly, modify the design of the robot's end effector, add more stable sensors or adopt a higher precision drive system (for example, use a more precise servo motor or a high-resolution position feedback sensor). For components that are greatly affected by temperature, adopt a thermal expansion compensation design (such as installing thermocouples for real-time temperature monitoring and compensating and adjusting the joints). Verify the optimized robot model, use simulation software to re-perform the grasping simulation, evaluate the optimized accuracy performance, ensure that the error is reduced to an acceptable range, and the optimized robot model is the three-dimensional robot optimization model.

[0126] Step S26: performing repeated positioning and grasping simulation according to the three-dimensional robot optimization model, thereby obtaining repeated positioning and grasping simulation data;

[0127] In this embodiment, simulation software (such as VREP or MoveIt in the ROS environment) is used to perform multiple simulation tests. The initial conditions of each simulation are the same as the previous ones to ensure that the positioning accuracy of the robot can be verified. The target position for repeated positioning in the simulation is set to accurately control the initial conditions of each grasping of the robot (including starting position, posture, and speed, etc.). During the simulation, the end effector position data of each operation is recorded. The end effector position error of each repeated positioning operation will be recorded. After repeated grasping, the repeated positioning grasping simulation data is statistically obtained, including the error, standard deviation, and maximum error value of each grasping.

[0128] Step S27: Calculate the accuracy of the repeated positioning grabbing simulation data to obtain repeated positioning accuracy data.

[0129] In this embodiment, the deviation between the actual position and the target position of the end effector in each repeated positioning operation is calculated using the calculation formulas for position error and angle error. Then, the error data of all repeated positioning captures are statistically analyzed, and indicators such as average error, maximum error, and standard deviation are calculated to obtain repeated positioning accuracy data. The error range of all data must be within the preset accuracy requirements. If the error exceeds the set threshold (such as ±0.5mm), it is necessary to further optimize the control strategy or adjust the hardware design. Through these accuracy calculations, it is verified whether the positioning accuracy of the robot meets the process requirements.

[0130] Preferably, step S24 is specifically as follows:

[0131] Step S241: performing time statistics according to the error capture data, thereby obtaining error capture time data;

[0132] In the present embodiment, the start time and end time of each grab operation are recorded to ensure that each error calculation has a clear time range. The start and end of each operation are marked by a timestamp. Then, a data analysis tool (such as the Pandas library of MATLAB or Python) is used to calculate the duration of each grab operation. The time difference of each grab task is the grab time of the task. In order to ensure the accuracy of statistics, the maximum time threshold of the grab task can be set during the data processing process, such as the maximum allowed time of each grab operation is 5 seconds. If the task exceeds this threshold, it is marked as abnormal data and removed. Finally, the average grab time, maximum time, minimum time and standard deviation of each operation are counted to obtain error grab time data.

[0133] Step S242: performing temperature statistics on the three-dimensional robot model according to the error grasping time data, thereby obtaining robot temperature data;

[0134] In this embodiment, temperature statistics are performed on the three-dimensional robot model based on the error grasping time data. By installing temperature sensors on key components of the robot (such as servo motors, joints, etc.), the working temperature of the robot during the grasping operation is monitored in real time. The temperature sensor data is collected in real time by a data acquisition system (such as the NIData Acquisition system), and the temperature changes of each error grasping operation are recorded. Then, the temperature data is associated with the error grasping time data, and its relationship with the operation time is analyzed by calculating the temperature change of each grasping operation. For each operation, the temperature values ​​before and after the operation are recorded to ensure the accuracy of the temperature data. The calculation formula for the temperature change is:

[0135] Temperature change = temperature end value - temperature start value;

[0136] At the same time, set the temperature threshold to ensure that the data is within a reasonable range, for example, the temperature does not exceed 60°C. The temperature change data of each operation is statistically analyzed, and the average, maximum, minimum and standard deviation of the temperature are calculated to finally obtain the robot temperature data.

[0137] Step S243: performing semiconductor component thermal breakdown analysis on the three-dimensional robot model based on the robot temperature data, thereby obtaining semiconductor component thermal breakdown data;

[0138] In this embodiment, the semiconductor components involved in the robot are determined, such as transistors and diodes in the power drive module. By consulting the thermal breakdown threshold of the component (usually the maximum operating temperature provided by the component manufacturer), such as the thermal breakdown temperature of some semiconductor components is 150°C. Then, the temperature sensor data is compared with the operating temperature of the component to analyze whether the temperature exceeds the thermal breakdown threshold of the semiconductor component during each operation. Monitoring is performed by setting a temperature threshold, such as setting the temperature threshold to 100°C. When the temperature data exceeds this threshold, the event is recorded and the number of times and duration of the excess are calculated. If the temperature exceeds the thermal breakdown threshold, a detailed thermal breakdown analysis is performed, and the failure mode of the semiconductor component at high temperature is simulated in combination with the thermal breakdown characteristic model of the component, so as to obtain the thermal breakdown data of the semiconductor component.

[0139] Step S244: performing solder joint failure analysis based on the semiconductor element thermal breakdown data, thereby obtaining solder joint failure data;

[0140] In this embodiment, the failure of the solder joint is usually caused by the change of mechanical stress caused by temperature fluctuation and the influence of thermal cycle. In this step, firstly, the temperature change of the solder joint is analyzed according to the thermal breakdown of the semiconductor element. The temperature increase at the solder joint will cause the expansion of the metal material of the solder joint, thereby increasing the mechanical stress of the solder joint, and then increasing the failure probability of the solder joint. Based on the number of thermal cycles and the temperature fluctuation range of the solder joint, the thermal stress of the solder joint is calculated using a material mechanics model (such as linear thermal expansion theory or thermal stress analysis). Combined with the temperature change data, a critical temperature threshold (such as 150°C) for solder joint failure is set, and when the solder joint temperature exceeds this threshold, a failure analysis is performed. In addition, the fatigue life of the solder joint under multiple temperature fluctuations can be simulated and calculated to obtain the failure probability of each solder joint. Finally, the failure data of all solder joints are collected to obtain the solder joint failure data, including the failure location, failure cause and occurrence frequency.

