Intelligent driving method and system for swing guide rod electric cylinder

Abstract

The invention relates to the technical field of electric actuating mechanism control, in particular to an intelligent driving method and system for a swing guide rod electric cylinder. The method comprises the following steps: acquiring design data of the electric bumper; performing structure initialization according to the electric cylinder design data to obtain an electric cylinder initialization structure; performing guide rod swing simulation according to the initialized structure of the electric cylinder to obtain guide rod swing data; performing guide rod swing clamping stagnation analysis according to the guide rod swing data to obtain guide rod swing clamping stagnation data; the abrasion degree of the sliding bearing is detected based on guide rod swing clamping stagnation data; performing magnetic powder crack detection based on the wear degree of the sliding bearing to generate crack data; predicting the metal fatigue life of the guide rod based on the crack data; and evaluating the deflection degree of the guide rod according to the swing data of the guide rod. Based on the electric actuating mechanism control technology, the reliability and accuracy of the electric cylinder are improved, the service life of the electric cylinder is prolonged, and the fault occurrence rate is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric actuator control, in particular to an intelligent driving method and system for a swing guide rod electric cylinder. BACKGROUND

[0002] The traditional swing guide rod electric cylinder driving lacks dynamic monitoring and intelligent recognition ability for the actual running state of the guide rod, cannot detect the jamming behavior in the guide rod swing process or the wear state of the sliding bearing in real time, and thus cannot timely alarm when a slight structural failure occurs, thereby causing problems such as guide rod jamming and guide rail damage. For the ball screw structure, the gap is usually judged and maintained by means of artificial periodic inspection, and there is a lack of automatic detection and compensation mechanism for the abnormal gap of the screw rod, which is easy to cause positioning deviation, efficiency reduction and mechanical fatigue and other hidden dangers in long-term operation. In the aspect of trajectory planning, the existing technology is mostly based on task path or servo strategy, and the metal fatigue life variation law of the guide rod material is not fully considered, which leads to too strong path rigidity, aggravation of fatigue risk, shortening of service life, and even affects operation safety. More importantly, the coordination between the analysis modules of the traditional system is weak, and there is a lack of integrated linkage mechanism from structure design, state monitoring, life evaluation to driving optimization, which makes it difficult to form a complete closed-loop intelligent driving scheme, and limits the intelligent, adaptive and high-reliable operation ability of the system. SUMMARY

[0003] Therefore, it is necessary to provide an intelligent driving method and system for a swing guide rod electric cylinder to solve at least one of the above technical problems.

[0004] To achieve the above-mentioned purpose, an intelligent driving method for a swing guide rod electric cylinder comprises the following steps: Step S1: obtaining electric cylinder design data; performing structure initialization according to the electric cylinder design data to obtain an electric cylinder initialization structure; and performing guide rod swing simulation according to the electric cylinder initialization structure to obtain guide rod swing data; Step S2: performing guide rod swing jamming analysis according to the guide rod swing data to obtain guide rod swing jamming data; detecting the wear degree of the sliding bearing based on the guide rod swing jamming data; performing magnetic powder crack detection based on the wear degree of the sliding bearing to generate crack data; and predicting the metal fatigue life of the guide rod based on the crack data; Step S3: evaluating the guide rod deflection degree according to the guide rod swing data; performing ball screw gap abnormality analysis based on the guide rod deflection degree to obtain ball screw gap abnormality data; and designing an automatic gap compensation structure according to the ball screw gap abnormality data; Step S4: According to the guide rod metal fatigue life, the guide rod intelligent trajectory planning is carried out, and the guide rod intelligent trajectory is generated; according to the automatic gap compensation structure, the electric cylinder initialization structure is optimized, and the electric cylinder optimized structure is obtained; according to the electric cylinder optimized structure and the guide rod intelligent trajectory, the guide rod intelligent driving simulation is carried out, and the guide rod intelligent driving data is generated.

[0005] The present application can comprehensively master the electric cylinder structure characteristics and guide rod motion characteristics at the early stage of design through structure initialization and guide rod swing simulation, and lay the foundation for subsequent state perception and optimization. Through in-depth analysis of the guide rod swing data, the sticking behavior of the guide rod in actual operation can be identified, and then combined with the sliding bearing wear state and the magnetic powder crack detection technology, early diagnosis and life trend prediction of internal structure abnormalities can be realized, and the safety and predictability of system operation can be improved. At the same time, by combining guide rod deflection evaluation and ball screw gap abnormality analysis, dynamic monitoring of screw nut matching precision can be realized, effectively avoiding positioning deviation and motion lag problems caused by out-of-control gap, and further through the design and integration of automatic gap compensation structure, the compensation behavior has self-adaptability and effectiveness, which significantly improves the transmission accuracy and the reliability of the whole device. In addition, the intelligent planning of the guide rod trajectory is carried out on the basis of the prediction results of the metal fatigue life, which can actively adjust the running track according to the fatigue sensitive area, avoid repeated loading of high fatigue path, effectively prolong the service cycle of the guide rod, and reduce the failure rate. Finally, the fatigue information, gap compensation and optimized structure are applied to the driving simulation, the synchronous matching of driving path and structure performance is realized, an intelligent closed-loop system covering design, monitoring, diagnosis, optimization and control is formed, the limitations of traditional technology isolated analysis and artificial maintenance are broken through, and the intelligent level, self-adaptive ability and high reliable operation ability of the electric cylinder system are significantly improved.

[0006] Preferably, step S1 specifically comprises: Step S11: Obtain electric cylinder design data, and extract servo motor structure data and ball screw structure data; Step S12: Identify the screw transmission structure according to the servo motor structure data; Step S13: Construct a guide rail support structure model according to the screw transmission structure; Step S14: Analyze the screw transmission parameters according to the ball screw structure data; Step S15: Input the screw transmission parameters into the guide rail support structure model, and carry out electric cylinder component coordination matching to obtain electric cylinder component coordination matching data; Step S16: According to the electric cylinder component coordination matching data, virtual assembly is carried out, and the electric cylinder initialization structure is obtained; Step S17: According to the electric cylinder initialization structure, the guide rod swing simulation is carried out, and the guide rod swing data is obtained.

[0007] The application obtains the electric cylinder design data and extracts the structure data of the servo motor and the ball screw, provides a basis for subsequent structure analysis and optimization, and ensures the comprehensiveness and accuracy of the electric cylinder design. By identifying the screw rod transmission structure, the mutual relationship and transmission efficiency between the screw rod and other components can be effectively determined, and the efficiency and stability of the driving system can be ensured. Based on the establishment of the screw rod transmission structure, the guide rail support structure model is reconstructed, which can accurately reflect the movement support frame of the electric cylinder and ensure the stability and coordination of the system during operation. After analyzing the screw transmission parameters, combined with the guide rail support structure model, the entire electric cylinder assembly can realize accurate coordination matching during the design process, thereby improving the matching degree and adaptability of the system, laying a foundation for subsequent virtual assembly, reducing errors and adjustment costs in physical assembly. The initialization structure of the electric cylinder obtained after virtual assembly ensures accurate simulation and function verification before actual operation, which can effectively eliminate potential problems in the design stage and discover abnormalities such as guide rod swing in advance. Finally, through the guide rod swing simulation, not only the motion data of the guide rod can be obtained, but also the jamming phenomenon that may occur in the simulation can be predicted, providing data support for subsequent maintenance and improvement, and further improving the reliability and intelligent level of the system. Through the comprehensive application of this series of steps, the problems of lack of dynamic monitoring of structure state, inability to evaluate mechanical fatigue in real time and insufficient automatic compensation in traditional methods can be effectively made up, the overall intelligence and efficient operation ability of the electric cylinder are improved, and the system's early warning and adaptability to faults are enhanced.

[0008] Preferably, step S16 specifically comprises: Step S161: identifying component interfaces according to electric cylinder assembly coordination matching data; Step S162: performing electric cylinder geometric structure alignment processing based on the component interfaces to obtain electric cylinder assembly reference data; Step S163: setting assembly constraint relationships according to the electric cylinder assembly reference data; Step S164: performing virtual assembly according to the assembly constraint relationships to obtain an electric cylinder initialization structure.

[0009] The application can accurately analyze the connection relationship between each component by identifying the electric cylinder assembly interface, and provide clear interface data for the subsequent assembly process. The application of this step makes the docking in the assembly process more accurate, avoiding the assembly errors and instability caused by unclear assembly interface in the traditional way. Then, through the electric cylinder geometric structure alignment processing based on the component interface, not only the geometric precision of each part in the assembly process is ensured, but also the coordination and cooperation between components are ensured, so that the overall operation of the system is more efficient and stable. The obtained electric cylinder assembly reference data provides strong support for subsequent setting of assembly constraint relationship, effectively reducing the error accumulation caused by lack of constraint conditions in traditional assembly. Through the setting of these assembly constraint relationships, the relative position and motion precision of each component in the assembly process can be ensured, so that potential problems can be identified in advance during virtual assembly, and the phenomenon of jamming or failure of the electric cylinder in actual application due to inaccurate component docking can be avoided. Finally, through virtual assembly, the electric cylinder initialization structure obtained can provide sufficient data support for subsequent performance testing and optimization. This method effectively avoids the problem of insufficient dynamic monitoring of the guide rod and other parts in traditional technology, and improves the intelligent identification and adaptability of the whole system. Through the optimization of this process, the risk of structural failure caused by improper assembly can be greatly reduced, and the operation reliability and safety of the electric cylinder system are improved, which lays a solid foundation for subsequent state monitoring and intelligent adjustment.

[0010] Preferably, step S2 is specifically: Step S21: According to the guide rod swing data, the guide rod swing speed is calculated; Step S22: Calculate the instantaneous swing deceleration time period based on the guide rod swing speed; Step S23: Detect the swing amplitude stagnation and sudden change according to the instantaneous swing deceleration time period, and obtain the swing amplitude stagnation data; Step S24: Determine the guide rod swing jamming based on the swing amplitude stagnation data, and obtain the guide rod swing jamming data; Step S25: Detect the sliding bearing wear degree based on the guide rod swing jamming data; Step S26: Perform magnetic powder crack detection based on the sliding bearing wear degree, and generate crack data; Step S27: Predict the guide rod metal fatigue life based on the crack data.

