Automatic tightening tool precision control system based on real-time parameter acquisition

Abstract

The application provides an automatic tightening tool precise control system based on real-time parameter acquisition, relates to the field of industrial assembly, and comprises a tightening controller, at least one automatic tightening tool, a real-time parameter acquisition module, a dynamic control decision module, a communication module, a real-time monitoring module and a data management module; the real-time parameter acquisition module is arranged in the automatic tightening tool and is used for high-frequency real-time acquisition of torque, angle, rotating speed, current and tool state parameters in the whole tightening operation process; the real-time parameter acquisition module arranged in the automatic tightening tool is used for high-frequency real-time acquisition of torque, angle, rotating speed, current and tool state parameters at fixed millisecond intervals in the whole tightening operation process, and the acquired parameters are transmitted to the tightening controller in real time through the communication module, the tightening curve is generated in combination with the real-time monitoring module, and real-time sensing and state monitoring of the tightening process are realized.

Description

Technical Field

[0001] This invention relates to the field of industrial assembly, specifically to a precision control system for automated tightening tools based on real-time parameter acquisition. Background Technology

[0002] In the industrial assembly field, especially in the bolt tightening operations of high-precision components in automobiles, construction machinery, and rail transportation, tightening error prevention and quality control technologies have evolved from traditional manual tools to semi-automated tightening equipment. Early on, assembly operations relied primarily on operators using ordinary torque wrenches, controlling torque based on experience and simple measuring tools. The quality of the work was highly dependent on individual skill, easily leading to problems such as missed tightening, incorrect sequence tightening, over-tightening, or under-tightening. With the development of automated production lines, electric tightening tools and basic tightening controllers gradually emerged. While these could achieve basic torque control and result judgment, significant limitations remained.

[0003] Although existing technologies can accomplish tightening tasks to a certain extent, the following problems still exist in practical use:

[0004] Existing tightening systems collect parameters such as torque and angle at a low frequency during operation, making it difficult to achieve millisecond-level real-time high-frequency acquisition and closed-loop feedback. This results in the inability to detect process deviations in a timely manner, easily leading to quality defects such as over-tightening, under-tightening, and stripping. The process stability is poor, making it difficult to meet the requirements of high-safety-level assembly.

[0005] Existing technologies mostly adopt fixed control strategies, lacking the ability to dynamically identify and adaptively compensate for real-time parameter deviation trends. When faced with interference from working conditions such as material hardness fluctuations, tool wear, and temperature changes, they cannot adjust control parameters in real time, which easily leads to accumulated errors and ultimately insufficient consistency in tightening results.

[0006] Existing tightening systems generally suffer from data silos, making it difficult to fully trace real-time parameters, control processes, and results. They also lack effective integration with industrial positioning error prevention, equipment health management, and multi-tool collaboration, resulting in the inability to prevent risks such as missed tightening, incorrect sequence, and tool malfunctions in a timely manner, and weakening overall quality management and process optimization capabilities.

[0007] Therefore, a precision control system for automated tightening tools based on real-time parameter acquisition is needed to solve the above problems. Summary of the Invention

[0008] Technical problems to be solved

[0009] To address the shortcomings of existing technologies, this invention provides an automated tightening tool precision control system based on real-time parameter acquisition, which solves the problems mentioned in the background section above.

[0010] Technical solution

[0011] To achieve the above objectives, the present invention is implemented through the following technical solution: a precision control system for an automated tightening tool based on real-time parameter acquisition, comprising a tightening controller, at least one automated tightening tool, a real-time parameter acquisition module, a dynamic control decision module, a communication module, a real-time monitoring module, and a data management module;

[0012] The real-time parameter acquisition module is installed inside the automated tightening tool. It is used to acquire torque, angle, speed, current and tool status parameters in high frequency and real time throughout the tightening operation. The acquired real-time parameters are then sent to the tightening controller via the communication module.

[0013] The dynamic control decision module is located in the tightening controller. It is used to receive real-time parameters transmitted by the communication module, perform dynamic analysis on the tightening process, and generate and issue control commands to the automated tightening tool in real time based on the analysis results, so as to realize closed-loop precise adjustment of the operating parameters of the tightening tool.

[0014] The communication module is used to realize real-time data interaction and control command transmission between the automated tightening tool and the tightening controller; the real-time monitoring module is used to generate a tightening curve based on the real-time parameters and perform status monitoring.

[0015] The data management module is used to store data throughout the entire process and support traceability and analysis; the modules work together through data interaction and command transmission to achieve real-time perception, dynamic decision-making and precise control of the tightening process.

[0016] Preferably, the specific working steps of the real-time parameter acquisition module include:

[0017] SpA1, Initialization Acquisition: Before the tightening operation begins, acquire the tool's baseline status parameters and complete the connection verification with the tightening controller;

[0018] SpA2, High-frequency dynamic acquisition: During the tightening process, torque, angle, speed, current and tool status parameters are continuously acquired at fixed millisecond intervals;

[0019] SpA3, Real-time Upload: Each set of collected parameters is packaged in real time and sent to the tightening controller via the communication module;

[0020] The specific working steps of the dynamic control decision module include:

[0021] SpB1, Parameter Reception and Preprocessing: Receives real-time parameters transmitted by the communication module and performs validity verification and noise filtering;

[0022] SpB2, Strategy Matching and Decision-Making: Based on the current process stage and parameter deviation, select or switch the most suitable strategy from the preset torque control strategy, angle control strategy and torque-angle composite control strategy.

[0023] SpB3, Command Generation and Issuance: Calculates control deviation, generates precise adjustment commands, and issues them to automated tightening tools via the communication module to achieve real-time adjustment of operating parameters.

[0024] Preferably, the dynamic control decision module further includes an adaptive compensation unit, which works in conjunction with the real-time parameter acquisition module and the dynamic control decision module. Its specific working steps include:

[0025] SpC1, Deviation Trend Identification: After receiving real-time parameters from multiple cycles, the extended Kalman filter and gray system prediction fusion algorithm are used to analyze the torque and angle deviation trends;

[0026] SpC2, Compensation Instruction Generation: The compensation parameters are automatically generated using a fusion algorithm of fuzzy inference and recursive least squares, and the tool output is dynamically corrected.

