Servo screwdriver tightening control method and system based on mes cooperation

Abstract

The application discloses a servo screwdriver tightening control method and system based on MES cooperation, which comprises the following steps: S1, multi-dimensional identity binding and process initialization configuration; S2, pre-self-checking starting and real-time working condition sensing; the MES system analyzes and calculates the obtained self-checking result and working condition data to generate a working condition compensation coefficient; S3, segmented closed-loop tightening, and realizing two-way real-time interaction between the servo screwdriver and the MES during the tightening process, and realizing abnormal intelligent prediction and hierarchical disposal; the application realizes automatic iteration of process parameters based on big data analysis, continuously improves assembly precision and stability, and reduces the cost of manual debugging; and is especially suitable for high-precision thread tightening assembly scenes in the fields of 3C electronics, automobile parts, new energy batteries and precision instruments.

Description

Technical Field

[0001] This invention belongs to the field of intelligent manufacturing assembly technology, and in particular relates to a servo screwdriver tightening control method and system based on MES collaboration. Background Technology

[0002] In modern intelligent manufacturing assembly processes, servo screwdrivers, with their advantages of high torque control precision and stable operation, are gradually replacing traditional pneumatic screwdrivers and becoming the core assembly tool. Manufacturing Execution Systems (MES), as the core of workshop production management, are responsible for process issuance, production scheduling, quality traceability, and data management. Currently, the integration of servo screwdrivers with MES systems suffers from several technical shortcomings, as detailed below:

[0003] Firstly, data transmission is mostly unidirectional. The servo screwdriver can only upload the result data after tightening to the MES system. The MES cannot intervene in the entire tightening process in real time, correct parameters, and control commands. The two are disconnected, which is "post-event recording" rather than "in-process control".

[0004] Secondly, the tightening process parameters are pre-set static parameters, which cannot be dynamically and adaptively adjusted according to real-time working conditions such as screw material, product batch, ambient temperature and humidity, and bit wear. For different specifications of products and different assembly stations, the parameters need to be manually adjusted, resulting in poor adaptability and easy assembly defects such as over-tightening, under-tightening, stripping, and floating lock.

[0005] Third, the abnormality detection mechanism is lagging behind. It can only determine whether the result is qualified after the tightening action is completed. It cannot predict abnormalities in advance and stop them in time during the tightening process, which leads to defective products flowing into the next process and increases rework and scrap costs.

[0006] Fourth, the data traceability dimension is singular, only recording basic tightening data such as torque and angle, without fully associating and binding operator information, product SN code, material batch, tooling status, environmental parameters, and equipment health status, which cannot meet the stringent quality traceability compliance requirements of industries such as automobiles and new energy.

[0007] Fifth, data is easily lost in network outage scenarios, and the lack of offline caching and breakpoint resume mechanisms affects the integrity and traceability of production data.

[0008] Through patent search and comparison with existing technologies, it was found that most existing related patents focus on the optimization of the torque control algorithm of the servo screwdriver itself, the single data upload function, or the separate production management module of the MES system. They have not formed a complete closed-loop collaborative control system. Therefore, a new control method and system are urgently needed to solve the above-mentioned technical problems. Summary of the Invention

[0009] This invention addresses the shortcomings of existing technologies, such as low integration between servo screwdrivers and MES, one-way communication, static parameter fixation, delayed anomaly response, incomplete traceability, and easy data loss. It provides a servo screwdriver tightening control method and system based on MES closed-loop collaboration, achieving bidirectional real-time closed-loop control between MES and the servo screwdriver: "MES issues commands - servo executes - real-time feedback - dynamic correction - anomaly prediction - data archiving." This achieves the technical effects of dynamic adaptive tightening parameters, early anomaly prediction and prevention, full-element quality traceability, and stable data transmission, improving assembly accuracy and pass rate, and reducing production costs.

[0010] To address the problems existing in the background art, the present invention adopts the following technical solution:

[0011] The MES-based servo screwdriver tightening control method includes the following steps:

[0012] S1. Multi-dimensional identity binding and process initialization configuration;

[0013] S2. Pre-self-test startup and real-time operating condition perception; The MES system analyzes and calculates the obtained self-test results and operating condition data to generate operating condition compensation coefficients.