[0011] The present application can accurately identify the working state of the guide rod by counting the guide rod swing speed, providing accurate basic data for subsequent analysis. The swing speed is further used to calculate the instantaneous swing deceleration period, effectively capturing the deceleration characteristics of the guide rod during the swing process, providing a reliable time window for detecting the jamming behavior. Based on this time period, the ability to detect the swing amplitude stagnation and sudden change can provide early warning of potential jamming problems, reducing the neglect of such minor faults in traditional methods and preventing them from evolving into serious faults in subsequent stages. Further analysis of the swing amplitude stagnation data can accurately determine whether the guide rod has jammed, and further identify the jamming risk, which provides important early warning information for subsequent maintenance. Based on the jamming data, combined with the wear detection of the sliding bearing, the wear degree of the bearing can be found in time, avoiding the running abnormalities caused by bearing damage in traditional methods. Next, through the magnetic powder crack detection, the crack state of the sliding bearing or other key components can be further analyzed to generate crack data, which provides accurate data support for timely replacement or repair of worn parts. Finally, based on the crack data, the prediction of the guide rod metal fatigue life enables the system to identify the metal fatigue risk early, avoid structural failure caused by fatigue damage, prolong the service life of the electric cylinder, and improve the operation safety. Through this series of steps, the entire electric cylinder system realizes comprehensive monitoring and intelligent optimization of the dynamic state, greatly improving the reliability, adaptability and self-regulation ability of the system, overcoming the problems of insufficient intelligence and fault warning in traditional technology, and improving the overall performance and safety.

[0012] Preferably, step S25 specifically comprises: Step S251: extracting swing position offset information according to the guide rod swing jamming data; Step S252: extracting the sliding bearing position based on the swing position offset information; Step S253: performing sliding simulation according to the sliding bearing position to obtain bearing sliding data; Step S254: performing material surface layer oxidation analysis based on the bearing sliding data to obtain material surface layer oxidation data; Step S255: performing metal particle detection according to the material surface layer oxidation data to obtain metal particle data; Step S256: determining the wear degree of the sliding bearing according to the metal particle data.

[0013] The present application extracts the swing position offset information, and the system can accurately identify the position change in the guide rod swing, laying the foundation for subsequent sliding bearing position identification and wear analysis. Based on the extracted sliding bearing position, the system can perform sliding simulation to obtain sliding data of the bearing, providing accurate data support for further analysis of the working state of the bearing. Through the analysis of the sliding data, abnormal conditions in the sliding process can be identified, and important clues for material surface oxidation can be provided. Oxidation analysis can reveal the degree of material surface oxidation and timely detect surface deterioration caused by wear or environmental factors, thereby preventing serious failures caused by material aging. In addition, based on the material surface oxidation data, the system further performs metal particle detection to generate metal particle data. This process helps to detect wear and contamination at the microscopic level, thereby more accurately assessing the wear of the sliding bearing. Finally, through the metal particle data, the system can judge the wear degree of the sliding bearing, providing timely information for maintenance personnel to prevent jamming or other mechanical failures caused by bearing damage, and ensure the efficient and stable operation of the electric cylinder system. Overall, this series of steps not only provides more comprehensive real-time monitoring and fault warning, but also realizes accurate analysis of the sliding bearing in the guide rod electric cylinder, greatly enhancing the intelligent level and fault diagnosis capability of the system, thereby improving the reliability and service life of the electric cylinder.

[0014] Preferably, step S26 is specifically: Step S261: identifying a high wear area based on the wear degree of the sliding bearing; Step S262: setting longitudinal magnetization for the high wear area, and setting the magnetization current range to 300A-2000A and the magnetization duration to 0.5s-3s to obtain longitudinal magnetization wear area data; Step S263: applying magnetic powder for a duration of 1s-5s according to the longitudinal magnetization wear area data to obtain magnetic powder application area data; Step S264: calculating the magnetic powder aggregation degree of the magnetic powder application area data; Step S265: identifying the magnetic powder aggregation area of the magnetic powder application area data according to the magnetic powder aggregation degree to obtain magnetic powder aggregation area data; Step S266: performing crack determination on the magnetic powder aggregation area data based on the preset crack magnetic powder data to generate crack data.

[0015] By identifying the high-wear area, the system can accurately locate the potential problem area, providing strong support for subsequent maintenance and repair. Then, by implementing longitudinal magnetization treatment on the high-wear area, appropriate magnetization current and duration are set, which helps to further reveal the microstructure changes of the wear area. This treatment can enhance the magnetic response of the wear area, providing more data support for subsequent magnetic powder application and crack detection. After applying the magnetic powder, the distribution of the magnetic powder in the wear area will reflect the degree of surface cracks or local damage. By calculating the aggregation degree of the magnetic powder, the potential damage area can be effectively evaluated, and the higher the aggregation degree, the more serious the damage. Next, based on the aggregation degree data, the system can identify the aggregation area of the magnetic powder, further confirming the location of the cracks. Finally, by comparing with the preset crack magnetic powder data, the existence of cracks can be accurately determined, and crack data can be generated to provide basis for subsequent maintenance decision. Overall, these steps enable the system to efficiently and real-time detect the minor damage of the sliding bearing and other key components, improve the accuracy of fault prediction, and reduce the risk of major equipment failure due to insufficient early warning, thereby greatly improving the safety, reliability and service life of the electric cylinder.

[0016] Preferably, step S3 is specifically: Step S31: evaluating the guide rod deflection degree according to the guide rod swing data; Step S32: collecting the ball screw image based on the guide rod deflection degree; Step S33: calculating the ball screw gap according to the ball screw image; Step S34: counting the vibration frequency based on the guide rod deflection degree, and extracting the guide rod low-frequency vibration data; Step S35: performing transmission simulation on the ball screw gap according to the guide rod low-frequency vibration data, and detecting the screw nut wear degree; Step S36: performing abnormal analysis on the ball screw gap according to the screw nut wear degree, to obtain ball screw gap abnormal data; Step S37: designing an automatic gap compensation structure according to the ball screw gap abnormal data.

[0017] The application provides key information of the guide rod running state by evaluating the guide rod deflection degree, can master the working performance of the guide rod in real time, and avoid system errors caused by excessive deflection. By collecting the ball screw image and calculating the screw gap, the system can accurately judge the wear state and wear degree of the screw, so as to find potential structural problems in advance and avoid positioning deviation and system accuracy decline caused by excessive screw gap. The vibration frequency is counted and the low-frequency vibration data is extracted, which further helps to detect abnormal conditions in the guide rod operation, and the low-frequency vibration characteristics can identify the slight deviation and failure earlier, so that repair measures can be taken in advance. Then, by driving simulation of the ball screw gap and detection of the wear degree of the screw nut, the system can not only detect the specific position of wear, but also evaluate the overall performance and service life of the system through simulation and analysis. The abnormal analysis process will help to identify the hidden danger of screw gap abnormality, further provide detailed data support, and effectively avoid efficiency decline and mechanical failure caused by excessive gap or wear. Finally, the automatic gap compensation structure is designed to ensure real-time automatic adjustment during operation, avoid the hysteresis of human intervention, and maintain high-efficiency and accurate operation of the system. Overall, these steps not only ensure the accuracy and reliability of the system, but also improve its intelligentization, dynamic response and self-adaptation ability, ensuring long-term stable operation of the electric cylinder system and effectively prolonging the service life of the equipment.

[0018] Preferably, step S37 specifically comprises: Step S371: designing a gap compensation strategy according to the ball screw gap abnormal data; Step S372: configuring an active gap adjustment structure based on the gap compensation strategy; Step S373: configuring a passive gap adjustment structure based on the gap compensation strategy; Step S374: integrating the active gap adjustment structure and the passive gap adjustment structure to obtain an automatic gap compensation structure.

[0019] The present application designs a targeted gap compensation strategy according to the gap abnormal data, so that the system can be accurately adjusted according to the actual working state, and the positioning error and work efficiency caused by the problem of screw gap are avoided. By configuring the active gap adjustment structure, the system can respond to the gap change in real time, and actively adjust when the deviation is found, to ensure the accuracy and consistency of the screw transmission process. Further, the passive gap adjustment structure can automatically adjust under the influence of system load change or environmental conditions, thereby providing auxiliary compensation without additional control signals, enhancing the adaptive ability of the system. Finally, the active and passive gap adjustment structures are integrated into an automatic gap compensation system, so that the entire drive system can be more flexible when facing different working conditions, realizing the unity of precise control and efficient operation. This integration measure effectively reduces the need for human intervention, improves the response speed and stability of the system, reduces wear and failure caused by abnormal gap, thereby greatly improving the overall reliability, service life and operating efficiency of the electric cylinder system.

[0020] Preferably, step S4 is specifically: Step S41: identifying a fatigue sensitive area according to the guide rod metal fatigue life; Step S42: detecting a trajectory influence factor based on the fatigue sensitive area; Step S43: designing a guide rod intelligent trajectory according to the trajectory influence factor, and generating the guide rod intelligent trajectory; Step S44: optimizing the electric cylinder initialization structure according to the automatic gap compensation structure, to obtain an electric cylinder optimized structure; Step S45: performing guide rod intelligent driving simulation according to the electric cylinder optimized structure and the guide rod intelligent trajectory, to generate guide rod intelligent driving data.

[0021] The present application can accurately locate the parts prone to fatigue damage by identifying the fatigue sensitive areas of the guide rod, thereby providing early warning of potential problems and taking appropriate optimization measures. Based on these fatigue sensitive areas, the trajectory impact factor is detected, enabling the system to evaluate the impact of different working paths on the service life of the guide rod, and then optimize the trajectory design. The designed intelligent trajectory of the guide rod can adaptively adjust the trajectory path, reduce the risk of fatigue, prolong the service life of the guide rod, and improve the operating efficiency of the system. Through integration with the automatic gap compensation structure, the optimized electric cylinder initialization structure is more accurate in adapting to different working conditions, ensuring the stability and long-term reliable operation of the electric cylinder. On this basis, the intelligent driving simulation of the guide rod can simulate various states in actual operation by adjusting parameters and optimizing path planning, ensuring that the guide rod can work stably under various load conditions, avoiding excessive wear or uneven load distribution caused by unreasonable path planning, and further improving the reliability, accuracy and adaptability of the system. Finally, this comprehensive optimization scheme forms a closed-loop feedback system, enabling the electric cylinder to automatically adapt to different working environments during use and ensuring efficient and stable operation of the equipment through intelligent control, reducing downtime and failures caused by fatigue, wear and other problems.