[0027] SpC3, Effect Verification: Receive real-time parameters for the next cycle, verify the compensation effect and perform iterative optimization to ensure that the final tightening result is stable and meets the standards.

[0028] Preferably, the real-time monitoring module works in conjunction with the real-time parameter acquisition module and the dynamic control decision module, and its specific working steps include:

[0029] SpD1, Real-time Curve Generation: Continuously generates and updates the tightening curve based on the received real-time parameters;

[0030] SpD2, Threshold Comparison and Intervention: The current parameters and curve shape are compared with the preset qualified threshold. When the parameters and curve shape are within the qualified threshold range, the normal tightening process continues and the monitoring status is updated. When the qualified threshold range is exceeded, an audible and visual alarm is immediately triggered and a process pause command is sent to the dynamic control decision module.

[0031] SpD3, Automatic Result Judgment: After tightening is completed, the system automatically determines whether the operation is qualified or unqualified based on the final parameters, and sends the judgment result to the data management module.

[0032] Preferably, the communication module adopts a hybrid communication architecture, working in conjunction with the real-time parameter acquisition module and the dynamic control decision module to ensure real-time performance and reliability. Its specific working steps include:

[0033] SpE1, Real-time Data Channel Transmission: Supports millisecond-level high-frequency parameters being uploaded from automated tightening tools to the tightening controller;

[0034] SpE2, Reliable Command Channel Transmission: A confirmation and retransmission mechanism is adopted to ensure that control commands are reliably sent from the tightening controller to the automated tightening tool and return execution feedback;

[0035] SpE3, Status Synchronization: Regularly perform online status synchronization and heartbeat detection of devices to maintain communication connection stability.

[0036] Preferably, the system further includes an equipment health management module, which works in conjunction with the real-time parameter acquisition module, the communication module, and the dynamic control decision module. This module receives tool status parameters transmitted by the communication module and then performs continuous monitoring and proactive intervention of the tool status. Its specific working steps include:

[0037] SpF1, Status Information Acquisition: Receives battery level, temperature, and cumulative tightening count tool status parameters periodically reported by the automated tightening tool;

[0038] SpF2, Health Trend Analysis: Continuously monitor tool performance data and identify potential abnormal trends;

[0039] SpF3, Intervention Command Issuance: When the tool is in normal condition, it maintains the current working mode. When an anomaly is detected, it automatically issues lock, parameter optimization, or maintenance reminder commands to the corresponding tool.

[0040] Preferably, the system further integrates an industrial positioning error prevention module. This module works collaboratively with the dynamic control decision module, real-time parameter acquisition module, and communication module to receive real-time parameters and operational information, thereby achieving a closed-loop error prevention system throughout the entire process. Its specific operational steps include:

[0041] SpG1, Job Information Acquisition: Acquire the job sequence, part identification, and tool matching information for the current workstation;

[0042] SpG2, Error Prevention Verification: Combines real-time parameter execution sequence control, part error prevention, tool error prevention, and process consistency verification;

[0043] SpG3, Closed-loop execution: The tightening process is allowed to continue when all error prevention checks pass. When any error prevention check fails, an alarm is triggered and the operation is suspended. At the same time, the dynamic control decision module is notified to handle the situation accordingly.

[0044] Preferably, the data management module works collaboratively with the real-time parameter acquisition module, the dynamic control decision module, and the real-time monitoring module, and its specific working steps include:

[0045] SpH1, Full-process data storage: The real-time parameters, control command records, judgment results and context information for each tightening are completely stored in the database;

[0046] SpH2, Traceability Query: Supports quick retrieval of historical data by serial number, time, and workstation conditions;

[0047] SpH3, Statistical Analysis: Performs statistical process control analysis on batch data, generates process capability reports, and provides feedback to the dynamic control decision module for process optimization.

[0048] Preferably, the tightening controller supports multi-tool collaborative management. This multi-tool collaborative management function is achieved collaboratively through the dynamic control decision module, communication module, and real-time parameter acquisition module. Its specific working steps include:

[0049] Multiple automated tightening tools at the same workstation are bound to corresponding tightening tasks; precise control and status monitoring are performed independently based on the real-time parameters of each tool; multiple tools work collaboratively according to the work sequence.

[0050] Preferably, the system further includes a visualization interaction module, which works in conjunction with the real-time monitoring module, data management module, and communication module to provide on-site operation visualization and human-machine interaction after receiving information transmitted by the real-time monitoring module and data management module. Its specific working steps include: real-time display of tightening process status, tightening curve, production plan and alarm information, and support for operators to switch workstations, select work programs and make calls.

[0051] Beneficial effects

[0052] This invention provides a precision control system for automated tightening tools based on real-time parameter acquisition. This invention offers the following advantages:

[0053] 1. This invention, through a real-time parameter acquisition module installed within an automated tightening tool, acquires torque, angle, speed, current, and tool status parameters at high frequency and millisecond intervals throughout the entire tightening process. These parameters are then transmitted in real-time to the tightening controller via a communication module. Combined with a real-time monitoring module, a tightening curve is generated, enabling real-time perception and status monitoring of the tightening process. This effectively solves the problems of low acquisition frequency and inability to detect process deviations in a timely manner in existing technologies, significantly improving the stability and quality consistency of the tightening process.

[0054] 2. The dynamic control decision module of this invention has a built-in adaptive compensation unit. It uses an extended Kalman filter and a gray system prediction fusion algorithm to identify deviation trends, and then uses a fuzzy inference and recursive least squares fusion algorithm to generate compensation parameters in real time. This allows for dynamic and precise correction of the tool output, enabling intelligent switching and adaptive adjustment of torque control strategy, angle control strategy, and torque-angle composite control strategy. This effectively solves the problems of poor adaptability of fixed strategies and difficulty in eliminating accumulated errors in existing technologies, and significantly improves tightening accuracy and process robustness under complex working conditions.