[0014] S3, segmented closed-loop tightening, and realizes two-way real-time interaction between servo screwdriver and MES during the tightening process, and realizes intelligent prediction and hierarchical handling of abnormalities;

[0015] The segmented closed-loop tightening process includes the following steps:

[0016] Tooth-finding section: The servo screwdriver locates and screws in the screw, and transmits torque fluctuation values ​​and motor current values ​​back to the MES system in real time;

[0017] Rotation section: The MES system issues a dynamic speed control curve based on the working condition compensation coefficient. The dynamic speed control curve is obtained by dynamically correcting the original speed curve in real time through the working condition compensation coefficient.

[0018] Fitting section: The servo screwdriver detects torque change points in real time, determines that the screw is fully fitted to the product surface, and uploads the fitting signal to the MES system; the MES system compares the fitting parameters of the batch of products to determine whether there is any floating lock or non-fitting abnormality;

[0019] Final tightening section: The torque-angle dual closed-loop control combined with the fuzzy PID adaptive algorithm is used to adjust the output torque and rotation angle of the servo motor in real time, eliminating the error caused by the fluctuation of working conditions and ensuring that the final tightening accuracy is stable and meets the standard.

[0020] Pressure holding stage: The servo screwdriver holds the target torque value for 0.2s-1s. After completing the steady-state pressure holding, the pressure holding result is transmitted to the MES system, which then determines whether the final tightening result is qualified.

[0021] Furthermore, the method for intelligent prediction and tiered handling of anomalies includes:

[0022] Stripped thread abnormality: Sudden drop in torque and a sharp increase in rotation angle, instructing the servo screwdriver to stop immediately, marking the product as defective, and locking the assembly station;

[0023] Floating lock malfunction: Final tightening torque not up to standard, rotation angle too small. Issue a tightening instruction or prompt manual re-inspection.

[0024] Over-torque anomaly: Torque exceeds the upper limit threshold, angle is abnormally large, control servo screwdriver to brake urgently, isolate defective products;

[0025] Tightening out of sequence: Tightening is not performed in the preset sequence of the MES system, which will prevent subsequent actions and trigger an audible and visual alarm;

[0026] All anomaly handling processes and data are synchronized to the MES system in real time, leaving a complete record.

[0027] Furthermore, in step S2, the MES system analyzes and calculates the obtained self-inspection results and operating condition data to generate the operating condition compensation coefficient. The method is as follows:

[0028] After receiving the self-test results and operating condition data, the MES system normalizes parameters such as temperature, humidity, bit wear, tooling positioning accuracy, screw material, sensor accuracy, and motor operating status. It then calculates the operating condition compensation coefficient K using a multi-factor weighted fusion model, assigning weights according to the degree of influence of each factor on tightening accuracy. After weighted summation and threshold limiting, the operating condition compensation coefficient K is generated. This coefficient is used to correct the torque, speed, and angle target parameters in real time during the tightening process, achieving adaptive adjustment of operating conditions.

[0029] Furthermore, the control method also includes: end-to-end data archiving and process iterative optimization;

[0030] After each screw is tightened, all product data is archived and stored in the MES system. The MES system builds a tightening quality map based on massive tightening data, and calculates process capability index, defect rate, and abnormal trends. Through big data analysis, it automatically optimizes the compensation coefficient, speed curve, and threshold parameters in the tightening process package to achieve process iteration and upgrading.

[0031] Furthermore, the control method also includes: offline caching and breakpoint resumption;

[0032] If the workshop network is interrupted, the servo screwdriver controller automatically starts the local offline cache mode. The local cache capacity can store no less than 100,000 tightening data records to ensure uninterrupted production. After the network is restored, the controller automatically uploads the offline cache data to the MES system. The MES system performs integrity verification on the data, completes the breakpoint resume transmission, and prevents data loss.

[0033] Furthermore, during the screw-in stage, the servo screwdriver feeds according to the instructions and uploads rotation angle and torque change data in real time; the MES system compares the actual data with the theoretical model data in real time, and if the deviation exceeds the preset threshold, it immediately issues a speed reduction or pause command to avoid the assembly deviation from expanding.

[0034] Furthermore, in the final tightening stage, the servo screwdriver uploads real-time data on actual torque, angle, running time, and motor current. The MES system calculates a comprehensive final tightening correction coefficient based on material compensation coefficient, temperature compensation coefficient, and bit wear compensation coefficient through weighted fusion. Using the preset final tightening reference parameters of the multi-level tightening process package as a basis, the system obtains the target torque and angle for the final tightening that are suitable for the current working conditions through multiplicative correction, thereby achieving dynamic adaptive adjustment of the final tightening parameters and ensuring that the tightening accuracy meets the standards.