[0141] Step S245: Perform environmental error interference integration according to the semiconductor element thermal breakdown data and the solder joint failure data to obtain environmental error interference data.

[0142] In this embodiment, the influence of environmental factors (such as temperature fluctuations, humidity changes, electrical noise, etc.) on the working environment of the robot is determined. Environmental sensors (such as temperature and humidity sensors, electromagnetic field sensors, etc.) are installed to monitor environmental changes in real time, and the relevant data of environmental changes and thermal breakdown and solder joint failure are recorded. Then, through statistical methods such as multivariate regression analysis or principal component analysis (PCA), the relationship between environmental factors, thermal breakdown temperature and solder joint failure is analyzed to find the main influencing factors of various environmental factors on thermal breakdown and solder joint failure. By setting the standard threshold of environmental interference (such as the temperature fluctuation range of ±5°C and the humidity fluctuation range of ±10%), the environmental error interference data is standardized, and finally the integrated environmental error interference data is obtained.

[0143] Preferably, step S243 is specifically as follows:

[0144] Performing temperature mapping on the three-dimensional robot model based on the robot temperature data, thereby obtaining a three-dimensional robot temperature model;

[0145] In this embodiment, it is necessary to obtain the working temperature data of the robot, specifically, to collect real-time temperature data through temperature sensors installed at various key parts of the robot (such as servo motors, robot joints, control panels, etc.). The temperature sensor can use devices such as thermocouples or RTD (platinum resistance temperature detectors) to output accurate temperature data. The value of each temperature sensor is collected and stored by an industrial data acquisition system (such as NI's DAQ system). Then, a three-dimensional modeling tool (such as SolidWorks, CATIA, etc.) is used to combine the geometric data of the robot components to map the temperature data to the surface of the robot model. During the mapping process, the temperature data of each part will be displayed on the three-dimensional model as the thermal properties of the component by color or heat map. Each temperature data point is matched with the corresponding coordinates of the robot model to establish a temperature distribution map to form a three-dimensional robot temperature model. Each regional temperature value in this model reflects the actual temperature state of the component, and can be thermally analyzed through further data analysis and processing.

[0146] Perform semiconductor component location recognition on the three-dimensional robot temperature model to obtain a semiconductor component temperature model;

[0147] In this embodiment, the semiconductor components are usually installed in the driving circuit, power module or signal processing unit of the robot control system. Use image recognition technology or the geometric information of the components in the CAD design file to identify these semiconductor components in the three-dimensional temperature model. Specifically, first use the electrical schematic diagram of the robot control system or robot circuit to locate the positions of all semiconductor components involved (such as power transistors, diodes, etc.). Then, these parts are marked in the three-dimensional model to obtain their corresponding temperature data. Using the aforementioned temperature mapping method, the temperature change of each semiconductor component is extracted from the overall robot temperature model to form a temperature distribution map of the semiconductor component. At this time, the semiconductor component temperature model shows the temperature state of the component location, providing basic data for the next step of thermal breakdown analysis.

[0148] Obtain semiconductor thermal breakdown temperature data and semiconductor thermal conductivity;

[0149] In this embodiment, the thermal breakdown temperature data is obtained based on the technical specifications of the semiconductor component, which can usually be found in the manufacturer's manual or technical documentation of the component. For example, the thermal breakdown temperature of some power semiconductor components is 150°C. The thermal conductivity coefficient is a physical quantity that describes the heat transfer efficiency of semiconductor materials, which is usually determined by the properties of the material itself. For example, the thermal conductivity coefficient of silicon (Si) semiconductor is 150W / (m·K). According to the type of semiconductor component selected, refer to the relevant physical property data table to obtain specific thermal breakdown temperature and thermal conductivity coefficient data. These parameters are indispensable basic data in the subsequent thermal effect calculation.

[0150] Inputting the semiconductor thermal conductivity into the semiconductor element temperature model and performing thermal accumulation effect calculation to obtain thermal accumulation effect data;

[0151] In this embodiment, based on the known semiconductor element temperature distribution model and taking into account the heat transfer process inside the element, a heat conduction equation (such as Fourier's law) is used to simulate the transfer of heat inside the element. The heat conduction equation is as follows:

[0152]

[0153] Among them, Q is the heat flow, k is the thermal conductivity, A is the heat transfer area, ΔT is the temperature difference, and Δx is the heat transfer distance. This equation can be used to calculate the change in heat flow in different time periods. Considering that multiple thermal cycles will lead to the gradual accumulation of heat inside the component, a cumulative time parameter can be introduced in the calculation process to simulate the gradual increase in thermal effect. By calculating the amount of heat accumulation in each time period and combining it with the thermal breakdown temperature data, the thermal cumulative effect data is obtained. This data reflects the accumulation of thermal stress experienced by semiconductor components in a long-term high-temperature environment.