[0055] 3. This invention achieves a complete closed loop from work sequence error prevention, real-time parameter acquisition, closed-loop precise control to full-process data storage and SPC statistical analysis through deep integration of communication modules, data management modules, industrial positioning error prevention modules, equipment health management modules, and multi-tool collaborative management functions. It can not only effectively prevent error prevention problems such as missing screws, wrong sequence, and wrong tools, but also monitor the health status of tools in real time and support lifelong data traceability, providing a reliable basis for process optimization. It comprehensively solves the problems of data silos, low integration and weak quality management capabilities in existing technologies, and greatly improves the intelligence level of the system and the overall assembly quality. Attached Figure Description

[0056] Figure 1 This is a system framework diagram of the present invention;

[0057] Figure 2 This is a flowchart of the real-time parameter acquisition module of the present invention;

[0058] Figure 3 This is a flowchart of the dynamic control decision module of the present invention;

[0059] Figure 4 This is a schematic diagram of the system function menu interface of the present invention;

[0060] Figure 5 This is a schematic diagram of the background online status monitoring of the system of the present invention;

[0061] Figure 6 This is a schematic diagram of the user management interface of the system of the present invention;

[0062] Figure 7 This is a screenshot of the fault management list interface of the system of the present invention;

[0063] Figure 8 This is a schematic diagram of the online tightening quality analysis interface of the system of the present invention;

[0064] Figure 9 This is a schematic diagram of the quality defect analysis interface of the system of the present invention. Detailed Implementation

[0065] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Specific Implementation Example 1:

[0067] like Figure 1As shown, a precision control system for automated tightening tools based on real-time parameter acquisition is provided. The system includes a tightening controller, at least one automated tightening tool, a real-time parameter acquisition module, a dynamic control decision module, a real-time monitoring module, a communication module, and a data management module. It also includes an equipment health management module, an industrial positioning and error prevention module, and a visual interaction module. This system can operate in a stable industrial environment, supporting both single-station offline mode and workshop cluster online mode. The underlying hardware uses an industrial Wi-Fi 6 wireless communication link and a wired Ethernet network for backup, ensuring uninterrupted millisecond-level interaction and no data loss. It fully meets the real-time and reliability requirements of high-precision assembly scenarios such as automotive parts, construction machinery, and rail transportation. Figure 5 As shown, a schematic diagram of the background online status monitoring of the system of the present invention is provided for real-time monitoring of various tools, wherein the status of the tools is mainly divided into three types: online, offline, and fault.

[0068] like Figure 4 As shown, a schematic diagram of the system's function menu interface is provided. In this system, the real-time parameter acquisition module is integrated into the hardware carrier inside the automated tightening tool. Its hardware components include a torque acquisition chip, an angle encoder, a current sampling circuit, a speed Hall sensor, a status monitoring unit, a signal conditioning circuit, and a local cache unit. Specifically, the torque acquisition chip is used to acquire the real-time torque signal of the tightening output shaft through contact; the angle encoder records the number of bolt rotations and angle increments; the current sampling circuit acquires the real-time operating current of the tool's drive motor; the speed Hall sensor detects the output shaft speed and start / stop status; the status monitoring unit acquires the tool's switch status, trigger signal, limit signal, and emergency stop signal; the signal conditioning circuit converts analog signals into digital signals and performs filtering, amplification, and noise reduction; and the local cache unit temporarily stores high-frequency acquisition data that has not yet been uploaded, preventing data loss due to network disconnection. In practical scenarios, this module can be used for workstations such as automotive chassis bolt tightening, engine bolt fastening, and wheel hub bolt fixed-value tightening, and can adapt to assembly operations of bolts of different specifications from M6 to M20. The specific working steps of the real-time parameter acquisition module are as follows: Figure 2 As shown, the details are as follows:

[0069] First, initial data acquisition is performed: Before starting the tightening operation, the tool's factory calibration parameters, historical cumulative running data, and current hardware status are read to complete the benchmark parameter calibration. At the same time, a handshake frame is sent to the tightening controller through the communication module, and the controller returns a response frame to confirm that the connection is normal, the timing is synchronized, and the identity authentication is successful before entering the ready state. This step can effectively avoid the quality risks caused by starting the operation without calibrating, authenticating, or connecting the tool to the network in the actual production line.

[0070] Then, high-frequency dynamic data acquisition is performed. After the tightening is triggered, continuous sampling is performed at a fixed millisecond interval to continuously acquire the "five-in-one" parameters of torque, angle, speed, current, and tool status. The sampling period is stable and there are no dropped frames or misalignments. All acquired data are stamped with timestamps and unique tool numbers to ensure that each set of data can be located and traced. In actual scenarios, the mechanical changes of the entire tightening process can be completely reproduced, providing a reliable original basis for subsequent curve analysis and anomaly judgment.

[0071] Finally, real-time uploading is performed. Each set of collected data is encapsulated into a data frame according to a unified protocol and sent to the tightening controller in real time through the communication module. The uploading process supports breakpoint resumption and cached retransmission to ensure that high-frequency data is sent completely without affecting the real-time control performance.

[0072] The dynamic control decision module is deployed in the main control unit inside the tightening controller. Its hardware components include an industrial-grade main control chip, a computing unit, a storage unit, an instruction output interface, a logic control unit, and a clock synchronization unit. The main control chip is responsible for real-time calculations and logical judgments. The computing unit is used for parameter parsing, deviation calculation, strategy matching, and compensation generation. The storage unit stores control strategies, process parameters, and historical execution records. The instruction output interface sends control commands to the automated tightening tool. The logic control unit handles interlock judgments, timing control, and anomaly handling. The clock synchronization unit ensures that the controller and the tool maintain consistent time references. As the core control unit of the system, this module undertakes real-time decision-making and precise adjustment functions. In practical applications, it can be used in high-precision workstations, avoiding typical defects such as over-tightening, under-tightening, stripping, and false tightening.