[0035] Furthermore, the multi-dimensional identity binding and process initialization configuration includes the following methods:

[0036] The operator completes identity authentication at the servo screwdriver terminal, and the authentication information is synchronized to the MES system in real time; the information of the product to be assembled is read, and the MES system automatically matches the multi-level tightening process package of the corresponding workstation based on the information; the controller of the servo screwdriver receives the process package issued by the MES, parses the process instructions of the MES system into control parameters that can be executed by the servo motor, and completes the pre-assembly initialization.

[0037] The MES-based servo screwdriver tightening control system includes:

[0038] MES server, servo control module, execution perception module, communication module, and terminal display module;

[0039] MES server: Includes process management unit, quality traceability unit, dynamic decision-making unit, data analysis unit, anomaly alarm unit, and data storage unit, responsible for process package issuance, operating condition analysis, dynamic instruction correction, anomaly judgment, data archiving, and process optimization;

[0040] Servo control module: responsible for parsing MES instructions, driving servo motors, executing closed-loop control, and caching offline data;

[0041] Execution perception module: including servo screwdriver body, high-precision torque sensor, absolute encoder, temperature and humidity sensor, bit wear detection sensor, and vision verification sensor, responsible for tightening action execution and full-dimensional data acquisition;

[0042] Communication module: Enables bidirectional real-time data interaction between the MES server and the servo control module with a latency of ≤5ms, and has command verification and data anti-tampering functions;

[0043] Terminal display module: Located on the servo screwdriver, it displays the authentication interface, process parameters, real-time tightening data, abnormal alarm information, and operation prompts.

[0044] The beneficial technical effects of this invention are as follows:

[0045] 1. This invention achieves full-link closed-loop collaboration: breaking through the traditional one-way data transmission mode, realizing the transformation of MES from "data monitoring and recording" to "real-time control", with full two-way interaction, tightening the pass rate to over 99.99%;

[0046] 2. This invention achieves dynamic adaptive adjustment: it combines working condition data to achieve real-time parameter compensation, eliminating the need for repeated manual adjustments, adapting to multi-variety, small-batch mixed-line production, and significantly improving flexible assembly capabilities;

[0047] 3. This invention achieves proactive anomaly prevention: by analyzing real-time features to predict anomalies in advance, it can immediately stop malfunctions, reducing assembly scrap and rework by more than 90% and lowering production costs;

[0048] 4. This invention achieves full-element quality traceability: it links production data across all dimensions, meeting industry compliance traceability requirements such as IATF16949 and ISO9001, with 100% traceability accuracy;

[0049] 5. This invention achieves stable and reliable data: it has offline caching and breakpoint resume functions to avoid data loss due to network fluctuations and ensure the integrity of production data;

[0050] 6. This invention achieves autonomous process optimization: Based on big data analysis, process parameters are automatically iterated to continuously improve assembly accuracy and stability and reduce manual debugging costs; it is especially suitable for high-precision thread tightening assembly scenarios in industries such as 3C electronics, automotive parts, new energy batteries, and precision instruments. Attached Figure Description

[0051] Figure 1 This is a schematic diagram of the servo screwdriver tightening control method based on MES collaboration provided in an embodiment of the present invention;

[0052] Figure 2This is a comparison chart of the standard torque-angle-speed curve and the abnormal curve provided in the embodiments of the present invention;

[0053] Figure 3 This is the timing data / instruction transmission direction for the full-link data interaction between the MES and the servo screwdriver provided in this embodiment of the invention. Detailed Implementation

[0054] The following description, in conjunction with the accompanying drawings, provides a clearer and more complete account of the MES-based servo screwdriver tightening control method and system provided by the present invention, and a detailed description is provided in conjunction with the following specific embodiments and comparative examples:

[0055] Example 1

[0056] This embodiment provides a servo screwdriver tightening control method based on MES collaboration, which includes the following steps:

[0057] S1. Multi-dimensional identity binding and process initialization configuration

[0058] Operators authenticate themselves at the servo screwdriver terminal using their employee ID, fingerprint, or card, and the authentication information is synchronized to the MES system in real time. The MES system reads the product serial number (SN), material batch code, and tooling ID of the product to be assembled using a barcode scanner. Based on this information, the MES system automatically matches the corresponding multi-level tightening process package for the workstation. The process package includes parameters such as multiple torque-angle-speed correlation curves, tightening sequence, error prevention rules, material compensation coefficients, and anomaly judgment thresholds. The servo screwdriver's controller receives the process package from the MES system, establishes a virtual process mapping model, and parses the MES system's process instructions into control parameters executable by the servo motor, completing the pre-assembly initialization.