[0022] Preferably, the present specification also provides a swing guide rod electric cylinder intelligent driving system for performing the swing guide rod electric cylinder intelligent driving method as described above, which comprises: a guide rod swing simulation module for obtaining electric cylinder design data; performing structure initialization according to the electric cylinder design data to obtain an electric cylinder initialization structure; and performing guide rod swing simulation according to the electric cylinder initialization structure to obtain guide rod swing data; a guide rod metal fatigue life prediction module for performing guide rod swing jam analysis according to the guide rod swing data to obtain guide rod swing jam data; detecting the wear degree of the sliding bearing based on the guide rod swing jam data; performing magnetic powder crack detection based on the wear degree of the sliding bearing to generate crack data; and predicting the metal fatigue life of the guide rod based on the crack data; an automatic gap compensation structure design module for evaluating the guide rod deflection degree according to the guide rod swing data; performing ball screw gap anomaly analysis based on the guide rod deflection degree to obtain ball screw gap anomaly data; and designing an automatic gap compensation structure according to the ball screw gap anomaly data; a guide rod intelligent driving simulation module for performing guide rod intelligent trajectory planning according to the metal fatigue life of the guide rod to generate a guide rod intelligent trajectory; optimizing the electric cylinder initialization structure according to the automatic gap compensation structure to obtain an electric cylinder optimized structure; and performing guide rod intelligent driving simulation according to the electric cylinder optimized structure and the guide rod intelligent trajectory to generate guide rod intelligent driving data.

[0023] The swing guide rod electric cylinder intelligent driving system can realize the swing guide rod electric cylinder intelligent driving method, is used as the medium for the operation and signal transmission between various modules, and is used for completing the swing guide rod electric cylinder intelligent driving method. BRIEF DESCRIPTION OF DRAWINGS

[0024] Other features, objects, and advantages of the application will become more apparent from the following detailed description when read in connection with the following drawings: Fig. 1 A step flowchart diagram of the swing guide rod electric cylinder intelligent driving method of the application; Fig. 2 A detailed step flowchart diagram of step S1 in the application; Fig. 3 A detailed step flowchart diagram of step S16 in the application; The implementation, functional features and advantages of the application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0025] The technical method of the application will be described clearly and completely in combination with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0026] In addition, the accompanying drawings are only schematic diagrams of the application, and are not necessarily drawn to scale. The same reference signs in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some block diagrams shown in the drawings are functional entities, and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0027] It should be understood that although the terms "first", "second" and the like can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the example embodiments, a first element can be called a second element, and similarly a second element can be called a first element. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0028] To achieve the above-mentioned purpose, please refer to Figs. 1 to 3 The present application provides a swing guide rod electric cylinder intelligent driving method, the method comprising the following steps: Step S1: Obtain electric cylinder design data; structure initialization is performed according to the electric cylinder design data, and the electric cylinder initialization structure is obtained; guide rod swing simulation is performed according to the electric cylinder initialization structure, and guide rod swing data is obtained; In this embodiment, the design data of the electric cylinder is obtained through a measuring tool or CAD software, including the geometric parameters, dynamic parameters and mechanical properties (such as elastic modulus, tensile strength, etc.) of the materials used of the electric cylinder. These data include the length, diameter, driving motor power, load, etc. of the electric cylinder. After obtaining the design data, a three-dimensional geometric model of the electric cylinder is established using a special three-dimensional modeling software (such as SolidWorks). When initializing the structure according to the design data, the preliminary geometric model of the electric cylinder is simulated based on a mechanical analysis software (such as ANSYS or ABAQUS) to simulate the deformation, stress condition and motion characteristics of the electric cylinder under different working conditions. Through the preliminary simulation results, the initialization structure of the electric cylinder can be obtained, including the relative positions and sizes between the components. The guide rod swing simulation is performed using a dynamic simulation tool, and the working conditions such as the load, driving voltage and speed of the electric cylinder are input during the simulation process. The specific simulation parameters include the swing angle, speed, acceleration, etc. of the electric cylinder, and reasonable swing range and resistance parameters are set. Through these data, the swing behavior of the electric cylinder during operation is simulated, and the swing data of the guide rod is obtained, such as the swing angle, speed, etc. of the guide rod at different time points. These swing data provide a basis for subsequent stick analysis, wear detection, etc.

[0029] Step S2: Perform guide rod swing stick analysis according to the guide rod swing data, and obtain guide rod swing stick data; detect the wear degree of the sliding bearing based on the guide rod swing stick data; perform magnetic powder crack detection based on the wear degree of the sliding bearing, and generate crack data; predict the guide rod metal fatigue life based on the crack data; In this embodiment, the jamming analysis is performed by analyzing the changes in the swing angle and speed data, using signal processing methods (such as Fast Fourier Transform, time domain analysis, etc.) to identify the jamming phenomenon that occurs during the swing of the guide rod. In specific implementation, a threshold is set to detect abnormal stagnation phenomenon during the swing of the guide rod, for example, when the swing speed is lower than 0.1 rad / s within a certain time, it is considered that jamming occurs. The jamming data will contain information such as the time, duration, and position of the jamming, which serves as the basis for subsequent sliding bearing wear detection. According to the jamming data, the wear degree of the sliding bearing is detected. By comparing the sliding bearing movement data before and after the occurrence of the jamming phenomenon, it is analyzed whether the bearing has excessive friction or wear. Through material knowledge, the friction coefficient, contact surface hardness, and other parameters of the sliding bearing are used to calculate the wear degree. A sliding friction threshold (for example, a friction coefficient greater than 0.3 is considered as excessive wear) is set to determine the wear degree. On this basis, magnetic powder crack detection is performed. By setting a magnetic powder detection system, the surface of the sliding bearing is coated with magnetic powder using magnetization technology, and a magnetic field is applied through standardized test equipment to detect the crack position. The parameters used in this process include magnetization current and magnetization duration, specifically the magnetization current range is between 300A and 2000A, and the duration is set to 0.5s to 3s. The magnetic powder application area is determined according to the wear degree of the sliding bearing, and the crack data is obtained according to the detection results, recording the position, size, and other information of the crack. According to the crack data, a material fatigue analysis model is used to predict the fatigue life of the guide rod metal. The fatigue life prediction uses parameters such as the depth, length, and propagation speed of the crack, combined with material fatigue resistance data (such as S-N curve), to calculate the metal fatigue life through Fatigue life prediction models (such as Miner's rule), and obtain the remaining service life of the guide rod.

[0030] Step S3: evaluating the guide rod deflection degree according to the guide rod swing data; performing ball screw gap anomaly analysis based on the guide rod deflection degree to obtain ball screw gap anomaly data; and designing an automatic gap compensation structure according to the ball screw gap anomaly data; In this embodiment, the guide rod swing data is analyzed to evaluate the degree of deflection of the guide rod. The deflection degree is evaluated by measuring the deflection of the guide rod at multiple points, and the deviation from the ideal trajectory is calculated. In practice, a sensor (such as a displacement sensor or an optical sensor) is used to monitor the deflection behavior of the guide rod in real time. Through a data acquisition system (such as a NI data acquisition card), the deflection at different time points is recorded, and then the deflection angle and vibration frequency data are calculated. A deflection threshold (for example, a deflection angle greater than 0.5° is considered excessive deflection) is set as the basis for subsequent analysis. Based on the deflection degree data, the ball screw gap abnormality analysis is performed. By measuring the working state of the ball screw, including the movement speed of the nut, the gap change, etc., it is determined whether there is a gap abnormality. In practice, a high-precision displacement sensor or optical measurement equipment is used to monitor the movement process of the screw in real time. By calculating the rate of change of the gap, if the rate of change of the gap is greater than the set threshold (such as 0.02 mm / s), it is determined that there is a gap abnormality. The gap abnormality data will include the time, position and degree of abnormality. According to the gap abnormality data, an automatic gap compensation structure is designed. The design of the compensation structure needs to consider factors such as gap size, motion accuracy, etc., based on the gap change law, a compensation mechanism that can be automatically adjusted is designed. The automatic gap compensation structure can include active adjustment mechanisms (such as servo motor controlled adjustment devices) and passive compensation mechanisms (such as springs, buffers, etc.). In the design, it is necessary to combine with the dynamics simulation data to ensure that the compensation structure can realize effective gap compensation under different working conditions.

[0031] Step S4: According to the guide rod metal fatigue life, the guide rod intelligent trajectory planning is performed to generate the guide rod intelligent trajectory; according to the automatic gap compensation structure, the electric cylinder initialization structure is optimized to obtain the electric cylinder optimized structure; according to the electric cylinder optimized structure and the guide rod intelligent trajectory, the guide rod intelligent driving simulation is performed to generate the guide rod intelligent driving data.

[0032] In this embodiment, the key of trajectory planning is to consider the fatigue life, load characteristics and working environment of the guide rod, and to design a suitable working trajectory. In the implementation, a trajectory optimization algorithm based on fatigue life data (such as shortest path algorithm, genetic algorithm, etc.) is used to adjust the motion path of the guide rod. By inputting the fatigue life limit condition, it is ensured that the trajectory of the guide rod in the whole working process is not too rigid, thereby reducing the risk of metal fatigue. The generated guide rod intelligent trajectory data includes trajectory point position, speed and acceleration information at each time. According to the data of the automatic gap compensation structure, the electric cylinder is optimized in structure. The optimized structure design adjusts the position, shape, etc. of the internal parts of the electric cylinder, combined with the gap compensation mechanism, to ensure that the electric cylinder can maintain high precision and high reliability under different working environments. The optimized electric cylinder structure not only has higher working efficiency, but also can reduce the negative effects caused by abnormal gap. Combined with the optimized electric cylinder structure and intelligent trajectory data, the guide rod intelligent driving simulation is carried out. Through the simulation system, the optimized electric cylinder structure and intelligent trajectory data are input to perform dynamic simulation and obtain the driving response data of the electric cylinder in actual work. The driving simulation process uses a dedicated dynamics simulation software (such as MATLAB / Simulink) for simulation. In the simulation process, various working conditions and load changes are considered, and finally the guide rod intelligent driving data is generated, including driving force, torque, speed and other performance indicators.

[0033] Preferably, step S1 is specifically: Step S11: Obtain electric cylinder design data, and extract servo motor structure data and ball screw structure data; In this embodiment, complete electric cylinder system design data is obtained through technical drawings, design database or digital design file. The obtained electric cylinder design data should include guide rod maximum stroke, guide rod section size, electric cylinder working load, electric cylinder stroke speed and expected working frequency, etc. Basic parameters. For the servo motor part, the structure data of the servo motor is extracted, including motor rated power (unit: kW), rated speed (unit: rpm), rated torque (unit: Nm), rotor inertia (unit: kg·m²), motor installation method (such as flange installation size) and shaft diameter (unit: mm). For the ball screw structure part, the extracted structure data should include screw lead (unit: mm), pitch (unit: mm), nut diameter (unit: mm), screw effective stroke length (unit: mm), pre-press level (such as P1-P5 level), thread angle (unit: °) and axial stiffness (unit: N / μm). All data are obtained and verified by standard technical measurement equipment (such as laser length measuring instrument, three-coordinate measuring machine, etc.), and the structure compliance and integrity are verified by national standards (such as GB / T 17587.3-1998).