[0073] The dynamic control decision module includes three explicit control strategies, whose specific definitions and applications are as follows:

[0074] Torque control strategy: The target torque is the only control target. Tightening stops when the set torque is reached. It is suitable for bolts with clear clamping force requirements and rigid connections, such as automotive chassis subframe bolts.

[0075] Angle control strategy: The target rotation angle is used as the control target. Tightening stops when the set angle is reached. It is suitable for assemblies of seals, plastic parts, and flexible connections.

[0076] Torque-angle composite control strategy: First, pre-tighten with torque, then tighten with angle. The two-step control ensures clamping consistency and is suitable for high-safety-level bolts such as those for engine cylinder heads and wheel hubs.

[0077] The specific working steps of the dynamic control decision module are as follows: Figure 3 As shown, the details are as follows:

[0078] First, parameter reception and preprocessing are performed: real-time parameters uploaded by the communication module are received, and the integrity, timing correctness and format standardization of the data are verified. Noise data caused by environmental interference, electromagnetic interference and signal jitter is filtered out, and valid real parameters are retained for subsequent calculations to ensure stable and reliable control input.

[0079] Then, strategy matching and decision-making are performed: based on the current assembly process stage, bolt specifications, target parameters and real-time deviation, the torque control strategy, angle control strategy or torque-angle composite control strategy is automatically selected. The strategy switching process is without delay and without impact, ensuring that the optimal control method is used in different stages. In actual scenarios, the angle priority strategy is used for soft connection materials, the torque priority strategy is used for hard connection materials, and the composite strategy is used for precision structural parts.

[0080] Finally, commands are generated and issued: the control deviation is calculated based on the difference between the target parameters and the actual parameters, and corresponding torque adjustment commands, speed adjustment commands, angle correction commands or stop commands are generated based on the magnitude of the deviation. These commands are then issued to the automated tightening tool through the communication module to correct the output torque, speed, angle and other operating parameters in real time, thereby achieving closed-loop precise control.

[0081] The dynamic control decision module has a built-in adaptive compensation unit, which can be implemented using the main control chip and the computing unit without the need for additional hardware. Its specific working steps are explained below:

[0082] First, deviation trend identification is performed: the input data for this step consists of real-time torque parameters, real-time angle parameters, and timestamp sequences over multiple consecutive periods. During identification, an extended Kalman filter and a grey system prediction fusion algorithm are used. First, the parameters are smoothed by the extended Kalman filter to remove random disturbances. Then, the grey system prediction algorithm is used to analyze the deviation change trend in the near future to determine whether the deviation is converging, stabilizing, or continuing to increase. Finally, the trend determination result and deviation prediction value are output, which are used in the system to predict tightening quality risks in advance and achieve proactive control.

[0083] The specific calculation formula is as follows:

[0084] Extended Kalman filter formula:

[0085] ;

[0086] ;

[0087] Grey system prediction formula:

[0088] ;

[0089] in: : The optimal estimated state of torque and angle at time k after extended Kalman filtering (smoothed real-time parameter values); Based on the prior prediction of the current torque and angle at the previous moment; The Kalman gain matrix determines the correction weights of the actual measurement data to the state estimate. : The actual measured value of torque or angle uploaded by the real-time parameter acquisition module at time k; : A nonlinear observation function that maps the predicted state to a measurement space; : The Jacobian matrix of the observation matrix; , The posterior and prior error covariance matrices reflect the uncertainty of the current estimate. : Identity matrix; : The predicted value of a single cumulative sequence at time k+1 in the grey system prediction (used for deviation trend prediction). : Initial values ​​of the original torque or angle sequence; Development coefficient, reflecting the trend of deviation change (negative value indicates convergence, positive value indicates divergence); Grey action quantity reflects the influence of system input on output; : Index term, used for short-term trend prediction.

[0090] Then, compensation instructions are generated: the input data for this step are the deviation trend results, current process parameters, and tool operating status. A fusion algorithm of fuzzy inference and recursive least squares is adopted. The fuzzy inference algorithm first performs a fuzzy evaluation of the deviation type (over-twist, under-twist, fluctuation, etc.) and severity to determine the strength of the compensation rule. Then, the recursive least squares algorithm is used to update the compensation parameters online in real time, so as to achieve dynamic and accurate correction of the output torque, angle and speed of the tightening tool. This effectively eliminates the cumulative error caused by factors such as material hardness fluctuation, tool wear, and temperature change, and significantly improves the stability and consistency of the tightening process.

[0091] The specific calculation formula is as follows:

[0092] The fuzzy inference formula is as follows:

[0093] ;

[0094] in: : Compensation intensity factor output by fuzzy inference (the value range is usually [0,1], which is used to ultimately determine the magnitude of the compensation). : No. The activation degree of a fuzzy rule (calculated by a membership function from inputs such as deviation trend, current torque deviation, and angle deviation, reflecting the degree of matching of the rule); : No. The output center value corresponding to each fuzzy rule; : The total number of activated fuzzy rules.

[0095] The formula for the recursive least squares parameter algorithm is as follows:

[0096] ;

[0097] ;

[0098] in: The compensation parameter vector updated at time k (including torque compensation increment) Angle compensation increment Speed ​​compensation coefficient wait); : The compensation parameter vector from the previous moment; : Regression vector (compensation strength factor output by fuzzy inference) (This is composed of factors such as current deviation trends, process stage characteristics, and tool status parameters). : Expected compensation output (the ideal deviation correction between the target torque / angle and the current actual measurement value); : Parameter estimation error covariance matrix (reflects the uncertainty of the compensation parameters, which gradually decreases and converges with iteration); The parameter estimation error covariance matrix of the previous time step; : Regression vector Transpose of; : Identity matrix; : Current discrete time step (corresponding to the millisecond-level real-time parameter acquisition cycle).