[0059] S2, Pre-self-test start-up and real-time operating condition perception

[0060] The servo controller initiates a pre-self-test program to comprehensively test the servo motor windings, encoder, torque sensor, communication module, and bit installation status. The servo controller's pre-self-test program is based on the servo controller's self-test code, hardware status acquisition circuit, real-time sensor feedback, and communication link diagnostic logic.

[0061] The servo controller's pre-self-test program is implemented by a built-in ARM+FPGA dual-core processor running self-test code, in conjunction with hardware status acquisition circuits, sensor real-time feedback circuits, and communication link diagnostic logic. The self-test scope covers the servo motor, torque sensor, encoder, bit installation status, drive module, communication module, and local storage unit, completing a comprehensive test of electrical performance, mechanical status, communication connectivity, and system availability. The self-test results are uploaded to the MES system in real time. If an anomaly occurs, the device is locked and an alarm is triggered; if normal, the device enters the assembly-ready state.

[0062] The MES system analyzes and calculates the obtained self-inspection results and operating condition data to generate operating condition compensation coefficients. These coefficients are calculated using a multi-factor weighted fusion model, deviation normalization processing, and a real-time operating condition mapping algorithm.

[0063] After receiving the self-test results and operating condition data, the MES system normalizes parameters such as temperature, humidity, bit wear, tooling positioning accuracy, screw material, sensor accuracy, and motor operating status. It then calculates the operating condition compensation coefficient K using a multi-factor weighted fusion model, assigning weights to each factor based on their impact on tightening accuracy. After weighted summation and threshold limiting, the coefficient generates K. This coefficient is used to correct the torque, speed, and angle target parameters during the tightening process in real time, achieving adaptive adjustment of operating conditions. If the self-test or operating condition data is abnormal, the MES system immediately locks the servo screwdriver, pushes an audible and visual alarm and anomaly notification to the servo screwdriver terminal, and prevents the tightening action from starting. If the self-test and operating condition data are normal, the system enters the ready state.

[0064] S3, segmented closed-loop tightening and two-way real-time interaction

[0065] The servo screwdriver executes a five-stage tightening process, with each stage involving millisecond-level bidirectional data interaction with the MES system. The specific stages are as follows:

[0066] 1. Thread finding section: The servo screwdriver operates at low torque (5%-15% of the target torque) and low speed (≤50rpm) to perform screw positioning and screwing, and transmits torque fluctuation value and motor current value back to the MES system in real time; The MES system uses torque spectrum analysis to determine whether the screw is aligned, whether there are mixed screws, or whether there is misalignment. If any abnormality is found, the servo screwdriver is instructed to immediately retract and re-execute the thread finding action;

[0067] 2. Tightening Section: The MES system issues a dynamic speed control curve based on the working condition compensation coefficient. Because the tightening speed is directly affected by real-time working conditions such as ambient temperature, bit wear, screw material, and tooling positioning accuracy, if a fixed speed curve is used, defects such as misalignment, stripping, floating lock, and over-tightening are likely to occur. By introducing the working condition compensation coefficient, the original speed curve can be dynamically corrected in real time, allowing the servo screwdriver to always maintain the optimal feed speed under different working conditions, thereby improving tightening stability and accuracy.

[0068] The servo screwdriver feeds rapidly according to instructions and uploads rotation angle and torque change data in real time. The MES system compares the actual data with the theoretical model (the theoretical model here is the standard tightening torque-angle mathematical model built into the MES system and preset by the process, also called the reference tightening curve model) in real time. If the deviation exceeds the preset threshold, it immediately issues a speed reduction or pause command to avoid the assembly deviation from expanding.

[0069] 3. Fitting Section: The servo screwdriver detects torque change points in real time, determines that the screw is fully fitted to the product surface, and uploads the fitting signal to the MES system; the MES system compares the fitting parameters of the batch of products to determine whether there is any floating lock or non-fitting abnormality.