[0034] Step S12: Identify the screw drive structure according to the servo motor structure data; In this embodiment, according to the servo motor parameters extracted in S11, the screw drive structure matching the output characteristics is determined. The identification process is based on the rated speed of the servo motor and the required guide rod linear movement speed, and the following formula is used for conversion: guide rod speed V (mm / s) = lead P (mm) x motor speed n (rpm) / 60. According to this formula, the required lead of the screw is calculated by inverse calculation. If the required guide rod speed is 300mm / s and the motor speed is 3000rpm, the required lead is 6mm. On this basis, the screw model that meets the lead, diameter and pitch is selected from the standard ball screw series, combined with the rated load demand and the output torque of the servo motor, to ensure that the rated static load of the screw is higher than the maximum working load of the electric cylinder (for example, the maximum load of the electric cylinder is 5kN, then the rated static load of the selected screw needs to be ≥5.5kN). Finally, the motor-screw integrated drive structure is formed, and the structure recognition is completed.

[0035] Step S13: Construct a guide rail support structure model according to the screw drive structure; In this embodiment, the construction of the guide rail support structure takes the screw drive structure as the input basis, and mechanical modeling is performed according to the installation position of the screw, the support method and the load demand of the slider. In the modeling process, first, the effective length of the guide rail is determined according to the length of the screw and the nut stroke (for example, the nut stroke is 400mm, and ≥450mm guide rail length is required to meet the boundary reservation). The guide rail selection is in accordance with the standard of rolling linear guide rail (such as HG / T 21556-95), and the number of sliders, guide rail width and pre-tightening grade are selected. If the maximum deflection of the guide rod cannot exceed 0.05mm, high-rigidity double-slider structure needs to be selected, and the single-slider rated load should not be less than 2.5kN. The support structure should include linear guide rail, support base, limit end cover, oil return channel and dustproof sealing strip and other components. The support structure model is established through a three-dimensional modeling platform, and the assembly relationship, installation tolerance and shape and position tolerance requirements of all structural parts are marked to ensure that the guide rod has high stability and anti-vibration ability when supported.

[0036] Step S14: Analyze the screw drive parameters according to the ball screw structure data; In this embodiment, the analysis of the screw transmission parameters needs to be combined with the structural data of the ball screw and the principle of dynamic calculation. Specifically, it includes lead (P), screw diameter (d), thread angle (a), screw efficiency (η), lead angle (λ), load distribution coefficient (z), etc. The lead P has been extracted in S11, the thread angle is calculated by the formula tan(a) = P / (π·d), for example, P = 6mm, d = 20mm, then a ≈ 5.45°. The transmission efficiency η depends on the thread angle and the friction coefficient μ (the value range is 0.01-0.03), and is calculated using η = tan(λ) / [tan(λ) + μ]. The lead angle λ can be obtained by λ = arctan(P / (π·d)). Under the rated load, the load distribution in the ball pair is analyzed, and the load distribution coefficient z is set (generally 1.2-1.5) for subsequent load simulation and torque analysis. All the screw parameters will be used as input to accurately describe the motion characteristics and power transmission efficiency of the ball screw.

[0037] Step S15: input the screw transmission parameters into the guide rail support structure model, and perform electric cylinder assembly coordination matching to obtain electric cylinder assembly coordination matching data; In this embodiment, all the screw transmission parameters calculated in S14 are imported into the guide rail support structure model, and the size coordination and component docking analysis of the mechanical structure are performed. During the matching process, the relative layout and connection interface between components are adjusted according to the ball screw transmission path, guide rail support position and motor axis position. The coordination matching process must consider the lead error (≤0.02mm / 300mm), radial runout (≤0.01mm), axial concentricity (≤0.03mm) and other key indicators to ensure that the screw rotation axis and the guide rail slider sliding track remain coaxial. Using the tolerance analysis method, the relative offset and assembly interference between the servo motor end cover, the screw support seat and the nut connecting plate are evaluated. According to the standard limit deviation value recommended by the national mechanical design manual (such as H7 / k6 fit), the size adjustment and selection are performed. All the matching data (including fit clearance, connection bolt specification, coupling type, slider quantity, etc.) are uniformly output as electric cylinder assembly coordination matching data, which are used as the basic information for subsequent virtual assembly.

[0038] Step S16: virtual assembly according to the electric cylinder assembly coordination matching data to obtain the electric cylinder initialization structure; In this embodiment, the coordination matching data obtained in S15 is used to complete the complete virtual assembly operation of the electric cylinder system. The virtual assembly includes components such as lead screws, nuts, guide rails, sliders, servo motors, couplings, end covers, bearings, and support seats. Each component is inserted into the system model in the order of assembly in the mechanical drawing, and a reasonable fitting relationship is set, for example, a sliding fit between the slider and the guide rail, and a screw pair modeling between the lead screw and the nut. The axis coincidence degree is set to not more than 0.01 mm during assembly, and constraint conditions are used to prevent component interference, such as collision between the nut and the support frame when the nut slides. The assembly tolerance value is set, and axial and radial pre-tightening forces are applied to the connections between components to ensure that the system has no interference deformation or assembly interference in the initial state. The final virtual assembly model is a complete electric cylinder initialization structure, which includes the accurate geometry, structural position, and connection method of all core components.

[0039] Step S17: Simulate the guide rod swing according to the electric cylinder initialization structure to obtain guide rod swing data.

[0040] In this embodiment, the simulation uses a multi-body dynamics simulation method, sets input parameters such as electric cylinder working frequency (e.g., 5 Hz), guide rod stroke (e.g., 200 mm), swing angle range (e.g., ±15°), and load mass (e.g., 50 kg), and constructs the actual working condition environment of the guide rod movement. The motor input torque (e.g., 2 Nm) is applied, and the lead screw lead and transmission efficiency are combined to calculate the speed, acceleration, and torque of the guide rod at different time points. A flexible constraint model of the guide rod connecting components is established to simulate the slight deviation or swing amplitude change of the guide rod in actual operation due to flexible connection. The sampling frequency is set to 1000 Hz during simulation, and the instantaneous swing angle, center deviation value, speed, and acceleration data of the guide rod are recorded every 0.1 seconds within the full stroke range. The final output is a complete guide rod swing data file, which is used as the basis input for subsequent steps such as stick-slip analysis and life prediction.

[0041] Preferably, step S16 specifically includes: Step S161: Identify the component interface according to the electric cylinder component coordination matching data; In this embodiment, the shaft hole interface between the servo motor output shaft and the input hole of the coupling is identified, the thread or key connection interface between the output hole of the coupling and the shaft end of the screw rod is identified, the flange or screw connection interface between the outer wall of the nut and the mounting hole of the slider is identified, and the reference surface and hole position matching relationship between the guide rail base and the support structure is identified. The interface identification adopts a method based on three-dimensional CAD geometric boundary feature identification, extracts geometric data such as assembly surface normal vector, hole center coordinates, and assembly reference axis direction of each component, and matches hole shaft matching type, thread standard, tolerance grade, and assembly interference amount according to national mechanical assembly standards (such as GB / T 1182-2008). Taking the servo motor shaft end as an example, if its diameter is φ14 mm and the tolerance grade is h6, the corresponding shaft hole of the coupling needs to be φ14H7, and when identifying such an interface, the assembly surface number, geometric center coordinates (such as X=35mm, Y=0mm, Z=82mm), and matching type (such as tight fit H7 / h6) should be accurately recorded. All interface information needs to be numbered and output in table form as the geometric basis for subsequent alignment and assembly constraint definition.

[0042] Step S162: Perform electric cylinder geometric structure alignment processing based on the component interface to obtain electric cylinder assembly reference data; In this embodiment, the geometric registration operation in the global coordinate system needs to be performed according to the assembly interfaces identified in step S161. This operation should follow the "master-slave alignment principle", that is, taking the electric cylinder base or guide rail support structure as the static reference body, and aligning other components (such as servo motor, ball screw assembly, slider and nut) according to the identified interface. When aligning, a combination of rotation matrix and translation vector is used to realize six-degree-of-freedom adjustment. The specific operation includes: first, align the Z-axis direction of two components through the normal vector of the identified reference surface, and then calculate the translation vector Δx, Δy, Δz through the coaxial hole center coordinates to adjust the position. Taking the alignment of the screw rod and the guide rail slider as an example, if the normal of the guide rail reference surface is (0, 0, 1) and the normal of the screw nut mounting surface is (0.01, 0, 0.9998), the rotation matrix needs to be calculated to fine-tune the screw rod coordinate system to the same direction as the guide rail, and then the installation hole center deviation Δz=0.15mm is used for accurate alignment. All alignment parameters (rotation angle, translation amount, reference surface number, etc.) are output in the form of a three-dimensional coordinate matrix, which is summarized to form the electric cylinder assembly reference data for assembly constraint definition.

[0043] Step S163: Set the assembly constraint relationship according to the electric cylinder assembly reference data; In this embodiment, when setting the assembly constraint relationship, the assembly connection definition should be strictly in accordance with the geometric alignment results and the component function logic. The assembly constraint relationship is divided into three categories: face-face coincidence constraint, shaft-hole coaxial constraint, and plane distance constraint. The specific operation is as follows: the end face of the servo motor output shaft and the input end face of the coupling are set as face-face coincidence constraint, and the tolerance is set as ±0.01mm; the output shaft of the coupling and the shaft end of the ball screw are set as shaft-hole coaxial constraint, and the cooperation level is H7 / h6; the flange face of the screw nut and the mounting face of the slider are set as plane distance constraint, and the net distance is kept as 0mm, that is, the installation is in close contact. All constraint relationships should be set with unique identifiers, and detailed parameters such as constraint direction vector, starting coordinate, end coordinate, and tolerance limit value should be recorded. During the constraint setting process, there should be no degree of freedom redundancy or excessive constraint, so the assembly motion degree of freedom analysis should be carried out to ensure that each subcomponent has only the expected degree of freedom in the virtual space (for example, the guide rod moves linearly along the Z axis). After all the constraint definitions are completed, a standard format constraint table is output, including component number, constraint type, constraint direction, cooperation tolerance, error correction value, etc., which is the assembly executable instruction set.

[0044] Step S164: Virtual assembly according to the assembly constraint relationship to obtain the initialization structure of the electric cylinder.

