Fixture identity recognition and process parameter automatic issuing method and system based on Internet of Things

Abstract

The invention discloses a clamp identity recognition and process parameter automatic issuing method and system based on the Internet of Things, and is suitable for the discrete manufacturing fields of machining, assembling and the like. The system comprises a clamp side acquisition assembly, an edge gateway, a process parameter library and an equipment controller. The method comprises the following steps: S1, identifying clamp types and collecting working conditions; s2, applying strong correlation perturbation excitation by the controller according to the type of the actuating mechanism and acquiring response; s3, extracting dynamic characteristics and comparing the dynamic characteristics with the baseline model to judge the effectiveness of the fixture; s4, performing consistency compiling on the process parameter packet; s5, the parameters are written into a temporary storage area, and a minimum no-load verification action set bound with the process section is executed; and S6, if the verification is passed, the atoms are switched to an effective region and processing enabling is opened. Through a closed-loop mechanism of dynamic physical fingerprint type effectiveness judgment, consistency compiling, minimum verification, temporary storage effectiveness and rollback, the probability that a clamp fails and parameters are wrongly mapped to enter an automatic cycle is reduced, and the first workpiece qualification rate and the production line stability are improved.

Description

Technical Field

[0001] This invention relates to the field of discrete manufacturing, such as machining and assembly, and in particular to a method and system for fixture identification and automatic distribution of process parameters based on the Internet of Things. Background Technology

[0002] In discrete manufacturing (such as machining and assembly), tooling fixtures are crucial for ensuring workpiece positioning accuracy, consistency of repeated clamping, and machining safety. They are frequently switched based on work orders, process stages (drilling, milling, assembly, etc.), and equipment models. With increasing demands for digitalization, networking, and flexibility in manufacturing, production lines commonly employ MES / production line control, industrial IoT communication, and parameter library management to enable work order-driven program calls and process parameter distribution. However, in real-world environments with multiple fixtures, devices, and protocols, issues such as fixture misuse, failure to promptly identify fixture degradation, incorrect parameter mapping, and parameters not being properly applied before processing commence, leading to batch quality risks, equipment collision risks, and increased downtime and rework costs. Therefore, how to achieve closed-loop verification of fixture "availability / effectiveness" and process parameter "correct application" without excessively increasing implementation complexity remains a critical engineering challenge in the implementation of smart manufacturing.

[0003] In the existing technology, there are many solutions for "remote configuration and parameter distribution of production line equipment". For example, Chinese patent document CN112987667B provides a remote configuration system and method for flexible manufacturing production line equipment based on OPCUA. Its overall architecture consists of a remote host, a field host, and field production line equipment. The remote host generates production line equipment configuration information according to production needs and sends it to the field host. The field host then distributes the configuration information to each field device and receives the feedback of operating status information, realizing remote management and closed-loop monitoring of equipment configuration. This type of solution is significant for addressing issues such as centralized configuration management, cross-device information modeling, and remote distribution. However, its focus is usually on the equipment information model, configuration distribution channels, and status feedback mechanisms. The validity of the fixture, the "end-point execution object," often relies on static identification or manual confirmation. Furthermore, after distribution, the main closed-loop criterion is usually "configuration distribution successful / equipment status normal," which makes it difficult to directly answer critical questions closer to the actual work, such as "whether the parameters are correctly mapped and effective on the controller side," "whether the fixture is in a healthy and usable state under the current working conditions," and "whether it is necessary to perform minimum no-load verification actions for drilling / milling / assembly to avoid erroneous processing."