[0070] 4. Final Tightening Section: The torque-angle dual closed-loop control combined with the fuzzy PID adaptive algorithm is used to adjust the output torque and rotation angle of the servo motor in real time, eliminating errors caused by fluctuations in working conditions and ensuring that the final tightening accuracy is stable and meets the standards.

[0071] The servo screwdriver uploads real-time data on actual torque, angle, running time, and motor current. The MES system calculates a comprehensive final tightening correction coefficient based on material compensation coefficient, temperature compensation coefficient, and bit wear compensation coefficient through weighted fusion. Using the preset final tightening reference parameters of the multi-level tightening process package as a basis, the system obtains the target final tightening torque and angle adapted to the current working conditions through multiplication correction, realizing dynamic adaptive adjustment of final tightening parameters to ensure that tightening accuracy meets the standards.

[0072] 5. Pressure Holding Section: The servo screwdriver maintains the target torque value for 0.2s-1s. After completing the steady-state pressure holding, the pressure holding result (here, the pressure holding result refers to the comprehensive judgment result of three indicators of the servo screwdriver in the pressure holding section: torque stability, angle holding value, and steady-state error) is uploaded to the MES system, and the MES system determines whether the final tightening result is qualified.

[0073] like Figure 3 As shown, Figure 3 The diagram shows the timing of data interaction and command transmission between the MES and the servo screwdriver. T1–T5 correspond to the thread finding segment, screwing in segment, fitting segment, final tightening segment, and pressure holding segment, respectively. The arrows indicate the downward direction of commands and the upward direction of data. The MES and the servo screwdriver achieve bidirectional real-time interaction at each stage, completing command issuance, data feedback, dynamic correction, anomaly judgment, and result archiving, forming a complete closed-loop collaborative control.

[0074] S4. Intelligent Anomaly Prediction and Tiered Handling

[0075] Throughout the tightening process, real-time data on torque change slope, angle increment, motor current fluctuation, and vibration spectrum features are extracted and transmitted to the MES system's anomaly detection model (a multi-feature fusion tightening anomaly classification and discrimination model built based on a tightening process feature library, employing a hybrid discrimination model combining support vector machine (SVM) and threshold rules). The MES system matches the real-time feature data with the anomaly detection model to achieve early anomaly prediction and implement tiered handling.

[0076] Stripped thread abnormality: Sudden drop in torque and a sharp increase in rotation angle, instructing the servo screwdriver to stop immediately, marking the product as defective, and locking the assembly station;

[0077] Floating lock malfunction: Final tightening torque not up to standard, rotation angle too small. Issue a tightening instruction or prompt manual re-inspection.

[0078] Over-torque anomaly: Torque exceeds the upper limit threshold, angle is abnormally large, control servo screwdriver to brake urgently, isolate defective products;

[0079] Tightening out of sequence: Tightening is not performed in the preset sequence of the MES system, which will prevent subsequent actions and trigger an audible and visual alarm;

[0080] All anomaly handling processes and data are synchronized to the MES system quality module in real time, leaving a complete record.

[0081] like Figure 2 As shown, Figure 2 In the diagram, the horizontal axis represents the rotation angle, the left vertical axis represents the tightening torque, and the right vertical axis represents the tightening speed. The diagram includes a standard torque / angle speed curve, a stripped tooth abnormal torque curve, a floating lock abnormal torque curve, and an over-tightening abnormal torque curve. Point D is the node where final tightening is completed and pressure holding begins. The standard curve and the abnormal curve show significant differences in characteristics before and after point D, which can intuitively identify stripped teeth, floating locks, and over-tightening abnormalities, enabling real-time process identification.

[0082] S5, End-to-End Data Archiving and Process Iteration Optimization

[0083] After each screw is tightened, all data elements, including product serial number (SN), material batch, operator information, assembly time, complete torque-angle curve, environmental parameters, equipment status, exception code, and judgment result, are archived and stored in the MES system database. Based on massive tightening data, the MES system establishes a tightening quality map, calculates the process capability index (CPK), defect rate, and anomaly trends, and automatically optimizes the compensation coefficient, speed curve, and threshold parameters in the tightening process package through big data analysis to achieve process iteration and upgrade. It supports accurate traceability by multiple dimensions such as product SN, batch, time, workstation, and operator, and generates compliant quality reports.

[0084] S6, offline caching and resume interrupted downloads

[0085] If the workshop network is interrupted, the servo screwdriver controller automatically starts the local offline cache mode. The local cache capacity can store no less than 100,000 tightening data records to ensure uninterrupted production. After the network is restored, the controller automatically uploads the offline cache data to the MES system. The MES system performs integrity verification on the data, completes the breakpoint resume transmission, and prevents data loss.