[0045] In this embodiment, the assembly process should be carried out on the CAD / CAE platform, and the servo motor, coupling, ball screw, guide rail, slider, support seat and other structural parts should be inserted in sequence according to the component number and constraint order in the three-dimensional environment. The assembly system will automatically align the components and apply the cooperation positioning relationship according to the constraint instructions. If there is cooperation interference (such as the outer diameter of the screw is greater than the inner diameter of the bearing), the system will stop the assembly and output the interference alarm information. During the assembly verification process, the coaxiality of the key transmission path (allowable error ≤0.01mm), the motion degree of freedom of the slider (only Z-axis direction movement is allowed), and the consistency of the motor output and the screw rotation direction (angle error <0.5°) should be checked. In addition, the initial position of the guide rod is calibrated, and the guide rod zero point is set at the midpoint of the screw stroke, which is convenient for subsequent motion control simulation. The finally output electric cylinder initialization structure should contain the three-dimensional space position, assembly direction, and cooperation tolerance information of all parts, and the assembly result should be saved in the form of three-dimensional assembly drawing (such as STEP or IGES format) and assembly list for subsequent guide rod swing simulation and analysis.

[0046] Preferably, step S2 is specifically: Step S21: According to the guide rod swing data, the guide rod swing speed is calculated; In this embodiment, the angular velocity data output by the three-axis accelerometer and angle sensor (such as model ADIS16470) arranged on the guide rod is collected by a high-speed data acquisition card, and the sampling frequency is set to 1000 Hz. The collected angular velocity data is filtered, a five-order Butterworth low-pass filter is used, and the cutoff frequency is set to 20 Hz to exclude high-frequency interference signals. Subsequently, the angular velocity is converted into angular displacement using a numerical integration method (such as the composite trapezoidal method), and the velocity is calculated in combination with the time series data, with the specific calculation formula being: v(t) = (θ(t) - θ(t-Δt)) / Δt, where Δt is 0.001 s.

[0047] Step S22: Calculate the instantaneous swing deceleration time period of the base guide rod swing speed; In this embodiment, the velocity sequence is subjected to first-order differentiation to obtain the acceleration change trend. The threshold method is used to screen out the time point where the absolute value of acceleration is greater than 200° / s² and the sign changes from positive to negative as the deceleration starting point; when the acceleration drops below -100° / s², it is considered as the deceleration end point, defined as the instantaneous deceleration time period, and its start and end times are recorded and marked in the swing sequence.

[0048] Step S23: Detect the amplitude stagnation sudden change according to the instantaneous swing deceleration time period to obtain the amplitude stagnation data; In this embodiment, the data segment of the last 200 ms in each deceleration time period is extracted, the slope change rate of the angle change is calculated, and if the slope changes by less than 1° / s within 100 ms and the duration exceeds 150 ms, the segment is defined as "amplitude stagnation" and is extracted as the stagnation data and information such as angle value, corresponding time stamp, and acceleration trend before and after, forming a stagnation data packet.

[0049] Step S24: Determine the guide rod swing jam based on the amplitude stagnation data to obtain the guide rod swing jam data; In this embodiment, the guide rod jam behavior is analyzed in combination with the stagnation data. If the same guide rod continuously appears stagnation data within 3 cycles, and the angle fluctuation is less than 2°, it is considered to have structural jam. The maximum swing speed, angle change, acceleration change, and other information in the corresponding time period are extracted to constitute the guide rod swing jam data, and the jam position area and time window are identified for subsequent structural wear analysis.

[0050] Step S25: Detect the sliding bearing wear degree based on the guide rod swing jam data; In this embodiment, the energy dissipation trend is determined by measuring the acceleration drop rate before and after the period of jamming, and if the energy drop rate within the swing period exceeds 30% and the jamming position is concentrated in the bearing support section, it is determined that the sliding bearing is worn. The laser displacement sensor (resolution of 1 pm) is used to measure the gap between the bearing hole and the guide rod, and if the gap is greater than 0.2 mm (the manufacturing standard is 0.05 mm), it is defined as serious wear, and the wear site coordinates, gap value and wear grade are recorded.

[0051] Step S26: Perform magnetic powder crack detection based on the wear degree of the sliding bearing to generate crack data; In this embodiment, a magnetic particle detector (model Y-6 portable magnetic powder detector) is used to perform wet fluorescent magnetic powder detection on the suspicious area of the guide rod, the concentration of fluorescent magnetic powder liquid is controlled at 0.15 g / mL, an alternating current field of 0.6 A is applied, and the crack distribution is observed. After taking the image, image enhancement processing (using edge detection operators such as Canny) is performed to extract the crack length and density. If the crack length exceeds 3 mm or the number of cracks exceeds 5, it is determined that there is a fatigue crack.

[0052] Step S27: Predict the metal fatigue life of the guide rod based on the crack data.

[0053] In this embodiment, the Miner linear cumulative damage method is used for life assessment. The crack growth rate da / dN is calculated, and the Paris formula da / dN = C·(ΔK)^m is used, where C = 2.5 x 10^-12, m = 3, and ΔK is the stress intensity factor. The stress condition of the guide rod is extracted using the finite element method, and the crack length a and the fracture toughness K_IC = 30 MPa·m^0.5 of the guide rod material are combined to estimate the remaining life. The fatigue cycle number N_f is calculated, and combined with the actual working load frequency to convert to time, completing the life prediction.

[0054] Preferably, step S25 specifically comprises: Step S251: Extract swing position offset information from the guide rod swing jamming data; In this embodiment, the guide rod swing sticking data contains time series swing angle, angular velocity and angular acceleration parameters. First, the swing angle change curve is differentiated by one order to extract the swing instantaneous speed. The set stable running baseline (linear swing within ±0.2°) is used as a reference to identify the swing deviation trend in the sticking time period. The guide rod center position in the normal running stage is used as the reference position (with the guide rail center point coordinates as zero point). If the swing position deviation is detected to be more than ±0.8mm, it is recorded as a deviation event. The deviation information is obtained by joint measurement of the three-axis accelerometer and the laser displacement sensor. The sampling frequency of the three-axis accelerometer is 5kHz, and the accuracy error is not more than ±0.5%. The displacement sensor uses optical level 0.01mm precision level. In the sticking period, the swing position with continuous deviation more than 50ms is extracted and the deviation direction and coordinates are recorded for subsequent relative position analysis of the sliding bearing.

[0055] Step S252: extracting the sliding bearing position based on the swing position deviation information; In this embodiment, based on the swing position deviation information obtained above, the relative position of the sliding bearing is inversely solved by using the space back projection method in combination with the guide rod structure layout diagram and the sliding bearing layout parameters (including the guide rod installation length, bearing spacing and bearing center position). The CAD import method is used to read the electric cylinder structure assembly drawing, and the offset point coordinates are intersected with the guide rod reference line in the 3D geometric coordinate system to calculate the rotation axis of the guide rod in the offset section. According to the 0.5mm gap between the guide rod outer diameter (set to φ32mm) and the sliding bearing inner diameter (φ33mm), a sliding space envelope model is established. The ring domain region intersected with the rotation axis is identified by using the Boolean operation method, and the region is defined as the actual bearing load point of the sliding bearing. The bearing position coordinates are output and compared with the design initial coordinates to obtain the bearing deviation data for the next step of simulation analysis.

[0056] Step S253: performing sliding simulation according to the sliding bearing position to obtain bearing sliding data; In this embodiment, according to the identified actual position of the sliding bearing, a multi-body dynamics simulation platform (such as RecurDyn or ADAMS) is used to construct a sliding contact interface model. The actual running track of the guide rod and the bearing offset position are imported, and the material attribute parameters are set: the sliding bearing adopts bronze alloy (elastic modulus E = 1.1 x 1011 Pa, Poisson's ratio 0.33), and the guide rod adopts 45 steel (elastic modulus E = 2.0 x 1011 Pa). The sliding friction factor μ = 0.12 is set, and the lubrication state adopts the quasi-dry friction mode. The guide rod movement speed input is based on the swing data statistics, and is set to 0.3~1.2m / s variable speed cycle. The simulation time is set to 10s, and the bearing contact pressure, relative sliding speed and contact area are recorded every 0.001s. The output bearing sliding data includes total contact energy consumption, sliding path length per unit time, bearing inner surface stress frequency and contact surface temperature rise data. If the temperature rise exceeds 85°C, it is determined to be a mild lubrication failure area, which provides input basis for material surface layer analysis.

[0057] Step S254: material surface layer oxidation analysis based on bearing sliding data, to obtain material surface layer oxidation data; In this embodiment, according to the temperature rise data and contact area change information output in the bearing sliding data, the material surface layer oxidation analysis of the sliding area is carried out. First, the continuous friction time period of the area where the temperature rise exceeds the critical temperature 85°C is extracted, and the total friction energy density (cumulative friction energy per unit area, unit J / mm²) is calculated. Set the oxidation starting energy threshold to 15J / mm², and the area exceeding the threshold enters the oxidation analysis process. The failure area is detected by infrared thermal imaging combined with energy spectrum analysis (EDS). The thermal imager has a resolution of 0.05°C, which can identify the area change of local hot spots, and the energy spectrum detection can analyze the proportion of Fe2O3 and CuO oxides. The oxidation degree of the material surface layer is based on the thickness of the oxidation layer, and less than 1μm is considered to be mild oxidation, 1~3μm is moderate oxidation, and more than 3μm is considered to be a severe oxidation area. The oxidation area, distribution position and oxidation layer thickness are recorded, and the output is the material surface layer oxidation data, which is used for subsequent metal particle judgment.

[0058] Step S255: metal particle detection according to material surface layer oxidation data, to obtain metal particle data; In this embodiment, after the extraction of the surface layer oxidation data is completed, the areas with moderate or severe oxidation are determined as the target areas for metal particle detection. After the areas are located by the thermal imaging map and the oxidation thickness data, a focused ion beam (FIB) instrument is used for precise sampling. The sample cutting depth is controlled within 50 pm below the oxidation layer to avoid surface contamination interfering with the real wear particle distribution. The sample is pretreated in a clean environment, including ethanol ultrasonic cleaning for 60 seconds and vacuum drying treatment, to ensure the surface cleanliness for electron microscopic analysis. Then, a scanning electron microscope (SEM) is used for microstructure observation, with an electron beam acceleration voltage set to 15 kV and a resolution of 3 nm. The single sample is divided into 10 equal-area detection units by the field partition method, with a fixed area of 500 pm x 500 pm for each unit. Images are collected in each area, and metal particle data is extracted. The particles are separated from the background by image processing algorithms (such as the Otsu threshold-based binary segmentation method), the edges are extracted, and the actual area of each particle is calculated to convert it into an equivalent particle size. All extracted particles are divided into four grades according to the particle size distribution: <10 pm, 10-30 pm, 30-50 pm, and >50 pm, and the number density (pieces / mm²) of each grade of particles in unit area is counted. Area weighted average is used in the statistical process to reduce the interference of abnormal values of single large particles. After the particle number density statistics are completed, the samples in the same detection area are sent to an X-ray diffractometer (XRD) for phase composition analysis. XRD uses a Cu-Ka radiation source, with a scan angle range of 10°-90°, a step size of 0.02°, and a scan rate of 2° / min. The diffraction spectrum is peak-fitted and matched with the database to identify whether the sample contains Cu, Pb, Sn, and other sliding bearing alloy matrix derivative phases, and whether there are Fe3C, Fe-Cu composite particles, and other inclusions representing severe wear. The content of each phase is estimated by peak area integration, with a proportional error of ±5%. When the total particle density exceeds 1500 pieces / mm², and the number of particles with a particle size of 30 pm or more accounts for more than 15% of the total number of particles, the area is determined as a "high-intensity wear characteristic area" according to the set metal debris distribution intensity standard. The final output metal particle data includes the number density statistical value of each particle size segment, the maximum particle size, the particle density distribution graph, the main phase composition and proportion list, and the matching result with the wear material type. All data are used as reference input for the next step of wear degree evaluation and fatigue life prediction.