[0004] Another type of existing technology focuses on "identification and traceability of manufactured objects," such as introducing RFID / barcode identification media into intelligent production lines to achieve automatic association between workpiece information, work order information, and processing flow. Patent document CN109396863A discloses an intelligent manufacturing production line for cutting processes and its operating method. This production line includes an industrial robot unit, a digital three-dimensional material storage unit, a processing unit, and an intelligent production line control system. The digital three-dimensional material storage unit is equipped with RFID electronic tags at its locations to record processing information. RFID readers are installed on the robot fixtures to read and write RFID tag information and are connected to the intelligent production line control system, thereby achieving automatic reading of processing information and collaborative control of the production line. This type of solution can improve the automation level of logistics and information flow, help reduce manual input errors, and improve traceability efficiency. However, in situations involving multiple fixture switching, simply ensuring that "the fixture / workpiece identification is correctly read" is insufficient to guarantee that "the fixture is truly effective and usable." For example, hydraulic circuit leaks, vacuum suction rebound, increased actuator hysteresis, and dynamic response drift caused by wear of positioning elements are often undetectable at the RFID tag level. If these issues are still automatically issued according to the established process and processing is directly permitted, it may lead to batch defects or equipment malfunctions in a short period of time. In other words, identification and traceability technologies address the question of "who / which task," while fixture effectiveness and minimum process validation address the question of "whether it can be reliably executed / whether it is suitable for the current process segment." The two are not equivalent at the engineering level.

[0005] Furthermore, there are also related technical disclosures regarding fixture clamping quality monitoring and machining constraint determination. For example, patent document US10232481B2 describes a fixture structure for machining multifaceted prism-shaped workpieces, mentioning the use of clamping force monitoring to determine whether the workpiece is in the correct position and whether the constraint level meets the machining requirements. This type of technology indicates that "clamping force / clamping status signal" has significant value in fixture scenarios, and can be used to determine whether the clamping is in place and whether the constraint is sufficient, thereby reducing the risk of machining deviation or workpiece deformation. However, from the perspective of manufacturing systems, such solutions usually focus on the monitoring and determination of the fixture body structure and clamping process, and do not systematically use "dynamic characteristics strongly correlated with the type of fixture actuator" for fixture effectiveness determination, nor are they deeply coupled with the entire closed-loop process of "automatic process parameter distribution - controller temporary storage - minimum no-load verification action set of process segment - enabling machining after verification." Especially in flexible production lines, the types of fixtures are complex (hydraulic fixtures, vacuum fixtures, electric fixtures, etc.) and the operating conditions change frequently (air / oil supply pressure, temperature, load, wear). If only static thresholds or a single clamping force reading are used for judgment, it is often difficult to balance the false positive rate and the false negative rate. At the same time, the requirements and cost constraints of the "minimum verification action" for drilling, milling, assembly and other process sections are different. The lack of a minimum no-load verification set for process sections may still lead to hidden dangers such as "parameters have been issued but the mapping or interlocking conditions do not match" and "the fixture action sequence is inconsistent with the actual actuator characteristics" only being exposed during formal processing.

[0006] In summary, while existing technologies have made progress in remote configuration issuance, production line identification, traceability and collaborative control, and fixture clamping monitoring, they still have shortcomings in the manufacturing-specific closed loop of "fixture validity determination, parameter consistency compilation and mapping, minimum no-load verification action set bound to the process segment, and enabling machining only after passing the verification." Firstly, fixture health status and validity are often not included in the strong constraint determination before parameter issuance. Secondly, parameter mapping and interlock dependencies under multiple protocols / controllers lack systematic consistency verification oriented towards manufacturing constraints, easily leading to incorrect field mapping or missing interlocks. Thirdly, after issuance, there is a lack of a minimum cost verification action set corresponding to the drilling / milling / assembly process segment, causing errors to be discovered only in the formal machining stage. To solve these problems, it is necessary to provide a fixture identification, determination, and parameter issuance closed-loop solution that is closer to the electromechanical characteristics and process segment differences of the manufacturing site, in order to reduce the risk of fixture misuse and incorrect machining and improve changeover efficiency and machining consistency. Summary of the Invention

[0007] This invention aims to provide an IoT-based method and system for determining the effectiveness of fixtures and automatically issuing process parameters for discrete manufacturing production lines with multiple fixtures and multiple process segments. By performing perturbation excitation and dynamic feature identification according to the type of fixture actuator, and combining it with the minimum no-load verification action set corresponding to the drilling / milling / assembly process segment, a rapid closed-loop verification of the fixture status and parameter matching before and after issuing process parameters can be achieved. This reduces the risk of incorrect processing caused by fixture misuse, fixture failure, and incorrect parameter issuance, and improves the changeover efficiency and processing consistency of the production line.