[0154] Perform instantaneous temperature detection on the thermal accumulation effect data according to the semiconductor thermal breakdown temperature data, so as to obtain the semiconductor instantaneous over-temperature data;

[0155] In this embodiment, a temperature threshold needs to be set as a standard for judging whether the semiconductor element has instantaneous overheating. The temperature threshold is usually the thermal breakdown temperature of the semiconductor element, which can be obtained from the technical manual of the element or the specification parameters provided by the manufacturer. For example, assuming that the thermal breakdown temperature of a semiconductor element is 150°C, 150°C is set as the threshold for instantaneous overheating. This set value is of great significance because when the temperature exceeds this critical value, the element will have a thermal breakdown failure, affecting its working performance or life. Real-time temperature detection of thermal cumulative effect data is required. First, the system should continuously collect temperature data of semiconductor elements through temperature sensors, and these data are usually sampled and stored through embedded control systems. Each data point contains the temperature value in the current time period, and the sampling frequency and time period of the data can be set according to actual needs. For example, the sampling frequency can be set to once per second, and the data collection time can be set to 30 seconds or longer. As the data is continuously updated, the system calculates the cumulative temperature at each moment and compares these cumulative values ​​with the set thermal breakdown temperature. If the temperature value in the thermal cumulative effect data exceeds the set instantaneous over-temperature threshold (for example, more than 150°C) within a certain period of time, the system will mark the period as an instantaneous over-temperature event. At this time, the system will record the start and end time of the over-temperature event, and mark the temperature data during this period as instantaneous over-temperature data. In order to accurately record the occurrence time and duration of each instantaneous over-temperature event, the moment of temperature exceeding the limit can be captured by performing temperature checks second by second or minute by minute in each sampling cycle. The system should conduct a detailed analysis of each over-temperature event, check the duration of the temperature exceeding the limit and the temperature fluctuation. If the temperature exceeds the set value for more than the specified duration (such as more than 10 seconds), the severity of the event and the impact on the semiconductor components can be further determined. In this way, a complete set of semiconductor instantaneous over-temperature data can be obtained, including detailed information such as temperature data, occurrence time, duration, etc. for each over-temperature event. Automated analysis can be performed with the help of data processing software (such as MATLAB or Python). For example, using the Pandas library in Python, temperature data can be imported in real time, threshold comparisons can be performed, and time series data records can be generated. The analysis process should be able to capture instantaneous over-temperature events in real time within a short period of time and accurately calculate the time span of each event.

[0156] Based on the semiconductor instantaneous overtemperature data, the instantaneous thermal breakdown risk assessment is performed to obtain the semiconductor component thermal breakdown data.

[0157] In this embodiment, by analyzing instantaneous overtemperature data, calculating the frequency and duration of overtemperature events, and combining the thermal breakdown temperature and the ability to tolerate heat accumulation of semiconductor components, the thermal breakdown risk of semiconductor components under the current working environment is evaluated. Specifically, a standard for thermal breakdown risk assessment is set. For example, if the thermal breakdown temperature is exceeded more than 5 times, or the overtemperature time exceeds 10 seconds each time, the component is considered to have a high risk of thermal breakdown. In this way, a thermal breakdown risk model based on time and temperature can be generated, and finally the thermal breakdown data of semiconductor components can be obtained, including risk level, frequency of occurrence and other information.

[0158] Preferably, step S244 is specifically as follows:

[0159] Perform temperature fluctuation statistics based on the thermal breakdown data of semiconductor components to obtain thermal breakdown temperature fluctuation data;

[0160] In this embodiment, the thermal breakdown temperature data of semiconductor components are collected and sorted. The thermal breakdown temperature is usually determined by the experimental data or standard parameters of the component. For example, the temperature range is set to 150°C to 200°C, and the maximum temperature reached by the semiconductor component during operation under different environmental conditions is recorded. Then, a statistical analysis tool is used to perform a fluctuation analysis on these temperature data. The standard deviation or variance analysis method is used to calculate the difference between each data point and the average thermal breakdown temperature to identify the fluctuation range of the thermal breakdown temperature. Temperature fluctuation data is usually expressed in terms of standard deviation, and a higher standard deviation indicates a larger temperature fluctuation. Through this process, the thermal breakdown temperature fluctuation data is obtained, and the specific value should be set according to the experimental environment of each component. Thresholds to ensure the accuracy of the data.

[0161] Perform solder joint thermal cycle stress analysis based on thermal breakdown temperature fluctuation data to obtain solder joint thermal cycle stress data;

[0162] In this embodiment, a thermal cycle stress model is established based on the geometric characteristics of the solder joint (such as size, shape, material properties) and the thermal breakdown temperature fluctuation data. This model needs to take into account the thermal expansion characteristics and thermal stress effects of the solder joint at different temperatures. Taking the material of the solder joint (such as Sn-Pb solder) as an example, the stress change of the solder joint in each temperature fluctuation cycle can be calculated with reference to the thermal expansion coefficient of the solder and the cooling rate of the solder joint. During the simulation process, it is necessary to set the maximum temperature fluctuation range (such as ±20°C), and calculate the thermal cycle stress in combination with the thermal expansion coefficient and the yield strength of the solder joint. Through these data, the thermal cycle stress data of the solder joint is obtained, including the stress change of the solder joint under different temperature fluctuation conditions.

[0163] Solder point fatigue identification is performed based on solder point thermal cycle stress data to obtain solder point fatigue data;

[0164] In this embodiment, the fatigue cumulative damage model (such as Miner's law) is used to predict the fatigue life of the solder joint. According to the maximum stress value of the solder joint in the thermal cycle stress, combined with the fatigue limit of the solder joint material, the fatigue damage value of each thermal cycle stage is determined. Using the cumulative damage formula, the total fatigue damage value of the solder joint under multiple thermal cycles is calculated, and based on this value, it is judged whether the solder joint is close to the fatigue limit. The main parameters in this process include the fatigue limit of the solder joint material, the thermal cycle stress range and the fatigue damage rate. Throughout the process, the damage value of the solder joint at each cycle stage should be recorded, and finally the solder joint fatigue data should be obtained.

[0165] Fatigue solder joint images are collected based on solder joint fatigue data, thereby obtaining fatigue solder joint images;

[0166] In this embodiment, an appropriate high-resolution camera (such as a digital microscope or a high-definition camera) is selected to collect images of the solder joint area. When collecting the solder joint fatigue image, the clarity and sufficient contrast of the image should be ensured to facilitate subsequent image processing and analysis. During the image acquisition process, the impact of environmental factors on the image quality can be reduced by adjusting the camera's light intensity and exposure time to ensure that the details of the solder joint are clearly visible. For each solder joint fatigue area, multi-angle shooting should be performed to ensure that all potential cracks or damaged areas are covered. Through this process, high-quality fatigue solder joint images are obtained for subsequent analysis.