[0059] Step S256: Determine the wear degree of the sliding bearing according to the metal particle data.

[0060] In this embodiment, according to the metal particle data, the wear degree of the sliding bearing is evaluated by using the wear composite index WCI (Wear Composite Index). The calculation formula of WCI is: WCI = (p x D x F) / A, wherein p is the particle density (pieces / mm2), D is the average particle size (μm), F is the wear material proportion factor (1.0 for copper-based material, and 1.2 for iron-containing particles), and A is the detection area (mm2). The WCI result is compared with the experimental standard library: WCI < 100 is mild wear, 100 ≤ WCI < 300 is moderate wear, and WCI ≥ 300 is severe wear. The wear distribution trend and severity are confirmed by combining the types of metal particles, distribution area, and consistency with the sliding direction in the detection. Finally, the sliding bearing wear grade data and position coordinate information are output, which are used for input parameter setting of metal fatigue evaluation.

[0061] Preferably, step S26 is specifically: Step S261: identifying a high wear area based on the wear degree of the sliding bearing; In this embodiment, according to the sliding bearing wear degree data determined in the previous steps, the metal particle density distribution map and particle size distribution information are read, the area where the number of particles per unit area exceeds 1500 pieces / mm2 and the proportion of particles with particle size greater than 30 μm exceeds 15% is extracted, and the area is determined as a high wear area. In the implementation process, two-dimensional distribution mapping technology is used to correspond the wear data with the bearing physical coordinates to delimit the specific position coordinates of the high wear area. The arc position (unit: °) of the high wear area on the surface of the bearing ring and the length (unit: mm) in the width direction need to be recorded to form the target area boundary data for subsequent magnetization. When delimiting the area, a microscopic image recognition tool is used to extract the contour, a closed boundary area is constructed based on the gray contrast and particle cluster density, and the high wear area recognition result is output.

[0062] Step S262: setting longitudinal magnetization for the high wear area, and setting the magnetization current range to 300A-2000A and the magnetization duration to 0.5s-3s to obtain longitudinal magnetization wear area data; In this embodiment, for the identified high wear area, the magnetization direction of the longitudinal magnetization device is set to be parallel to the axial direction of the sliding bearing, the magnetization current is set by using an adjustable constant current magnetization power supply, the initial setting value is 300A, and the magnetization current is gradually adjusted to 2000A with a step of 100A to ensure that the saturation magnetization interval of different permeability materials is covered. The magnetization duration is set by the magnetization controller to be 0.5 seconds to 3 seconds, the magnetic flux distribution stability is detected by a Hall voltage sensor after each magnetization to ensure that the magnetic flux density is between 1.5T and 2.2T, which meets the response interval of the surface micro-cracks of the bearing. After each magnetization is completed, the magnetization current, duration and magnetic flux density are recorded, and the "longitudinal magnetization wear area data" is established for synchronous control of the magnetic powder application.

[0063] Step S263: Apply magnetic powder for 1-5 seconds according to the longitudinal magnetization wear area data to obtain magnetic powder application area data. In this embodiment, the magnetic powder application process uses standard A type black magnetic powder, with a particle size range of 40-80 pm. The magnetic powder application device sprays the magnetic powder uniformly onto the aforementioned magnetized area through a pneumatic nozzle. The spraying time is set to 1-5 seconds by a timing controller, and the control air pressure is 0.2-0.3 MPa to ensure complete coverage of the magnetic powder on the surface. A high-resolution camera is used to record the initial distribution characteristics of the magnetic powder under the action of the magnetic field during the spraying process, forming "magnetic powder application area data". This data uses the RGB image gray scale layer as a reference for subsequent magnetic powder aggregation degree calculation.

[0064] Step S264: Calculate the magnetic powder aggregation degree based on the magnetic powder application area data. In this embodiment, the magnetic powder application area image data is read, and the image gray scale distribution histogram method is used to extract the pixel area with a gray scale higher than 200 in the image, and the cumulative sum of the gray scale value per unit area (mm2) is calculated, which is defined as the magnetic powder aggregation degree. The aggregation degree analysis uses the threshold and contour algorithms in the OpenCV image processing library, with a gray scale threshold of 220 and an area threshold of 0.3 mm2. The image is scanned pixel by pixel, and the total number and distribution position of the pixels that meet the aggregation conditions are counted to establish a magnetic powder aggregation degree coordinate distribution map, which is used to identify the powder enrichment phenomenon caused by cracks.

[0065] Step S265: Identify the magnetic powder aggregation area based on the magnetic powder aggregation degree, and obtain the magnetic powder aggregation area data. In this embodiment, according to the magnetic powder aggregation degree coordinate distribution map, the magnetic powder concentration mutation area is identified, i.e. the area with an aggregation density more than 1.8 times the average of the surrounding area and a continuous area greater than 2 mm2. The region growing method is used to extract the boundary of the above-mentioned high aggregation degree area, with a minimum continuous boundary growth length of 1.5 mm, and the boundary shape characteristics (including opening width, length, corner, etc.) are output. The above-mentioned area is defined as the magnetic powder aggregation area, and its position, size and aggregation density are recorded to form "magnetic powder aggregation area data".

[0066] Step S266: Based on the preset crack magnetic powder data, the crack data is generated by determining the magnetic powder aggregation area data.

[0067] In this embodiment, on the basis of the magnetic powder aggregation area data, a preset crack magnetic powder data standard is called to perform crack determination. The standard includes three types of magnetic powder distribution form characteristics such as typical fatigue cracks, spalling cracks and surface cracking, such as an opening angle less than 30°, a length greater than 3 mm, and an aggregation density higher than 3000 pixels / mm². The crack determination adopts a neural network image recognition model to perform crack form matching, and outputs a crack type label and a position coordinate. Meanwhile, the crack grade is quantified (1-5) in combination with the magnetic powder aggregation intensity and geometric continuity. Finally, crack data is generated, including crack number, type, position, size and grade information, which is used for subsequent deduction of the guide rod fatigue life.

[0068] Preferably, step S3 is specifically: Step S31: evaluating the guide rod deflection degree according to the guide rod swing data; In this embodiment, the time domain analysis and frequency domain analysis joint evaluation method is adopted for the obtained guide rod swing data. First, the maximum deviation angle value of the guide rod swing angle in each driving cycle is extracted, denoted as θ_max, and the unit is degree. Then, the standard cycle reference angle θ_ref is calculated, which is obtained through ideal working condition guide rod motion trajectory simulation, and generally within ±0.3° is taken as the normal deflection interval. The deflection deviation value is calculated as θ_dev = |θ_max - θ_ref|, and the deflection degree grading threshold is set: θ_dev<0.5° for slight deflection, 0.5°≤θ_dev<1.0° for moderate deflection, and θ_dev ≥1.0° for severe deflection. Finally, the guide rod deflection degree data is output, including the deflection amplitude (angle value), direction (clockwise / counter-clockwise) and deflection grade label.

[0069] Step S32: collecting the ball screw image based on the guide rod deflection degree; In this embodiment, according to the deflection grade determined in step S31, the image acquisition frequency and resolution parameters are adjusted. The frame rate is set to 10 fps in the slight deflection area, and is increased to 30 fps in the moderate and severe deflection areas; the image resolution is set to at least 2048×1536 pixels. An industrial camera (such as Basler acA2500-60gc) is used to track and shoot the ball screw in the guide rod running cycle, and the multi-angle lighting (side light + top light) is used to improve the contrast of the screw and the nut profile. The collected data includes the screw helical tooth groove edge, the nut edge profile and the cross-sectional structure image at every 20 mm position along the axial direction.

[0070] Step S33: calculating the ball screw gap according to the ball screw image; In this embodiment, the sub-pixel edge detection algorithm is used to process the image collected in step S32, and the Canny operator is used to extract the boundary line of the contact area between the screw rod helical tooth and the nut. Then, the image coordinates are converted into actual space coordinates through the image calibration parameters (lens focal length, imaging size, distortion correction matrix). The minimum distance between the helical groove tooth top and the nut edge is used to calculate the radial clearance δ_r, and the tooth groove deviation of the screw rod at two adjacent axial positions is used to calculate the axial clearance δ_a. The clearance abnormality prediction threshold is set as δ_r>0.06mm or δ_a>0.08mm, and the ball screw clearance data including the maximum value, the average value and the offset direction are output.

[0071] Step S34: Statistics vibration frequency based on guide rod deflection degree, and extract guide rod low-frequency vibration data; In this embodiment, the guide rod swing time series data is subjected to fast Fourier transform (FFT), and the frequency range is set to 0.1Hz to 100Hz. The part with a frequency less than 10Hz in the frequency spectrum is extracted and defined as a low-frequency band, which is common in loose clearance or unstable cooperation working conditions. The maximum peak frequency f_max_lf (Hz) and the corresponding amplitude A_max_lf (rad or ° / s) are calculated, the low-frequency vibration energy is integrated as a low-frequency vibration energy index E_lf (unit: rad²·Hz), and the low-frequency vibration energy index E_lf is output as a vibration trend index in combination with the guide rod deflection level. The index provides a basic vibration evidence for subsequent judgment of ball screw transmission abnormalities.

[0072] Step S35: Transmission simulation of ball screw clearance according to guide rod low-frequency vibration data of guide rod, and detection of screw nut wear degree; In this embodiment, δ_r and δ_a obtained in step S33 and E_lf in step S34 are input into a two-dimensional dynamic contact simulation model to establish a dynamic cooperation structure between the ball screw and the nut. The simulation load is set to the maximum driving torque 50Nm, the working frequency 5Hz, and the stroke 200mm. The contact stress distribution and relative slip area of the screw rod in one working cycle are obtained through finite element contact simulation (explicit integration method can be used), and the working conditions in which the contact area in the nut and screw contact area decreases by more than 25% and the stress concentration area extends by more than 3mm are identified. At the same time, the maximum shear stress value τ_max (unit: MPa) per unit area is calculated. If τ_max exceeds 60% of the material yield strength, it is preliminarily determined that the nut wear trend is obvious, and the nut wear index value is output.