[0099] Finally, the effect is verified: the input for this step is the real-time parameters collected in the next cycle. The actual result after compensation is compared with the target range to determine whether the compensation meets the standard. If it does not meet the standard, the compensation parameters are iteratively optimized until the result is stable and qualified, ensuring that the final tightening accuracy meets the process requirements.

[0100] The real-time monitoring module's hardware components include a display driver unit, a curve rendering unit, a threshold comparison unit, an alarm output unit, and a status latch unit. The display driver unit drives the on-site display screen to output the image; the curve rendering unit draws the tightening curve point-by-point based on real-time parameters; the threshold comparison unit compares the real-time parameters with preset acceptable thresholds; the alarm output unit connects to an audible and visual alarm; and the status latch unit saves abnormal states until the system is reset. In actual workstations, this module allows operators to observe the work status in real time, achieving visual monitoring and rapid anomaly response. All thresholds in the real-time monitoring module are quantifiable thresholds specific to industrial scenarios. Examples of specific thresholds are as follows:

[0101] Torque acceptable threshold: Target torque 50 N·m, acceptable range 47.5 N·m~52.5 N·m;

[0102] Angle acceptable threshold: Target angle 90°, acceptable range 87°~93°;

[0103] Tool temperature threshold: Normal 0℃~50℃, alarm will sound if it exceeds 60℃;

[0104] Motor current threshold: Normal 2A~3.5A, alarm if it exceeds 5A;

[0105] Battery power threshold: Operation is prohibited when the battery level is below 20%.

[0106] The specific working steps of the real-time monitoring module are explained in detail below:

[0107] First, real-time curve generation is performed: based on continuously received real-time parameters, the tightening curve is refreshed and drawn point by point with time as the horizontal axis and torque / angle as the vertical axis. The curve has no delay, no stuttering, and no distortion, and can completely restore the entire tightening process. In actual scenarios, it can intuitively reflect whether the tightening process is normal.

[0108] Then, threshold comparison and intervention are performed: the current real-time parameters, curve slope, and curve shape are compared with the above-mentioned preset qualified thresholds and qualified curve models. If the parameters are within the qualified threshold range, normal execution continues and the monitoring status is updated; if the parameters are out of tolerance or the curve shape is abnormal, an audible and visual alarm is immediately triggered, and at the same time, a process pause command, an emergency stop command, and a prohibition on continuing tightening command are sent to the dynamic control decision module to forcibly stop the tightening action and prevent unqualified products from continuing to be assembled.

[0109] Finally, the results are automatically determined: after tightening, the final torque, final angle, curve characteristics and process status are comprehensively considered to determine whether the operation is qualified or unqualified. The determination result, key parameters, time information and tool information are sent to the data management module for storage, so as to realize automatic confirmation of results and reduce the error caused by manual judgment.

[0110] The communication module enables real-time data interaction and control command transmission between automated tightening tools and the tightening controller. It employs a hybrid communication architecture, working in conjunction with the real-time parameter acquisition module and the dynamic control decision module to ensure real-time performance and reliability. The hardware components of the communication module include a Wi-Fi 6 communication chip, an MQTT protocol processor, a WebSocket processor, a signal enhancement unit, a heartbeat detection unit, and a data distribution unit. The Wi-Fi 6 chip operates in the 5GHz industrial-grade frequency band, offering advantages such as strong anti-interference capabilities, high transmission rates, and low latency. The MQTT processor ensures reliable transmission of command and result data, the WebSocket processor provides low-latency transmission of high-frequency real-time data, the signal enhancement unit guarantees communication stability in complex workshop environments, the heartbeat detection unit periodically monitors the online status of the equipment, and the data distribution unit forwards different types of data to the corresponding modules. As the system's data interaction hub, the communication module can meet the needs of concurrent connections for multiple tools, high-density data transmission, and stable long-distance communication in practical scenarios. The communication module adopts a hybrid communication architecture, and its specific operating steps are detailed below:

[0111] Real-time data channel transmission: It adopts WebSocket direct connection mode, which is dedicated to carrying the upload of high-frequency acquisition parameters at the millisecond level, ensuring low latency and high throughput, and meeting the data requirements of real-time control and curve rendering;

[0112] Reliable command channel transmission: Adopting the MQTT protocol and equipped with an acknowledgment and retransmission mechanism, it is dedicated to carrying out the issuance and execution result feedback of start tightening commands, stop tightening commands, parameter configuration commands, lock commands, and unlock commands, ensuring that commands are not lost, duplicated, or out of order, and guaranteeing the reliable execution of control commands;

[0113] Status synchronization: The system periodically sends heartbeat frames to the automated tightening tool and receives heartbeat responses from the tool. If no response is received within 30 seconds, the device is considered offline. At the same time, the system synchronizes the online, offline, fault, running, and standby statuses of the device to maintain a stable communication connection throughout the system.

[0114] The system also includes an equipment health management module. This module's hardware is implemented using the tightening controller, eliminating the need for separate hardware. It reuses the main control unit, status acquisition unit, and storage unit, and implements health monitoring and proactive intervention through software logic. In practical scenarios, this module is used to proactively detect potential problems such as tool wear and tear, overheating, overload, and insufficient lifespan, preventing downtime due to sudden malfunctions during production. The specific working steps of the equipment health management module are detailed below:

[0115] First, status information is collected: the tool status parameters uploaded by the communication module are received, including battery level, body temperature, cumulative tightening count, running time, motor current, fault codes and other information. All information comes from the real-time parameter acquisition module and the tool's underlying hardware, and the data is real and reliable.

[0116] Then, health trend analysis is performed: continuously record tool performance data, compare it with historical normal ranges, analyze temperature change trends, current fluctuation trends, and lifespan decay trends, identify potential anomalies and early signs of failure, and achieve predictive maintenance.

[0117] Finally, intervention commands are issued: if the tool is in normal condition, the current working mode is maintained; if abnormal conditions such as excessive temperature, insufficient power, abnormal current, or excessive cumulative number of times are detected, tool lock command, load reduction command, parameter optimization command, and maintenance reminder command are automatically issued to the corresponding tool to restrict the continued use of the tool or prompt maintenance personnel to handle it in time to avoid the tool running with defects.