[0167] Performing weld edge detection on the fatigue weld image to obtain weld edge data, and marking weld edge coordinates to obtain weld edge coordinate data;

[0168] In this embodiment, an edge detection algorithm (such as Canny edge detection) is used to process the fatigue solder joint image to extract the edge contour of the solder joint. First, the image needs to be preprocessed, including denoising and grayscale operations. Then, the Canny edge detection algorithm is used to identify the solder joint edge by setting a low threshold and a high threshold. The edge detection result can extract the contour of the solder joint, which is then used for subsequent analysis. In this process, the setting of the low threshold and the high threshold can be adjusted according to the actual situation to ensure the accuracy of edge extraction. For example, the low threshold can be set to 50 and the high threshold can be set to 150 to ensure that only obvious edges are extracted. After obtaining the solder joint edge data, the coordinates of the edge can be marked, and the spatial coordinates of each pixel point on the solder joint edge can be recorded to finally obtain the solder joint edge coordinate data.

[0169] Performing polar coordinate system spatial mapping on the weld point edge coordinate data, thereby obtaining the weld point polar coordinate system spatial data;

[0170] In this embodiment, the center point of the solder joint is selected as the origin of the polar coordinate system. By using the standard polar coordinate conversion formula, the rectangular coordinates (x, y) of each edge point are converted to polar coordinates (r, θ), where r is the distance from the point to the center point and θ is the angle between the point and the horizontal line. The conversion formula is:

[0171]

[0172] Crack line identification is performed based on the spatial data of the polar coordinate system of the welding point, thereby obtaining crack line data;

[0173] In this embodiment, a straight line detection algorithm (such as Hough transform) is used to identify crack lines. First, the polar coordinate data of the weld is input into the Hough transform algorithm, and the presence of cracks is identified by detecting straight line segments in the weld area. Cracks appear as continuous straight lines or curved lines in the image, and the Hough transform can effectively detect these structures. Important parameters in this process include the minimum length and maximum deviation of straight line detection, which can be adjusted according to the resolution of the image and the characteristics of the weld. Through this step, the crack line data in the weld image is identified.

[0174] The crack area of ​​the fatigue solder joint image is marked according to the crack line data, so as to obtain the crack area data;

[0175] In this embodiment, the specific location and range of the crack are determined based on the starting and ending points of the crack line. Then, these crack areas are highlighted or marked on the image to mark the area boundaries of the crack. These marked areas include all the identified crack parts, and according to the length and width of the crack, different degrees of crack areas can be further distinguished. Through this process, the crack area data in the weld spot image is obtained, including information such as the size, shape and location of the crack.

[0176] The weld failure analysis is performed based on the crack area data to obtain the weld failure data.

[0177] In this embodiment, the risk of solder joint falling off is calculated based on the size and shape of the crack area, combined with the thermal cycle stress and fatigue data of the solder joint. If the crack area is large and there is a high cycle stress, the solder joint will fall off during operation. The key parameters in this process include the area of ​​the crack, the crack depth, the thermal cycle stress range, and the strength of the solder joint material. Through these parameters, the failure of the solder joint can be quantitatively analyzed, and finally the solder joint failure data can be obtained, including the time and location of the failure and the degree of crack influence.

[0178] Preferably, step S3 specifically comprises:

[0179] Step S31: performing dynamic load simulation on the three-dimensional robot model to obtain dynamic load data;

[0180] In the present embodiment, a three-dimensional robot model is established according to the robot's work tasks, motion paths and load conditions. The model can be modeled by CAD software (such as SolidWorks) and imported into finite element analysis software (such as ANSYS or Abaqus). When performing dynamic load simulation, it is necessary to set specific load parameters, including the load of the robot joint, the moving speed, acceleration, the physical properties of the working environment (such as gravity, air resistance), etc. The load should be set according to the actual working conditions, for example, the maximum load and the minimum load in a certain working scene are simulated. After setting the parameters, a dynamic analysis method (such as Lagrange equation or Newton-Euler method) is adopted, and dynamic loads are applied to each component of the robot to obtain the change data of the load in different time steps, and finally generate dynamic load data. The data will include the torque, acceleration, speed and corresponding displacement information of each joint at different time points.

[0181] Step S32: meshing the three-dimensional robot model according to the dynamic load data, thereby obtaining a three-dimensional robot meshing model;

[0182] In this embodiment, the purpose of meshing is to decompose the robot model into multiple small units so that more accurate results can be obtained when mechanical analysis is performed on each unit. When meshing, it is necessary to select a suitable mesh type (such as a tetrahedron or hexahedron mesh) and set the size of the mesh according to the complexity and load conditions of the robot model. For more complex or heavily loaded areas, the mesh should be set more finely, while larger meshes can be used for simpler areas. When meshing, it is necessary to consider the structural characteristics of each part and ensure the overall quality of the mesh. The specific division criteria can be completed by the automatic mesh generator in the software, or the mesh density and type of each area can be manually set to finally obtain a three-dimensional robot meshing model.

[0183] Step S33: performing grid stress node calculation on the three-dimensional robot grid division model to obtain grid stress node data;

[0184] In the present embodiment, stress analysis is performed on the three-dimensional robot meshing model by a finite element solver. First, based on dynamic load data, external loads or constraints are applied to each node of the grid to simulate the actual working environment. The stress of each grid unit is calculated using a stress-strain relationship formula (Hooke's Law) or a material constitutive model (such as an elastomer or plastic body model). The stress calculation results will include the stress value of each node, and it is usually necessary to combine static analysis with dynamic analysis. During the analysis process, it is necessary to ensure that all boundary conditions (such as fixed support, constraints) and loading conditions are accurate. The grid stress node data obtained will contain the stress value, strain value, and maximum stress point and position of each node.