[0073] Step S36: Abnormal analysis of ball screw clearance according to screw nut wear degree, and obtaining ball screw clearance abnormal data; In this embodiment, the wear indicator output in step S35 is compared with the gap data in step S33. If both δ_r and δ_a exceed the threshold value, and the nut wear indicator (contact area reduction rate, τ_max value) is in the high wear grade, it is determined that the gap at this position is abnormal. The axial position (z coordinate) where the gap anomaly occurs is used as an index to generate a ball screw gap anomaly data set, and the data format includes: abnormal position z_i (mm), abnormal type (radial / axial), δ value, wear grade, crack risk coefficient R_cr (calculated according to the ratio of the fatigue limit of the nut material to the contact stress). All abnormal sections are marked in the ball screw spatial position map for subsequent structure correction.

[0074] Step S37: Design an automatic gap compensation structure according to the ball screw gap anomaly data.

[0075] In this embodiment, based on the gap anomaly sections marked in step S36, an automatic gap compensation mechanism with axial elastic compensation and radial adjustment function is designed. The specific design includes: setting a double nut structure, wherein the first nut is fixedly connected to the screw, and the second nut is connected to the guide rail housing through a disc spring set, pre-pressed in the radial direction by 0.1-0.3mm, realizing automatic follow-up compensation; in the axial direction, the adjustment stroke range is set to ±0.2mm, and the adjustment force is applied by a magnetostrictive actuator, with a response time controlled within 20ms. The compensation mechanism material is selected from titanium alloy or high-strength engineering plastic with elastic modulus E>100GPa and wear resistance grade WSD<0.05, and a dynamic response strategy is set according to the guide rod swing frequency data, with force-displacement matching according to frequency segments. The final output is structure drawings and compensation force checking data for electric cylinder structure optimization.

[0076] Preferably, step S37 specifically comprises: Step S371: Design a gap compensation strategy according to the ball screw gap anomaly data; In this embodiment, after detecting the ball screw gap anomaly data, the gap value change range, fluctuation period and corresponding load frequency are extracted. The gap value change amplitude is accurately quantified in microns, and the mapping relationship between the maximum and minimum values of the gap and the number of working cycles is recorded. If the gap change exceeds 120% of the initial design gap value, it is determined that compensation is needed. When designing the compensation strategy, based on the load-gap relationship map, a minimum compensation force model is established, and a dynamic hysteresis control curve is adopted to limit the response delay time to not more than 50ms. The compensation strategy includes three types of control modes: elastic loading pre-tightening control, slider tension adjustment and axial offset correction, and the pitch, lead and ball diameter of the ball screw (for example, the pitch is 5mm and the ball diameter is 3mm) are used as constraint conditions to set the displacement compensation value to not less than 30μm, in order to prevent low-speed crawling and increase the positioning error.

[0077] Step S372: configure the active gap adjustment structure based on the gap compensation strategy; In this embodiment, according to the gap compensation strategy described above, the active gap adjustment structure is composed of a piezoelectric driving unit and a micro roller slide table. The structure is installed on the ball screw nut seat, and the installation tolerance is controlled within ±0.02 mm. The piezoelectric unit driving response accuracy is ±1 μm, the voltage driving range is 0V to 150V, and the corresponding maximum stroke is set to 100 μm. The stepping power module is powered in PWM (pulse width modulation) mode, and the control frequency is not less than 1 kHz. During configuration, the adjustment structure must be arranged equidistantly along the screw axis direction, and a tuning node is installed every 90°. Dynamic control is realized through closed-loop displacement sensor feedback, and the sensor accuracy needs to reach ±0.5 μm. The system response time is required to be controlled within 30 ms, and the adjustment frequency is real-time running within 20 Hz range, which meets the frequent adjustment working condition.

[0078] Step S373: configure the passive gap adjustment structure based on the gap compensation strategy; In this embodiment, the passive gap adjustment structure includes a spring-loaded radial pre-tightening ring and a thermal expansion self-adaptive gasket. The radial pre-tightening ring is constructed with double-layer wave spring, and the material is 65Mn high-elasticity steel. The single-coil spring stiffness is controlled within the range of 10N / mm to 30N / mm, and the pre-tightening force design value is 50N. The gasket is made of PTFE composite material with an expansion coefficient of 18×10 -6 / °C. When the working temperature variation range is-10℃ to 60℃, the linear expansion compensation gap can reach 25 μm. During installation, the pre-tightening ring is assembled in the groove on the outer wall of the nut, and the gasket is placed on the sliding surface between the nut and the screw. The limiting bayonet structure is fixed to avoid axial displacement. The passive structure does not adjust the dynamic response, and its configuration process depends on the initial gap range and the use of temperature environment to determine, to ensure that the compensation structure is always in the effective working interval during the whole temperature range operation.

[0079] Step S374: integrate the active gap adjustment structure and the passive gap adjustment structure to obtain the automatic gap compensation structure.

[0080] In this embodiment, the piezoelectric active adjustment device is coaxially installed with the radial pre-tightening ring in the ball screw nut housing, and the output force direction of the adjustment device is consistent with the action direction of the passive ring. The preset mechanical limiting stop block in the integrated structure has a maximum adjustment displacement of not more than 150% of the design gap of the nut. The stability of the integrated structure under the coupling action of dynamic excitation and thermal load is verified by using finite element analysis means, and the maximum stress is controlled to be below 250 MPa. The adjustment signal is input into the controller, and the master-slave control logic is set, that is, the active structure is preferentially adjusted, and if the adjustment displacement is less than 10 μm, the passive structure is triggered to enter the auxiliary state. The integrated structure must be tested on the standard DIN 69051 type ball screw structure platform, and the complete data list of the final output automatic gap compensation structure is formed, including component specifications, gap range, adjustment frequency, response time, load capacity, etc., to form a complete electric cylinder transmission compensation subsystem.

[0081] Preferably, step S4 is specifically: Step S41: identifying a fatigue sensitive area according to a guide rod metal fatigue life; In this embodiment, after the guide rod metal fatigue life prediction is completed, pixel-level analysis of the residual life of different parts is performed using a life partition distribution map. In the analysis, the fatigue critical criterion threshold is set as 20% of the initial life when the metal fatigue life is less than the initial life. When the life of a certain area is less than the threshold, it is divided into a fatigue sensitive area. The fatigue life data is obtained by combining crack detection with the S-N curve (stress-life curve) as described above. The S-N curve is obtained by loading the same material sample with ±45° symmetric bidirectional bending load at room temperature. The actual residual life of each measurement point is mapped to the guide rod CAD model, the life contour zone is marked by spatial interpolation method, and the minimum life point is taken as the center to extend 20 mm outward as the initial sensitive core area. Then, the continuous area with a life gradient less than 5% / mm is combined as an expanded fatigue sensitive band to form a fatigue sensitive area identification.

[0082] Step S42: detecting a trajectory influence factor based on the fatigue sensitive area; In this embodiment, after the fatigue sensitive area is identified, the trajectory influence factor is extracted. The influence factors include: instantaneous swing load, impact frequency per unit time, inertia impact direction change rate, sliding area pressure change gradient and reciprocating stroke boundary change. The load data is obtained by the built-in high-frequency strain gauge of the electric cylinder, the frequency uses the original data of the three-axis acceleration sensor with a sampling rate of 5 kHz and performs FFT spectrum analysis. The pressure change is collected by the inner wall pressure diaphragm sensor, and the maximum-minimum pressure difference is divided by the cycle time as the gradient index. When the impact frequency of a certain fatigue sensitive point exceeds 15 times / min, or its pressure change rate exceeds 2.5 MPa / s, and the inertia direction change angle exceeds 10° / 0.1s, the point is listed as a high trajectory influence area. After all the influence factors are normalized, the influence factor vector diagram is established, which is used for the next step of trajectory optimization.

[0083] Step S43: Designing a guide rod intelligent trajectory according to the trajectory influence factor, and generating a guide rod intelligent trajectory; In this embodiment, according to the trajectory influence factor vector diagram obtained in step S42, the guide rod trajectory is optimized and designed by using a piecewise cubic spline function. The trajectory planning space is set as a three-dimensional coordinate system (X is the horizontal stroke direction, Y is the vertical deflection direction, and Z is the guide rod axial direction), and a trajectory control node is set in each fatigue sensitive area. The number of control points is not less than three times the number of edge points of the influence area. The control point constraint conditions include: using a derivative of 0 uniform speed segment in a non-high frequency impact area segment, using a derivative of negative value segment to avoid accelerated impact in a pressure gradient area, and using an arc segment with a curvature limit not greater than 0.02 mm -1 After the trajectory design is completed, 1000 cycles or more of virtual motion simulation are performed, and the fatigue index (cumulative damage value) is verified to be not more than 0.85 by stress-time integral calculation of the maximum stress point at the control point. Finally, the guide rod intelligent trajectory data set is generated.

[0084] Step S44: Optimizing the electric cylinder initialization structure according to the automatic gap compensation structure, and obtaining an optimized electric cylinder structure; In this embodiment, after the automatic gap compensation structure configuration is completed, the original electric cylinder initialization structure is subjected to spatial topology optimization processing. The "Topology Study" module in the SolidWorks Simulation plug-in is used, the target mass reduction is set to 5%, the stress point is the guide rod end, the constraint point is the fixed end of the cylinder body, and the gap compensation structure connection section is kept undeformable. In the material attribute, 45# steel is set, the elastic modulus is 210GPa, and the Poisson's ratio is 0.28. In the optimization condition, the maximum stress is set to not more than 80% of the material yield strength, and the target function is the maximum structure stiffness. In the topology optimization result, the unsupported solid block is removed, and the assembly gap of each connection structure is refined according to the tolerance level m in ISO2768. The topology optimization result is updated to the original CAD model to obtain the electric cylinder optimization structure including the automatic gap compensation structure constraint interface, the cable hole wiring channel and the heat sink form adjustment.

[0085] Step S45: According to the electric cylinder optimization structure and the guide rod intelligent trajectory, the guide rod intelligent driving simulation is performed to generate guide rod intelligent driving data.

[0086] In this embodiment, based on the electric cylinder optimization structure of step S44 and the guide rod intelligent trajectory data of step S43, a driving simulation environment is constructed. The ANSYS Motion module is used to establish an electric cylinder-guide dynamics simulation system, the simulation time is 30 minutes, the time step is set to 1ms, and the HHT integral algorithm is used for solution. The simulation input is the guide rod driving function, the function form is a combined trajectory speed-acceleration function, including the front acceleration section, the middle uniform swing section and the end slow stop section; the driving input torque is input by the current control mode of the servo motor driving model, and the required torque is calculated in real time according to the set trajectory. The monitoring parameters include the guide rod actual displacement error, the ball screw gap compensation response delay time, the driving torque peak value and the average power of each section. Finally, all the running data are sampled at a frequency of 1kHz and exported as guide rod intelligent driving data for archiving, which is used for subsequent system response optimization and life estimation.