[0118] The system further integrates an industrial positioning error prevention module. This module works in conjunction with the dynamic control decision module, real-time parameter acquisition module, and communication module to receive real-time parameters and operational information, achieving a closed-loop error prevention system throughout the entire process. The hardware components of the industrial positioning error prevention module include a workstation identification unit, a part identification unit, a tool matching unit, a logic verification unit, and an interlock control unit. This module can interface with on-site RFID, barcode scanners, and visual recognition equipment. In practical applications, it is suitable for scenarios such as multi-model mixed-line production, multi-bolt sequential tightening, and multiple tools sharing workstations, preventing error-prone issues such as missed tightening, incorrect tightening, incorrect sequence tightening, wrong parts, and wrong tools from the source. The specific working steps of the industrial positioning error prevention module are detailed below:

[0119] First, acquire the job information: read the current workstation number, production plan, job sequence, part model, tool number, binding relationship, etc., to ensure that the job object is consistent with the process requirements;

[0120] Then, error prevention verification is performed: combining real-time parameter execution sequence control verification, part model verification, tool number verification, and process consistency verification, it is determined whether the bolt sequence is correct, whether the parts match, whether the tools are correct, and whether the process is consistent.

[0121] Finally, the closed-loop execution begins: if all error prevention checks pass, the tightening process is allowed to continue; if any check fails, an alarm is immediately triggered and the operation is suspended. At the same time, the abnormal information is notified to the dynamic control decision module, and the instructions to prohibit starting the tightening command, prohibit outputting the torque command, and prohibit continuing the operation take effect, forming a complete error prevention closed loop.

[0122] The data management module's hardware components include a database storage unit, a data retrieval unit, a statistical analysis unit, a data export unit, and a permission management unit. The storage unit uses MySQL to store business data, TDengine to store real-time time-series data, and Redis to cache frequently accessed data, ensuring large-capacity, high-speed, and long-term reliable storage. This module is used for production quality traceability, process optimization, and report output, and in practical scenarios, it can meet the compliance requirements of the automotive industry, such as lifetime traceability, factory quality analysis, and factory audits. The specific working steps of the data management module are detailed below:

[0123] Data storage throughout the entire process: Real-time parameter sequences, control command records, judgment results, workstation information, tool information, personnel information, time information, product serial number and other contextual information for each tightening operation are completely stored in the database, and each record can be uniquely traced;

[0124] Traceability query: Supports quick retrieval of historical data by product serial number, time range, workstation number, tool number, qualification status and other conditions. The query speed is fast, the results are complete, and one-click export is supported.

[0125] Statistical analysis: Perform statistical process control analysis on batches of historical data and calculate process capability indices. , It generates reports on pass rate trends, defect distribution, equipment status, and process stability, and feeds back the analysis results and optimization suggestions to the dynamic control decision module to continuously improve tightening quality and process level.

[0126] The specific formula for calculating the process capability index is as follows:

[0127] ;

[0128] ;

[0129] in: Process capability index (reflects the potential capability of the tightening process, without considering process center offset); Process capability index (considering center offset) (reflects the actual ability of the tightening process to meet process specifications); Specification upper limit (maximum acceptable value of torque or angle specified in the process document); : Lower limit of specifications (the minimum acceptable value of torque or angle specified in the process document); : Actual average value during the tightening process (calculated from a large number of historical real-time torque and angle parameters statistically analyzed by the data management module); Standard deviation of the tightening process (reflects the degree of fluctuation in batch tightening data, calculated from historical real-time parameters).

[0130] The tightening controller, as the core hardware of the system, consists of an industrial-grade host, multi-channel control interface, data interface, power management unit, heat dissipation unit, and explosion-proof and anti-interference housing. It can simultaneously connect to and manage multiple automated tightening tools. Multi-tool collaborative management is used in practical scenarios for parallel, sequential, and group tightening of multiple bolts at the same workstation, which helps improve assembly efficiency. The specific working steps of multi-tool collaborative management are detailed as follows: Multiple automated tightening tools at the same workstation are bound to their corresponding tightening tasks, bolt locations, and process parameters to ensure that tools and tasks are not mixed; each tool independently performs precise control, status monitoring, and anomaly detection based on its own real-time parameters, without interference between tools; the start, pause, end, and synchronized operation commands of multiple tools are uniformly scheduled according to a preset work sequence to ensure correct overall assembly sequence, optimal efficiency, and stable quality.

[0131] The system also includes a visual interaction module. The hardware components of this module include an industrial touchscreen, button units, indicator light drivers, a sound output unit, and a network access unit. Deployed at the workstation, the module is designed for operator use, featuring a simple interface, convenient operation, and rapid response. The specific working steps of the visual interaction module are as follows: real-time display of the current tightening process status, real-time tightening curve, production plan progress, current workstation information, pass / fail status, and alarm information, allowing operators to intuitively grasp the work situation. It also supports rapid workstation switching, work program selection, parameter viewing, anomaly calls, and rework confirmation, fully meeting on-site usage needs.

[0132] In this system, the modules work together through data interaction and command transmission to achieve real-time perception, dynamic decision-making, and precise control of the tightening process. Specific Implementation Example 2:

[0134] This embodiment provides an automated tightening tool precision control system based on real-time parameter acquisition. The difference from Embodiment 1 is that this embodiment adopts a workshop-level cluster deployment mode, which is suitable for intelligent manufacturing scenarios with multiple workstations, multiple production lines, and multiple vehicle models mixed production lines. The system adopts a centralized management and distributed control architecture. Data from all workstations is uniformly uploaded to the back-end server, and production plans are uniformly issued by the back-end, thereby realizing centralized monitoring, unified scheduling, and full traceability of the tightening process throughout the plant.

[0135] The system in this embodiment includes: a central management server, multiple workstation tightening controllers, multiple automated tightening tools, a real-time parameter acquisition module, a dynamic control decision module, a communication module, a real-time monitoring module, a data management module, an equipment health management module, an industrial positioning error prevention module, a visual interaction module, and a large-screen monitoring module.