[0086] It should be noted that the servo screwdriver in this embodiment can use a brushless servo motor, a 16-bit high-precision torque sensor, a 23-bit absolute encoder, and a torque control accuracy of ±0.5%; the servo controller can use an ARM Cortex-A9 + FPGA dual-core processor with 128G local cache, capable of storing 150,000 tightening data entries; the communication module can use industrial Ethernet with a communication cycle of 3ms, supporting the OPC UA protocol, and a data transmission latency of ≤1ms; the MES server can be equipped with process management, quality traceability, and data analysis modules, supporting big data analysis and process optimization.

[0087] Example 2

[0088] As an example, in this embodiment, the MES-based servo screwdriver tightening control method in Embodiment 1 is used to tighten precision screws on a mobile phone casing of a 3C electronics factory: screw specification M1.2, target torque... The specific steps are as follows:

[0089] 1. Identity Binding and Initialization: The operator authenticates via employee ID, scans the product SN code on their mobile phone, the MES system matches the M1.2 screw tightening process package, and the servo controller parses parameters: tooth-seeking section torque. Rotation speed 40 rpm; Rotation speed 180 rpm in the screw-in section; Torque threshold in the contact section Target torque for final tightening stage Pressure holding time: 0.5s;

[0090] 2. Self-test and working condition perception: The servo controller self-test equipment is normal, the ambient temperature is 25℃, the bit wear is normal, the MES system calculates the compensation coefficient as 1.0, and there is no compensation;

[0091] 3. Five-stage tightening: The tooth-finding stage has stable torque with no abnormalities; the screw-in stage feeds rapidly according to instructions, and data is uploaded in real time; the fitting stage detects sudden torque changes and determines the seat; the final tightening stage features dual closed-loop control, with actual torque... Accuracy ±0.5%; qualified after pressure holding is completed;

[0092] 4. Data archiving: Store product serial numbers, torque curves, operator information, time, and other data into the MES system for one-click traceability.

[0093] Example 3

[0094] As an example, in this embodiment, the MES-based servo screwdriver tightening control method in Embodiment 1 is used to handle abnormal scenarios: the screw strips during the assembly process.

[0095] When tightened to the final tightening point, the torque suddenly drops. If the angle exceeds the preset value by 2 times, the MES system will match the stripped thread abnormality model in real time, immediately instruct the servo screwdriver to stop, lock the workstation, display a stripped thread alarm on the terminal, mark the product as defective, and simultaneously upload the abnormal data to the MES quality module to prevent defective products from flowing into the next process.

[0096] Example 4

[0097] This embodiment provides a servo screwdriver tightening control system based on MES collaboration, including an MES server, a servo control module, an execution sensing module, a communication module, and a terminal display module;

[0098] MES server: Includes process management unit, quality traceability unit, dynamic decision-making unit, data analysis unit, anomaly alarm unit, and data storage unit, responsible for process package issuance, operating condition analysis, dynamic instruction correction, anomaly judgment, data archiving, and process optimization;

[0099] The process management unit employs a multi-level process package management module, a process mapping and parsing module, and an OPC UA command issuance module, and operates based on a process matching rule algorithm.

[0100] The quality traceability unit employs a full-element data association module, a quality compliance report generation module, and an IATF16949 / ISO9001 compliance adaptation module, and operates based on a unique SN code index association algorithm.

[0101] The dynamic decision-making unit employs a real-time operating condition analysis module, a multi-factor weighted compensation module, and an anomaly real-time discrimination module, and operates based on a fuzzy PID adaptive algorithm and a multi-feature fusion anomaly discrimination algorithm.

[0102] The data analysis unit employs a process capability (CPK) analysis module, a tightening quality map construction module, and a process parameter iterative optimization module, and operates based on big data statistical analysis algorithms and trend prediction algorithms.

[0103] Servo control module: It adopts an ARM+FPGA dual-core controller and has a parameter parsing unit, a motor drive unit, a closed-loop adjustment unit, and an offline cache unit. It is responsible for parsing MES instructions, driving the servo motor, executing closed-loop control, and caching offline data. The parameter parsing unit here adopts an industrial standard protocol parsing algorithm and process parameter mapping algorithm based on OPC UA / Profinet. It is a common parsing solution in the mature industrial control field and is used after being adapted for this invention.