[0087] Preferably, the present specification also provides a swing guide rod electric cylinder intelligent driving system for executing the swing guide rod electric cylinder intelligent driving method as described above, the swing guide rod electric cylinder intelligent driving system comprising: a guide rod swing simulation module, configured to obtain electric cylinder design data; initialize the structure according to the electric cylinder design data to obtain an electric cylinder initialization structure; and simulate the guide rod swing according to the electric cylinder initialization structure to obtain guide rod swing data; The guide rod metal fatigue life prediction module is used for guide rod swing data to carry out guide rod swing jam analysis, and guide rod swing jam data is obtained; the sliding bearing wear degree is detected based on the guide rod swing jam data; the magnetic powder crack detection is carried out based on the sliding bearing wear degree, and crack data is generated; and the guide rod metal fatigue life is predicted based on the crack data; The automatic gap compensation structure design module is used for evaluating the guide rod deflection degree according to the guide rod swing data; the ball screw gap abnormality analysis is carried out based on the guide rod deflection degree, and ball screw gap abnormality data is obtained; and the automatic gap compensation structure is designed according to the ball screw gap abnormality data. The guide rod intelligent driving simulation module is used for carrying out guide rod intelligent trajectory planning according to the guide rod metal fatigue life, generating guide rod intelligent trajectory; optimizing the electric cylinder initialization structure according to the automatic gap compensation structure, obtaining the electric cylinder optimization structure; and carrying out guide rod intelligent driving simulation according to the electric cylinder optimization structure and the guide rod intelligent trajectory, generating guide rod intelligent driving data.

[0088] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the application being defined by the appended claims and not by the above description, therefore all variations falling within the meaning and scope of the equivalent elements of the application file are intended to be included in the present application.

[0089] The above description is only a specific implementation of the present application, enabling those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A smart drive method for a swing guide rod electric cylinder, characterized in that, Includes the following steps: Step S1: Obtain the electric cylinder design data; perform structural initialization based on the electric cylinder design data to obtain the electric cylinder initialization structure; Based on the initialization structure of the electric cylinder, the guide rod swing is simulated to obtain the guide rod swing data; Step S2: Perform guide rod swing and jamming analysis based on guide rod swing data to obtain guide rod swing and jamming data; detect the wear degree of sliding bearing based on guide rod swing and jamming data; perform magnetic particle crack detection based on sliding bearing wear degree to generate crack data; predict the fatigue life of guide rod metal based on crack data. Step S3: Evaluate the degree of guide rod runout based on the guide rod swing data; perform ball screw clearance anomaly analysis based on the degree of guide rod runout to obtain ball screw clearance anomaly data; An automatic backlash compensation structure was designed based on abnormal ball screw backlash data. Step S4: Based on the fatigue life of the guide rod metal, perform intelligent trajectory planning for the guide rod to generate the intelligent trajectory of the guide rod; optimize the initialization structure of the electric cylinder based on the automatic clearance compensation structure to obtain the optimized structure of the electric cylinder; Based on the optimized structure of the electric cylinder and the intelligent trajectory of the guide rod, intelligent drive simulation of the guide rod is performed to generate intelligent drive data for the guide rod.

2. The intelligent drive method for the swing guide rod electric cylinder according to claim 1, characterized in that, Step S1 is as follows: Step S11: Obtain the electric lever design data and extract the servo motor structure data and ball screw structure data; Step S12: Identify the lead screw drive structure based on the servo motor structure data; Step S13: Construct a guide rail support structure model based on the screw drive structure; Step S14: Analyze the screw drive parameters based on the ball screw structure data; Step S15: Input the screw drive parameters into the guide rail support structure model and perform coordination matching of the electric cylinder assembly to obtain the coordination matching data of the electric cylinder assembly; Step S16: Perform virtual assembly based on the electric cylinder assembly coordination and matching data to obtain the electric cylinder initialization structure; Step S17: Simulate the guide rod swing based on the electric cylinder initialization structure to obtain guide rod swing data.

3. The intelligent drive method for the swing guide rod electric cylinder according to claim 2, characterized in that, Step S16 is as follows: Step S161: Identify the component interface based on the electric cylinder assembly coordination and matching data; Step S162: Perform geometric alignment processing of the electric cylinder based on the component interface to obtain the assembly reference data of the electric cylinder; Step S163: Set assembly constraint relationships based on the electric cylinder assembly reference data; Step S164: Perform virtual assembly based on assembly constraints to obtain the initial structure of the electric cylinder.

4. The intelligent drive method for the swing guide rod electric cylinder according to claim 1, characterized in that, Step S2 is as follows: Step S21: Calculate the guide rod swing speed based on the guide rod swing data; Step S22: Calculate the instantaneous swing deceleration time period of the base guide rod swing speed; Step S23: Detect sudden changes in swing amplitude stagnation based on the instantaneous swing deceleration time period to obtain swing amplitude stagnation data; Step S24: Determine the guide rod swing jamming based on the swing amplitude stagnation data, and obtain the guide rod swing jamming data; Step S25: Detect the wear degree of the sliding bearing based on the guide rod swing and jamming data; Step S26: Perform magnetic particle crack detection based on the wear degree of the sliding bearing to generate crack data; Step S27: Predict the fatigue life of the guide rod metal based on crack data.

5. The intelligent drive method for the swing guide rod electric cylinder according to claim 4, characterized in that, Step S25 is as follows: Step S251: Extract the swing position offset information based on the guide rod swing jamming data; Step S252: Extract the position of the sliding bearing based on the swing position offset information; Step S253: Perform sliding simulation based on the position of the sliding bearing to obtain bearing sliding data; Step S254: Perform surface oxidation analysis on the material based on the bearing sliding data to obtain surface oxidation data of the material; Step S255: Detect metal particles based on the surface oxidation data of the material to obtain metal particle data; Step S256: Determine the degree of wear of the sliding bearing based on the metal particle data.

6. The intelligent drive method for the swing guide rod electric cylinder according to claim 4, characterized in that, Step S26 is as follows: Step S261: Identify high-wear areas based on the degree of wear of the sliding bearing; Step S262: Set longitudinal magnetization for the high wear area, and set the magnetization current range to 300A-2000A and the magnetization duration to 0.5s-3s to obtain longitudinal magnetization wear area data; Step S263: Apply magnetic powder for a duration of 1s-5s based on the longitudinal magnetization wear area data to obtain magnetic powder application area data; Step S264: Calculate the magnetic powder aggregation degree based on the data of the area where the magnetic powder is applied; Step S265: Identify magnetic powder aggregation regions based on the magnetic powder application area data according to the magnetic powder aggregation degree, and obtain magnetic powder aggregation region data; Step S266: Based on the preset crack magnetic particle data, crack determination is performed on the magnetic particle aggregation area data to generate crack data.

7. The intelligent drive method for the swing guide rod electric cylinder according to claim 1, characterized in that, Step S3 is as follows: Step S31: Evaluate the degree of guide rod deflection based on the guide rod swing data; Step S32: Acquire images of the ball screw based on the degree of guide rod deflection; Step S33: Calculate the ball screw clearance based on the ball screw image; Step S34: Calculate the vibration frequency based on the guide rod's sway and extract the low-frequency vibration data of the guide rod; Step S35: Simulate the transmission of ball screw clearance based on the low-frequency vibration data of the guide rod, and detect the wear degree of the screw nut; Step S36: Perform anomaly analysis on the ball screw clearance based on the wear degree of the lead screw nut to obtain abnormal ball screw clearance data; Step S37: Design an automatic backlash compensation structure based on abnormal ball screw backlash data.

8. The intelligent drive method for the swing guide rod electric cylinder according to claim 7, characterized in that, Step S37 is as follows: Step S371: Design a clearance compensation strategy based on abnormal ball screw clearance data; Step S372: Configure an active gap adjustment structure based on the gap compensation strategy; Step S373: Configure a passive gap adjustment structure based on the gap compensation strategy; Step S374: Integrate the active clearance adjustment structure and the passive clearance adjustment structure to obtain the automatic clearance compensation structure.

9. The intelligent drive method for the swing guide rod electric cylinder according to claim 1, characterized in that, Step S4 is as follows: Step S41: Identify fatigue-sensitive areas based on the fatigue life of the guide rod metal; Step S42: Detect trajectory influencing factors based on fatigue-sensitive areas; Step S43: Design the intelligent trajectory of the guide rod based on the trajectory influence factor, and generate the intelligent trajectory of the guide rod; Step S44: Optimize the electric cylinder initialization structure based on the automatic clearance compensation structure to obtain the optimized electric cylinder structure; Step S45: Based on the optimized structure of the electric cylinder and the intelligent trajectory of the guide rod, perform intelligent drive simulation of the guide rod to generate intelligent drive data for the guide rod.

10. A smart drive system for a swing guide rod electric cylinder, characterized in that, For performing the intelligent drive method of the swing guide rod electric cylinder as described in claim 1, the intelligent drive system of the swing guide rod electric cylinder includes: The guide rod swing simulation module is used to acquire electric cylinder design data; perform structural initialization based on electric cylinder design data to obtain the electric cylinder initialization structure; and perform guide rod swing simulation based on the electric cylinder initialization structure to obtain guide rod swing data. The guide rod metal fatigue life prediction module is used to perform guide rod swing and jamming analysis based on guide rod swing data to obtain guide rod swing and jamming data; detect the wear degree of sliding bearing based on guide rod swing and jamming data; perform magnetic particle crack detection based on sliding bearing wear degree to generate crack data; and predict the fatigue life of guide rod metal based on crack data. An automatic clearance compensation structure design module is used to evaluate the degree of guide rod runout based on guide rod swing data; perform ball screw clearance anomaly analysis based on the degree of guide rod runout to obtain ball screw clearance anomaly data; and design an automatic clearance compensation structure based on the ball screw clearance anomaly data. The intelligent drive simulation module for the guide rod is used to plan the intelligent trajectory of the guide rod based on the fatigue life of the guide rod metal, and generate the intelligent trajectory of the guide rod; optimize the initialization structure of the electric cylinder based on the automatic clearance compensation structure, and obtain the optimized structure of the electric cylinder; and simulate the intelligent drive of the guide rod based on the optimized structure of the electric cylinder and the intelligent trajectory of the guide rod, and generate the intelligent drive data of the guide rod.

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