[0136] The central management server is the core management unit of the entire workshop. It is used to uniformly store production plans, process SOPs, tightening parameters, real-time data and historical records, and synchronize processes and tasks to the tightening controllers at each workstation. Each tightening controller at each workstation still independently completes real-time data acquisition, closed-loop control, strategy switching, adaptive compensation and error prevention verification to ensure that the real-time control is not affected by the network.

[0137] The communication module adopts a hybrid networking of industrial Wi-Fi 6 and wired Ethernet. All workstations are connected to the same workshop LAN. Real-time data and control commands still use millisecond-level transmission channels to ensure no delay, no packet loss, and no conflict in multi-workstation concurrent scenarios.

[0138] The real-time parameter acquisition module is the same as in Example 1, which collects torque, angle, speed, current and tool status parameters at high frequency throughout the tightening process. The difference is that after the acquisition is completed, in addition to uploading to the tightening controller at this workstation, it will also be uploaded to the central management server through the communication module to achieve centralized data aggregation.

[0139] The dynamic control decision module still includes three strategies: torque control, angle control, and torque-angle composite control. It also has a built-in adaptive compensation unit. It uses an extended Kalman filter and a gray system prediction fusion algorithm to identify deviation trends and a fuzzy inference and recursive least squares fusion algorithm to generate compensation instructions, ensuring consistent tightening accuracy at each station.

[0140] In addition to displaying the tightening curve and status locally at the workstation, the real-time monitoring module also pushes the real-time images to the central management server. Managers can view the real-time tightening status, pass rate, equipment online rate, and abnormal alarm information of all workstations through the workshop large screen monitoring module, achieving global visualization.

[0141] The communication module supports parallel data upload from multiple workstations in cluster mode. The real-time data channel, reliable command channel, and heartbeat synchronization mechanism are consistent with those in Implementation Example 1, ensuring stable and reliable communication when multiple workstations and tools are connected concurrently.

[0142] The equipment health management module performs unified health monitoring on all tightening tools in the workshop, uploading data on each tool's power consumption, temperature, current, cumulative tightening cycles, and fault information to the server. This creates a plant-wide equipment ledger and maintenance plan. In case of an anomaly, alarms are simultaneously triggered at the local workstation and on the main screen in the control room. Figure 6 The diagram shows a user management interface of the system.

[0143] The industrial positioning error prevention module supports unified error prevention for multiple vehicle models, multiple processes, and multiple bolt sequences in cluster mode. The back-end server pre-configures the processes for each vehicle model and distributes them to the corresponding workstations via the network to prevent problems such as incorrect assembly, missing assembly, incorrect sequence, and wrong tools during mixed-line production.

[0144] The data management module is deployed on the central management server, which uniformly stores the tightening data of all workstations, tools and products throughout the entire plant. It supports batch querying, statistical analysis and SPC process capability calculation by conditions such as workshop, production line, workstation, batch, serial number and time. It generates a plant-wide quality report and feeds it back to the dynamic control decision module to achieve unified optimization of the process throughout the workshop.

[0145] Multi-tool collaborative management is extended to multi-workstation collaborative scheduling in cluster mode. The backend can uniformly allocate tasks according to the production progress and automatically switch workstation operation programs to achieve overall line cycle balance and efficient production.

[0146] In addition to local operation, the visual interaction module also supports remote viewing, remote parameter calibration, and remote anomaly handling. Managers can intervene in production line anomalies in real time from the office, improving response efficiency.

[0147] This embodiment achieves unified management and control of multiple workstations, centralized data aggregation, full-process quality traceability, and global monitoring of the production line through cluster deployment without changing the real-time acquisition, precise control, adaptive compensation, and error prevention logic.

[0148] like Figure 7 The system provides a fault management list interface diagram, which is a list of alarm logs / abnormal work orders for tightening tools (such as electric wrenches and torque wrenches), used for centralized management of equipment faults and risk events; such as Figure 8 This invention provides a schematic diagram of the online tightening quality analysis interface of the system, aiming to help quality engineers / maintenance personnel quickly locate the main workstations causing tightening quality problems, prioritize the resolution of concentrated issues, and efficiently improve overall assembly quality; such as Figure 9 This invention provides a schematic diagram of the quality defect analysis interface of the system, helping staff quickly locate high-risk equipment and supporting quality management decisions: prioritizing the handling of equipment such as Equipment 1 and Equipment 5, which have a high number of non-compliance (NG) occurrences and large fluctuations, and arranging special maintenance to investigate issues related to sensors, torque control, and communication. (See attached diagram for further explanation.) Figure 4 To be continued Figure 9 The system is centrally displayed in practical applications.

[0149] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising a reference structure" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0150] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A precision control system for an automated tightening tool based on real-time parameter acquisition, characterized in that, It includes a tightening controller, an automated tightening tool, a real-time parameter acquisition module, a dynamic control decision module, a communication module, a real-time monitoring module, and a data management module; The real-time parameter acquisition module is installed inside the automated tightening tool and is used to acquire torque, angle, speed, current and tool status parameters at high frequency in real time throughout the tightening operation. The acquired real-time parameters are then sent to the tightening controller via the communication module. The dynamic control decision module is located in the tightening controller and is used to receive real-time parameters transmitted by the communication module, perform dynamic analysis of the tightening process, and generate and send control commands to the automated tightening tool in real time based on the analysis results, so as to perform closed-loop precise adjustment of the operating parameters of the tightening tool. The communication module is used to realize real-time data interaction and control command transmission between the automated tightening tool and the tightening controller; the real-time monitoring module is used to generate a tightening curve based on the real-time parameters and perform status monitoring. The data management module is used to store data throughout the entire process and support traceability and analysis; the modules work together through data interaction and command transmission to achieve real-time perception, dynamic decision-making and precise control of the tightening process.