[0185] Step S34: drawing a stress distribution diagram based on the grid stress node data, thereby obtaining a stress distribution diagram;

[0186] In this embodiment, the figure shows the stress distribution of each component of the robot under different working conditions by means of color gradient or contour lines. When drawing the stress distribution diagram, first determine the range of the maximum stress and minimum stress of the data, and set the threshold of the color mapping according to this range. Areas with higher stress are usually represented by red, while areas with lower stress are represented by blue. By intuitively presenting the stress change trend of each part in a graphical manner, potential high-stress areas can be identified. The drawing of the stress distribution diagram is usually completed through the post-processing module in the software, and the result will be displayed in a three-dimensional view. The view can be rotated, scaled, and cut to facilitate the observation of the stress conditions of different parts.

[0187] Step S35: identifying high stress areas according to the stress distribution diagram, thereby obtaining high stress area data;

[0188] In this embodiment, an appropriate stress threshold is selected (for example, 80% of the material limit stress value of the robot component), and the area with higher stress is divided by the threshold. The identification process can be realized by the automatic identification tool in the software, or these areas can be manually marked. The obtained high stress area data will include the specific coordinates, stress values ​​and geometric shapes of these areas. This data will be used for the subsequent deformation analysis of the robot load structure.

[0189] Step S36: performing robot load structure deformation analysis on the high stress area data, thereby obtaining robot load structure deformation data;

[0190] In this embodiment, the mechanical properties of the application material and the geometric characteristics of the robot are combined with dynamic load data to simulate the deformation of the high stress area under load. The deformation analysis adopts the finite element method, and the displacement, strain, deformation and other information of each node are obtained by applying a load and iteratively calculating. The material parameters used include elastic modulus, Poisson's ratio, etc., and appropriate boundary conditions (such as constraints, support points) need to be set according to the specific load conditions. According to the deformation analysis results, the deformation data of each component of the robot under load can be obtained, and the degree of deformation in the high stress area is focused on.

[0191] Step S37: evaluating the load capacity according to the robot load structure deformation data, thereby obtaining the robot load capacity data;

[0192] In this embodiment, the load capacity of the robot under different loads is evaluated based on the deformation data of the robot load structure. The load capacity evaluation is mainly carried out by comparing the deformation data and the safety margin of the structure. During the specific evaluation, the maximum deformation of the robot and the preset allowable deformation range can be calculated, and whether there is a risk of structural failure can be determined based on the yield strength and ultimate strength of the material. The load capacity data includes the maximum loadable load, the maximum deformation value, and the safety factor of the structure. During the evaluation process, a standard safety factor (for example, a safety factor of 1.5 to 2) must be used to ensure the reliability of the robot in long-term use.

[0193] Step S38: Identify wear parts of the three-dimensional robot model according to the robot load capacity data, so as to obtain robot wear part data.

[0194] In this embodiment, wear site identification usually relies on high stress areas in the load capacity data and deformation data. First, by setting the wear threshold, determine which areas are worn due to repeated loads. Based on the load capacity assessment results, especially those areas where the stress exceeds a certain value, they will be used as potential wear sites. The wear threshold usually depends on the fatigue limit and safety factor of the material. The identified wear site data will include the location, shape, stress value and degree of wear of the wear area. These data will provide a basis for subsequent robot maintenance and life prediction.

[0195] Preferably, step S36 is specifically as follows:

[0196] Step S361: Obtaining robot material yield standard data;

[0197] In this embodiment, it is necessary to query or obtain the yield strength data of the material from the relevant standard database (such as ASM Handbooks or ISO 6892 standard) according to the type of material used by the robot. Yield strength is usually measured in megapascals (MPa), which indicates the maximum stress value at which the material changes from elastic deformation to plastic deformation under force. The selected material should include common industrial robot materials such as steel, aluminum alloy or titanium alloy. The specific yield strength value needs to be combined with the working environment, application type (such as welding, handling, etc.) of the robot and the characteristics of the material. After obtaining the yield strength of the material, the data is input into the robot finite element analysis software as an important parameter in subsequent analysis. If the yield strength value of the material cannot be directly found in the standard document, it is necessary to obtain it through a laboratory test method (such as a tensile test) and perform data processing according to the standard provisions.

[0198] Step S362: performing a preliminary deformation determination on the high stress area data according to the robot material yield standard data, thereby obtaining the robot load deformation data;

[0199] In this embodiment, high stress areas in the robot model are identified, and the stress analysis tool provided in the software is used to compare the stress value of the high stress area with the material yield standard. If the stress value of a node or area exceeds the yield strength of the material, it means that the area has entered the plastic deformation stage and further deformation judgment is required. The specific steps include setting stress thresholds (such as 80% to 100% of the yield strength) and applying these thresholds to the high stress area data to screen out the areas where deformation occurs. The robot load deformation data finally obtained will contain the degree of deformation of each node or area under the load, the type of deformation (such as stretching, compression, bending, etc.) and its range of variation.

[0200] Step S363: identifying the displacement direction of the robot load deformation data, thereby obtaining displacement direction data;

[0201] In this embodiment, the identification of displacement direction is performed by the post-processing module of finite element analysis (FEA) software. First, after stress analysis, the displacement vector data of each node will be output, and the displacement vector represents the moving direction and displacement of each node under the load, including the displacement of the node in the X, Y, and Z directions. By obtaining the displacement data of these nodes, the deformation under the load can be calculated. Then, according to the displacement vector data, the identification of displacement direction is determined by calculating the displacement of each node in the X, Y, and Z axis directions. Specifically, the displacement direction is identified by calculating the displacement and directionality of each node. For example, if the displacement in the X-axis direction is positive, the displacement of the node in the X-axis direction is positive, otherwise it is negative, and other axes are also judged in this way. In order to ensure the significance of the displacement, a threshold is set. Only when the displacement of the node is greater than the set value (for example, 0.01mm), its displacement direction is considered valid, and the displacement data below the threshold will be screened out. Finally, the system records the effective displacement direction data, including the displacement direction and moving axis (such as X, Y, and Z axes) of each node, and combines the angle information for accurate displacement analysis when necessary. Through these steps, the deformation direction of each node of the robot structure under load can be identified, providing a basis for subsequent structural analysis, optimization, and fault prediction.