[0104] Execution perception module: including servo screwdriver body, high-precision torque sensor, absolute encoder, temperature and humidity sensor, bit wear detection sensor, and vision verification sensor, responsible for tightening action execution and full-dimensional data acquisition;

[0105] Communication module: Adopts industrial Ethernet, supports OPC UA, Profinet, Modbus TCP communication protocols, realizes bidirectional real-time data interaction between MES server and servo control module with ≤5ms, and has command verification and data anti-tampering functions;

[0106] Terminal display module: Located on the servo screwdriver, it is used to display the identity authentication interface, process parameters, real-time tightening data, abnormal alarm information, and operation prompts.

[0107] This embodiment also employs the following technical solutions:

[0108] A non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the MES-based servo screwdriver tightening control method of Embodiment 1.

[0109] Furthermore, this embodiment also adopts the following technical solution:

[0110] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the MES-based servo screwdriver tightening control method of Embodiment 1.

[0111] From the above description of the embodiments, those skilled in the art will clearly understand that the facilities of the present invention can be implemented using software plus necessary general-purpose hardware platforms. Embodiments of the present invention can be implemented using existing processors, or by dedicated processors used for this or other purposes for suitable systems, or by hardwired systems. Embodiments of the present invention also include non-transitory computer-readable storage media, comprising machine-readable media for carrying or having machine-executable instructions or data structures stored thereon; such machine-readable media can be any available medium accessible by a general-purpose or special-purpose computer or other machine with a processor. For example, such machine-readable media can include RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disc storage, disk storage or other magnetic storage devices, or any other medium that can be used to carry or store the required program code in the form of machine-executable instructions or data structures and is accessible by a general-purpose or special-purpose computer or other machine with a processor. When information is transmitted or provided to a machine via a network or other communication connection (hardwired, wireless, or a combination of hardwired and wireless), that connection is also considered a machine-readable medium.

[0112] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A servo screwdriver tightening control method based on MES collaboration, characterized in that, Includes the following steps: S1. Multi-dimensional identity binding and process initialization configuration; S2. Pre-self-test startup and real-time operating condition perception; The MES system analyzes and calculates the obtained self-test results and operating condition data to generate operating condition compensation coefficients. S3, segmented closed-loop tightening, and realizes two-way real-time interaction between servo screwdriver and MES during the tightening process, and realizes intelligent prediction and hierarchical handling of abnormalities; The segmented closed-loop tightening process includes the following steps: Tooth-finding section: The servo screwdriver performs screw positioning and screwing, and transmits torque fluctuation value and motor current value back to the MES system in real time; Rotation section: The MES system issues a dynamic speed control curve based on the working condition compensation coefficient. The dynamic speed control curve is obtained by dynamically correcting the original speed curve in real time through the working condition compensation coefficient. Fitting section: The servo screwdriver detects torque change points in real time, determines that the screw is fully fitted to the product surface, and uploads the fitting signal to the MES system; the MES system compares the fitting parameters of the batch of products to determine whether there is any floating lock or non-fitting abnormality; Final tightening section: The torque-angle dual closed-loop control combined with the fuzzy PID adaptive algorithm is used to adjust the output torque and rotation angle of the servo motor in real time, eliminating the error caused by the fluctuation of working conditions and ensuring that the final tightening accuracy is stable and meets the standard. Pressure holding stage: The servo screwdriver holds the target torque value for 0.2s-1s. After completing the steady-state pressure holding, the pressure holding result is transmitted to the MES system, which then determines whether the final tightening result is qualified.

2. The servo screwdriver tightening control method based on MES collaboration according to claim 1, characterized in that, The method for intelligent prediction and tiered handling of anomalies includes: Stripped thread abnormality: Sudden drop in torque and a sharp increase in rotation angle, instructing the servo screwdriver to stop immediately, marking the product as defective, and locking the assembly station; Floating lock malfunction: Final tightening torque not up to standard, rotation angle too small. Issue a re-tightening instruction or prompt manual re-inspection. Over-torque anomaly: Torque exceeds the upper limit threshold, angle is abnormally large, control servo screwdriver to brake urgently, isolate defective products; Tightening out of sequence: Tightening is not performed in the preset sequence of the MES system, which will prevent subsequent actions and trigger an audible and visual alarm; All anomaly handling processes and data are synchronized to the MES system in real time, leaving a complete record.