2. The precision control system for an automated tightening tool based on real-time parameter acquisition according to claim 1, characterized in that: The specific working steps of the real-time parameter acquisition module include: SpA1, Initialization Acquisition: Before the tightening operation begins, acquire the tool's baseline status parameters and complete the connection verification with the tightening controller; SpA2, High-frequency dynamic acquisition: During the tightening process, torque, angle, speed, current and tool status parameters are continuously acquired at fixed millisecond intervals; SpA3, Real-time Upload: Each set of collected parameters is packaged in real time and sent to the tightening controller via the communication module; The specific working steps of the dynamic control decision module include: SpB1, Parameter Reception and Preprocessing: Receives real-time parameters transmitted by the communication module and performs validity verification and noise filtering; SpB2, Strategy Matching and Decision-Making: Based on the current process stage and parameter deviation, select or switch the most suitable strategy from the preset torque control strategy, angle control strategy and torque-angle composite control strategy. SpB3, Command Generation and Issuance: Calculates control deviation, generates precise adjustment commands, and issues them to automated tightening tools via the communication module to achieve real-time adjustment of operating parameters.

3. The precision control system for an automated tightening tool based on real-time parameter acquisition according to claim 2, characterized in that: The dynamic control decision module also includes an adaptive compensation unit, which receives real-time parameters transmitted by the communication module, identifies parameter deviation trends in real time, and automatically generates compensation instructions. Its specific working steps include: SpC1, Deviation Trend Identification: After receiving real-time parameters from multiple cycles, the extended Kalman filter and gray system prediction fusion algorithm are used to analyze the torque and angle deviation trends; SpC2, Compensation Instruction Generation: The compensation parameters are automatically generated using a fusion algorithm of fuzzy inference and recursive least squares, and the tool output is dynamically corrected. SpC3, Effect Verification: Receive real-time parameters for the next cycle, verify the compensation effect, and perform iterative optimization.

4. The precision control system for an automated tightening tool based on real-time parameter acquisition according to claim 1, characterized in that: The specific working steps of the real-time monitoring module include: SpD1, Real-time Curve Generation: Continuously generates and updates the tightening curve based on the received real-time parameters; SpD2, Threshold Comparison and Intervention: The current parameters and curve shape are compared with the preset qualified threshold. When the parameters and curve shape are within the qualified threshold range, the normal tightening process continues and the monitoring status is updated. When the qualified threshold range is exceeded, an audible and visual alarm is immediately triggered and a process pause command is sent to the dynamic control decision module. SpD3, Automatic Result Judgment: After tightening is completed, the system automatically determines whether the operation is qualified or unqualified based on the final parameters, and sends the judgment result to the data management module.

5. The precision control system for an automated tightening tool based on real-time parameter acquisition according to claim 1, characterized in that: The communication module adopts a hybrid communication architecture, and its specific working steps include: SpE1, Real-time Data Channel Transmission: Supports millisecond-level high-frequency parameters being uploaded from automated tightening tools to the tightening controller; SpE2, Reliable Command Channel Transmission: A confirmation and retransmission mechanism is adopted to ensure that control commands are reliably sent from the tightening controller to the automated tightening tool and return execution feedback; SpE3, Status Synchronization: Regularly perform online status synchronization and heartbeat detection of devices to maintain communication connection stability.

6. The precision control system for an automated tightening tool based on real-time parameter acquisition according to claim 1, characterized in that: The system also includes a device health management module, which receives tool status parameters transmitted by the communication module to continuously monitor and proactively intervene in the tool status. Its specific working steps include: SpF1, Status Information Acquisition: Receives battery level, temperature, and cumulative tightening count tool status parameters periodically reported by the automated tightening tool; SpF2, Health Trend Analysis: Continuously monitor tool performance data and identify potential abnormal trends; SpF3, Intervention Command Issuance: When the tool is in normal condition, it maintains the current working mode. When an anomaly is detected, it automatically issues lock, parameter optimization, or maintenance reminder commands to the corresponding tool.

7. The precision control system for an automated tightening tool based on real-time parameter acquisition according to claim 1, characterized in that: The system further integrates an industrial positioning and error prevention module, which receives real-time parameters and operational information to achieve a closed-loop error prevention system throughout the entire process. Its specific working steps include: SpG1, Job Information Acquisition: Acquire the job sequence, part identification, and tool matching information for the current workstation; SpG2, Error Prevention Verification: Combines real-time parameter execution sequence control, part error prevention, tool error prevention, and process consistency verification; SpG3, Closed-loop execution: The tightening process is allowed to continue when all error prevention checks pass, and an alarm is triggered and the operation is suspended when any error prevention check fails.

8. The precision control system for an automated tightening tool based on real-time parameter acquisition according to claim 1, characterized in that: The specific working steps of the data management module include: SpH1, Full-process data storage: The real-time parameters, control command records, judgment results and context information for each tightening are completely stored in the database; SpH2, Traceability Query: Supports quick retrieval of historical data by serial number, time, and workstation conditions; SpH3, Statistical Analysis: Performs statistical process control analysis on batch data, generates process capability reports, and provides feedback to the dynamic control decision module for process optimization.

9. The precision control system for an automated tightening tool based on real-time parameter acquisition according to claim 1, characterized in that: The tightening controller supports multi-tool collaborative management. This multi-tool collaborative management function is achieved through the collaborative operation of the dynamic control decision module, the communication module, and the real-time parameter acquisition module. Its specific working steps include: Multiple automated tightening tools at the same workstation are bound to corresponding tightening tasks; precise control and status monitoring are performed independently based on the real-time parameters of each tool; multiple tools work collaboratively according to the work sequence.

10. The precision control system for an automated tightening tool based on real-time parameter acquisition according to claim 1, characterized in that: The system also includes a visualization interaction module, which receives information transmitted from the real-time monitoring module and the data management module and provides on-site operation visualization and human-machine interaction. Its specific working steps include: real-time display of tightening process status, tightening curve, production plan and alarm information, and supports operators to switch workstations, select work programs and make calls.

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