[0202] Step S364: calculating the deformation amount of the displacement direction data, thereby obtaining deformation amount data;

[0203] In this embodiment, the calculation of deformation is mainly completed by the size of the displacement vector and the geometry of the deformation area. In finite element analysis, the deformation can be expressed by the amplitude of the displacement, and the calculation formula is deformation = modulus of the displacement vector (ie |Δx, Δy, Δz|). For each node, calculate its displacement value in the X, Y, and Z directions to obtain the total displacement. In order to further refine the deformation analysis, combined with the structure of each part of the robot, it is necessary to divide the deformation into regions. For example, a large deformation occurs in a high stress area, so it is necessary to focus on the deformation of these areas. The deformation data will help engineers evaluate whether the robot is in normal working condition and can reflect the response of the robot structure under load.

[0204] Step S365: determining the robot load structure based on the dynamic load data, thereby obtaining the robot load structure;

[0205] In the present embodiment, it is necessary to determine the load bearing capacity and response behavior of each component during operation. According to the dynamic load data (including information such as force, speed, acceleration, etc.) obtained from step S31, combined with the structural design of the robot (such as joint type, material properties, etc.), the robot structure under load is simulated and analyzed. Finite element analysis tools can be used to perform structural response analysis by loading dynamic load data. By gradually increasing the load and monitoring the response of the robot, the reliability and adaptability of the robot load structure can be determined. Ultimately, the robot load structure data obtained will include the response of each component under different load conditions, including stress, displacement, vibration mode, etc.

[0206] Step S365: determining the structural deformation of the robot load structure according to the deformation data, thereby obtaining the robot load structure deformation data.

[0207] In this embodiment, the deformation data needs to be input into the structural analysis module, which uses finite element analysis (FEA) software for overall simulation. The first step of the simulation process is to combine the deformation with the load data and estimate the actual deformation of each component of the robot through the known stress-strain relationship. The calculation of the stress-strain relationship depends on the constitutive model of the material. For example, for common materials such as steel and aluminum alloy, a linear elastic model is usually used to describe the deformation behavior in the elastic stage, and for the part beyond the elastic range, a plastic model or other nonlinear model is used. In the structural analysis process, the material properties of each structural unit must first be defined, especially the basic parameters such as yield strength, elastic modulus, and Poisson's ratio, and simulation is performed based on these parameters. The deformation of each component will be mapped according to the stress field in which it is located, and its response under the load will be simulated and calculated. For example, for the rotary joint of the robot arm, if the deformation of a certain area exceeds the preset threshold, it means that the area enters the plastic deformation stage, which will affect the overall accuracy and performance of the robot. The key step in determining the structural deformation is to set the deformation threshold, and the setting of the threshold is adjusted according to multiple factors, including the yield strength of the material, the working environment of the robot, and the task load. The typical threshold setting method is to set it according to a certain percentage of the yield strength of the material, for example, 80% of the yield strength is used as the critical value. When the deformation exceeds the threshold, it is considered that the robot load structure has undergone a significant deformation and requires further maintenance or structural optimization. The simulation tool will generate a detailed structural deformation data report, including the deformation amount of each component under different loads, deformation type (such as tension, compression, bending), and stress concentration area data. All of these data can serve as an important basis for subsequent design optimization, fault prediction, and maintenance planning. In order to ensure the reliability of the robot under different working conditions, the deformation data also needs to be compared with the actual use of the robot to verify whether there are abnormalities or hidden dangers in any local areas.

[0208] Preferably, step S38 is specifically as follows:

[0209] Step S381: drawing a load distribution diagram according to the robot load capacity data, thereby obtaining a load distribution diagram;

[0210] In this embodiment, the load capacity data of each part of the robot are first collected. These data are obtained by real-time collection of the load conditions during the movement of the robot, and usually include the load sensor data of each joint, arm, tool end, etc. The load distribution diagram is drawn using the thermodynamics or stress analysis module in the finite element analysis software. This module can automatically generate the load distribution diagram of the robot in each working state based on the collected data, combined with the robot's geometric shape and working environment. When generating the load distribution diagram, a load threshold is set. For example, when the load exceeds a certain standard value (such as 20Nm), the load intensity of different areas will be indicated by color depth on the diagram to help analyze the load distribution of the robot during work.

[0211] Step S382: Identify uneven parts of the load distribution diagram, so as to obtain data of uneven parts of the robot load;

[0212] In this embodiment, the load distribution is analyzed by using image processing or data processing algorithms. First, the load data in the image is discretized, and the load value is compared with the load standard through an algorithm to set a threshold for uneven distribution (for example, when the load difference exceeds 10%, it is determined to be uneven). In this step, the data threshold processing method is adopted, and all areas where the load exceeds this threshold are marked as uneven parts. The processing results are output as a data set of uneven parts, including the specific coordinates and load difference values ​​of each uneven part, providing detailed part data for subsequent analysis.

[0213] Step S383: performing long-term load location identification on the robot load uneven location data, thereby obtaining long-term load location data;

[0214] In this embodiment, by analyzing the time dimension in the data of the unevenly loaded parts, the duration of the load change is counted. By accumulating the load time of each uneven part in the robot working cycle, a time threshold is set (for example, if the load of a certain part lasts for more than 30 minutes, it can be considered that the part is in a long-term load state). Through the time window sliding statistical method, the areas with long-lasting loads are identified and the data of these parts are recorded. In the identification process, a combination of time and load intensity is used to determine which parts maintain a high load for a long time.