3. The servo screwdriver tightening control method based on MES collaboration according to claim 1, characterized in that, In step S2, the MES system analyzes and calculates the obtained self-inspection results and operating condition data to generate the operating condition compensation coefficient. The method is as follows: After receiving the self-test results and working condition data, the MES system normalizes parameters such as temperature, humidity, bit wear, tooling positioning accuracy, screw material, sensor accuracy, and motor operating status. It then calculates the working condition compensation coefficient K by using a multi-factor weighted fusion model, assigning weights according to the degree of influence of each factor on tightening accuracy. After weighted summation and threshold limiting, the working condition compensation coefficient K is generated. This coefficient is used to correct the target parameters of torque, speed, and angle during the tightening process in real time, so as to achieve adaptive adjustment of working conditions.

4. The servo screwdriver tightening control method based on MES collaboration according to claim 1, characterized in that, The control method also includes: end-to-end data archiving and process iteration optimization; After each screw is tightened, all product data is archived and stored in the MES system. The MES system builds a tightening quality map based on massive tightening data, and calculates process capability index, defect rate, and abnormal trends. Through big data analysis, it automatically optimizes the compensation coefficient, speed curve, and threshold parameters in the tightening process package to achieve process iteration and upgrading.

5. The servo screwdriver tightening control method based on MES collaboration according to claim 1, characterized in that, The control method also includes: offline caching and breakpoint resumption; If the workshop network is interrupted, the servo screwdriver controller automatically starts the local offline cache mode. The local cache capacity can store no less than 100,000 tightening data records to ensure uninterrupted production. After the network is restored, the controller automatically uploads the offline cache data to the MES system. The MES system performs integrity verification on the data, completes the breakpoint resume transmission, and prevents data loss.

6. The servo screwdriver tightening control method based on MES collaboration according to claim 1, characterized in that, During the screw-in stage, the servo screwdriver feeds according to the instructions and uploads rotation angle and torque change data in real time. The MES system compares the actual data with the theoretical model data in real time. If the deviation exceeds the preset threshold, it immediately issues a speed reduction or pause command to avoid the assembly deviation from expanding.

7. The servo screwdriver tightening control method based on MES collaboration according to claim 1, characterized in that, During the final tightening stage, the servo screwdriver uploads real-time data on actual torque, angle, running time, and motor current. The MES system calculates a comprehensive final tightening correction coefficient based on material compensation coefficient, temperature compensation coefficient, and bit wear compensation coefficient through weighted fusion. Using the preset final tightening reference parameters of the multi-level tightening process package as a basis, the system obtains the target torque and angle for the final tightening that are suitable for the current working conditions through multiplication correction, thereby achieving dynamic adaptive adjustment of the final tightening parameters and ensuring that the tightening accuracy meets the standards.

8. The servo screwdriver tightening control method based on MES collaboration according to claim 1, characterized in that, The multi-dimensional identity binding and process initialization configuration includes the following methods: The operator completes identity authentication at the servo screwdriver terminal, and the authentication information is synchronized to the MES system in real time; the information of the product to be assembled is read, and the MES system automatically matches the multi-level tightening process package of the corresponding workstation based on the information; the controller of the servo screwdriver receives the process package issued by the MES, parses the process instructions of the MES system into control parameters that can be executed by the servo motor, and completes the pre-assembly initialization.

9. A servo screwdriver tightening control system based on MES collaboration, used to implement the method described in any one of claims 1-8, characterized in that, include: MES server, servo control module, execution perception module, communication module, and terminal display module; MES server: Includes process management unit, quality traceability unit, dynamic decision-making unit, data analysis unit, anomaly alarm unit, and data storage unit, responsible for process package issuance, operating condition analysis, dynamic instruction correction, anomaly judgment, data archiving, and process optimization; Servo control module: responsible for parsing MES instructions, driving servo motors, executing closed-loop control, and caching offline data; Execution perception module: including servo screwdriver body, high-precision torque sensor, absolute encoder, temperature and humidity sensor, bit wear detection sensor, and vision verification sensor, responsible for tightening action execution and full-dimensional data acquisition; Communication module: Enables bidirectional real-time data interaction between the MES server and the servo control module with a latency of ≤5ms, and has command verification and data anti-tampering functions; Terminal display module: Located on the servo screwdriver, it is used to display the identity authentication interface, process parameters, real-time tightening data, abnormal alarm information, and operation prompts.

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