[0215] Step S384: performing joint periodic impact position analysis on the long-term load position data, thereby obtaining joint periodic impact position data;

[0216] In this embodiment, the long-term load position data is analyzed for the periodic impact position of the joints, so as to obtain the periodic impact position data of the joints. This step is combined with the joint load data, especially the data under the periodic working state for analysis. By analyzing the periodic changes in the load, the joint parts where the load changes significantly in each movement cycle are identified. For example, a periodic threshold is set. For example, when the load fluctuates by more than 10% in a cycle, the joint can be regarded as a periodic impact joint. This analysis extracts the periodic components by Fourier transforming the load data, and then identifies the periodic impact parts and records the impact data of each joint.

[0217] Step S385: Calculate the wear based on the periodic impact site data of the joint, so as to obtain the wear site data of the robot.

[0218] In this embodiment, wear calculation is performed based on the identification data of the periodic impact parts of the joints, combined with the friction mechanics and fatigue analysis models. First, the impact frequency and intensity are extracted according to the periodic impact data of the joints, and then the amount of wear generated by each joint under each impact is calculated according to the set friction coefficient (such as 0.15) and material properties. Through this calculation, the accumulated amount of wear on each part is obtained. A threshold is set (for example, when the wear exceeds 0.5mm, it is considered that the part has undergone severe wear), and the wear part data is output, including the wear value of each joint, the wear type (such as surface wear, material fatigue, etc.), to provide a basis for subsequent maintenance and replacement.

[0219] Preferably, the present specification also provides a performance parameter acquisition system for an industrial robot, which is used to execute the performance parameter acquisition method for an industrial robot as described above, and the performance parameter acquisition system for an industrial robot includes:

[0220] A model building module is used to obtain industrial robot design data and build a three-dimensional robot model to obtain a three-dimensional robot model;

[0221] The repeatable positioning accuracy analysis module is used to perform sensor monitoring on the three-dimensional robot model to obtain robot sensor data; perform repeatable positioning accuracy analysis based on the robot sensor data to obtain repeatable positioning accuracy data;

[0222] The wear part identification module is used to perform load capacity analysis based on the three-dimensional robot model to obtain the robot load capacity data; and to identify the wear parts of the three-dimensional robot model based on the robot load capacity data to obtain the robot wear part data;

[0223] The motion performance test module is used to predict the life of the robot based on the wear part data of the robot, so as to obtain the robot life data; and to perform motion performance test based on the robot life data, so as to obtain the robot motion performance data;

[0224] The parameter adjustment module is used to perform accuracy statistics based on the repeated positioning accuracy data to obtain low repeated positioning accuracy data; and to adjust the servo system performance parameters of the robot motion performance data based on the low repeated positioning accuracy data to obtain the servo system performance parameters.

[0225] The performance parameter acquisition system of the industrial robot of the present invention can implement the performance parameter acquisition method of any industrial robot of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the performance parameter acquisition method of the industrial robot. The internal modules of the system cooperate with each other to optimize the motion accuracy of the industrial robot and the performance of the servo system.

[0226] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0227] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for obtaining performance parameters of an industrial robot, characterized in that: The following steps are involved: Step S1: Acquire industrial robot design data and construct a three-dimensional robot model to obtain a three-dimensional robot model; Step S2: performing sensor monitoring on the three-dimensional robot model to obtain robot sensor data; performing repeated positioning accuracy analysis based on the robot sensor data to obtain repeated positioning accuracy data; Step S3: performing load capacity analysis on the three-dimensional robot model to obtain robot load capacity data; performing wear part identification on the three-dimensional robot model based on the robot load capacity data to obtain robot wear part data; Step S4: performing life prediction based on the robot wear part data, thereby obtaining the robot life data; performing motion performance testing based on the robot life data, thereby obtaining the robot motion performance data; Step S5: performing accuracy statistics based on the repeated positioning accuracy data to obtain low repeated positioning accuracy data; adjusting the servo system performance parameters of the robot motion performance data based on the low repeated positioning accuracy data to obtain the servo system performance parameters.

2. The method for obtaining performance parameters of an industrial robot according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: Acquire industrial robot design data, and perform mechanical joint type feature extraction to obtain mechanical joint type data; Step S12: performing joint degree of freedom identification according to the mechanical joint type data, thereby obtaining joint degree of freedom data; Step S13: performing connection relationship analysis on the mechanical joint type data according to the joint degree of freedom data, thereby obtaining mechanical joint connection relationship data; Step S14: constructing a three-dimensional robot model according to the mechanical joint connection relationship data and the mechanical type data, thereby obtaining a three-dimensional robot model.

3. The method for obtaining performance parameters of an industrial robot according to claim 1, characterized in that: Step S2 is specifically as follows: Step S21: performing sensor monitoring on the three-dimensional robot model to obtain robot sensor data; Step S22: performing repeated grasping simulation according to the three-dimensional robot model to obtain repeated grasping data; Step S23: performing error capture calculation on the repeatedly captured data, thereby obtaining error capture data; Step S24: performing environmental error interference analysis on the three-dimensional robot model according to the error capture data, thereby obtaining environmental error interference data; Step S25: performing environmental interference optimization on the three-dimensional robot model according to the environmental error interference data, thereby obtaining a three-dimensional robot optimization model; Step S26: performing repeated positioning and grasping simulation according to the three-dimensional robot optimization model, thereby obtaining repeated positioning and grasping simulation data; Step S27: Calculate the accuracy of the repeated positioning grabbing simulation data to obtain repeated positioning accuracy data.

4. The method for obtaining performance parameters of an industrial robot according to claim 3, characterized in that: Step S24 is specifically as follows: Step S241: performing time statistics according to the error capture data, thereby obtaining error capture time data; Step S242: performing temperature statistics on the three-dimensional robot model according to the error grasping time data, thereby obtaining robot temperature data; Step S243: performing semiconductor component thermal breakdown analysis on the three-dimensional robot model based on the robot temperature data, thereby obtaining semiconductor component thermal breakdown data; Step S244: performing solder joint failure analysis based on the semiconductor element thermal breakdown data, thereby obtaining solder joint failure data; Step S245: Perform environmental error interference integration according to the semiconductor element thermal breakdown data and the solder joint failure data to obtain environmental error interference data.

5. The method for obtaining performance parameters of an industrial robot according to claim 4, characterized in that: Step S243 is specifically as follows: Performing temperature mapping on the three-dimensional robot model based on the robot temperature data, thereby obtaining a three-dimensional robot temperature model; Perform semiconductor component location recognition on the three-dimensional robot temperature model to obtain a semiconductor component temperature model; Obtain semiconductor thermal breakdown temperature data and semiconductor thermal conductivity; Inputting the semiconductor thermal conductivity into the semiconductor element temperature model and performing thermal accumulation effect calculation to obtain thermal accumulation effect data; Perform instantaneous temperature detection on the thermal accumulation effect data according to the semiconductor thermal breakdown temperature data, so as to obtain the semiconductor instantaneous over-temperature data; Based on the semiconductor instantaneous overtemperature data, the instantaneous thermal breakdown risk assessment is performed to obtain the semiconductor component thermal breakdown data.

6. The method for obtaining performance parameters of an industrial robot according to claim 4, characterized in that: Step S244 is specifically as follows: Perform temperature fluctuation statistics based on the thermal breakdown data of semiconductor components to obtain thermal breakdown temperature fluctuation data; Perform solder joint thermal cycle stress analysis based on thermal breakdown temperature fluctuation data to obtain solder joint thermal cycle stress data; Solder point fatigue identification is performed based on solder point thermal cycle stress data to obtain solder point fatigue data; Fatigue solder joint images are collected based on solder joint fatigue data, thereby obtaining fatigue solder joint images; Performing weld edge detection on the fatigue weld image to obtain weld edge data, and marking weld edge coordinates to obtain weld edge coordinate data; Performing polar coordinate system spatial mapping on the weld point edge coordinate data, thereby obtaining the weld point polar coordinate system spatial data; Crack line identification is performed based on the spatial data of the polar coordinate system of the welding point, thereby obtaining crack line data; The crack area of ​​the fatigue solder joint image is marked according to the crack line data, so as to obtain the crack area data; The weld failure analysis is performed based on the crack area data to obtain the weld failure data.

7. The method for obtaining performance parameters of an industrial robot according to claim 1, characterized in that: Step S3 is specifically as follows: Step S31: performing dynamic load simulation on the three-dimensional robot model to obtain dynamic load data; Step S32: meshing the three-dimensional robot model according to the dynamic load data, thereby obtaining a three-dimensional robot meshing model; Step S33: performing grid stress node calculation on the three-dimensional robot grid division model to obtain grid stress node data; Step S34: drawing a stress distribution diagram based on the grid stress node data, thereby obtaining a stress distribution diagram; Step S35: identifying high stress areas according to the stress distribution diagram, thereby obtaining high stress area data; Step S36: performing robot load structure deformation analysis on the high stress area data, thereby obtaining robot load structure deformation data; Step S37: evaluating the load capacity according to the robot load structure deformation data, thereby obtaining the robot load capacity data; Step S38: Identify wear parts of the three-dimensional robot model according to the robot load capacity data, so as to obtain robot wear part data.

8. The method for obtaining performance parameters of an industrial robot according to claim 7, characterized in that: Step S36 is specifically as follows: Step S361: Obtaining robot material yield standard data; Step S362: performing a preliminary deformation determination on the high stress area data according to the robot material yield standard data, thereby obtaining the robot load deformation data; Step S363: identifying the displacement direction of the robot load deformation data, thereby obtaining displacement direction data; Step S364: calculating the deformation amount of the displacement direction data, thereby obtaining deformation amount data; Step S365: determining the robot load structure based on the dynamic load data, thereby obtaining the robot load structure; Step S365: determining the structural deformation of the robot load structure according to the deformation data, thereby obtaining the robot load structure deformation data.

9. The method for obtaining performance parameters of an industrial robot according to claim 7, characterized in that: Step S38 is specifically as follows: Step S381: drawing a load distribution diagram according to the robot load capacity data, thereby obtaining a load distribution diagram; Step S382: Identify uneven parts of the load distribution diagram, so as to obtain data of uneven parts of the robot load; Step S383: performing long-term load location identification on the robot load uneven location data, thereby obtaining long-term load location data; Step S384: performing joint periodic impact position analysis on the long-term load position data, thereby obtaining joint periodic impact position data; Step S385: Calculate the wear based on the periodic impact site data of the joint, so as to obtain the wear site data of the robot.

10. A performance parameter acquisition system for an industrial robot, characterized in that: Used to execute the performance parameter acquisition method of an industrial robot as claimed in claim 1, the performance parameter acquisition system of the industrial robot comprises: A model building module is used to obtain industrial robot design data and build a three-dimensional robot model to obtain a three-dimensional robot model; The repeatable positioning accuracy analysis module is used to perform sensor monitoring on the three-dimensional robot model to obtain robot sensor data; perform repeatable positioning accuracy analysis based on the robot sensor data to obtain repeatable positioning accuracy data; The wear part identification module is used to perform load capacity analysis based on the three-dimensional robot model to obtain the robot load capacity data; and to identify the wear parts of the three-dimensional robot model based on the robot load capacity data to obtain the robot wear part data; The motion performance test module is used to predict the life of the robot based on the wear part data of the robot, so as to obtain the robot life data; and to perform motion performance test based on the robot life data, so as to obtain the robot motion performance data; The parameter adjustment module is used to perform accuracy statistics based on the repeated positioning accuracy data to obtain low repeated positioning accuracy data; and to adjust the servo system performance parameters of the robot motion performance data based on the low repeated positioning accuracy data to obtain the servo system performance parameters.

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