[0008] To achieve the above-mentioned technical objectives, the technical solution of the present invention is as follows.

[0009] A method for fixture identification and automatic distribution of process parameters based on the Internet of Things (IoT), comprising:

[0010] S1, the controller selects perturbation excitation according to the type of clamping actuator and collects the response, forming a dynamic feature vector of the clamp and comparing it with the baseline model to determine the effectiveness of the clamp. The perturbation excitation of the hydraulic clamp is the pressure step and the leakage rate feature is extracted. The perturbation excitation of the vacuum clamp is the evacuation / release step and the evacuation time constant and rebound feature are extracted. The perturbation excitation of the electric clamp is the displacement micro-stroke jitter and the stiffness and hysteresis features are extracted.

[0011] S2, after passing the judgment, generates a process parameter package according to the work order and equipment capacity and compiles it into variable mapping and interlocking conditions in a consistent manner;

[0012] S3, after the parameters are sent to the controller's temporary storage area, execute the minimum no-load verification action set corresponding to the process segment. The drilling segment must include at least tool setting / deep jogging, the milling segment must include at least low-speed idling and tool feed test run, and the assembly segment must include at least fixture action sequence test run and positioning verification. Only after the verification is passed can machining be enabled; otherwise, rollback and anomaly report will be sent.

[0013] As a further improvement, the perturbation excitation is any one or a combination of clamping / releasing pulses, pressure or vacuum setpoint steps, and displacement micro-stroke jitter, and includes frequency scanning or phase perturbation to form the dynamic transmission characteristics of the fixture.

[0014] As a further improvement, the dynamic feature vector includes at least one or a combination of rise time, overshoot, steady-state error, frequency domain peak / bandwidth, and hysteresis loop area, and outputs the judgment result with a similarity threshold and confidence level.

[0015] As a further improvement, the fixture baseline model includes fixture number, workstation number, and temperature / gas supply pressure condition labels. The edge gateway adaptively updates the threshold based on fixture wear or condition drift and records the version.

[0016] As a further improvement, the consistency compilation includes converting the parameter package into an intermediate representation (IR), performing unit consistency, upper and lower bound boundary, interlock dependency and execution order constraint checks at the IR layer, and outputting mapping results for OPCUA nodes, Modbus registers or controller internal variable tables.

[0017] As a further improvement, the no-load verification process further includes performing low-speed jogging or idling verification on the spindle / axis system, and comparing the collected current, vibration or position following error with the allowable window derived from the parameter package.

[0018] Another aspect of the present invention provides an IoT-based fixture identification and automatic process parameter distribution system, comprising: a fixture-side acquisition component, an edge gateway, a process parameter library, and an equipment controller; wherein the fixture-side acquisition component is used to acquire clamping pressure / vacuum degree / displacement response; the edge gateway is used to generate perturbation excitation commands, extract dynamic feature vectors and compare them with a baseline model, generate process parameter packages and compile them into variable mappings and interlocking conditions; the equipment controller is used to receive parameters to a temporary storage area and execute an idle verification process, and only after successful verification is machining enabled.

[0019] As a further improvement, the edge gateway includes a dynamic identification module and a model management module. The dynamic identification module is used for time-domain / frequency-domain feature extraction, and the model management module is used for baseline model versioning, operating condition label association, and wear drift update. The edge gateway also includes a consistency compilation module, which is used to generate intermediate representations (IR) and complete unit consistency, boundary and interlock dependency verification, and output a consistent variable mapping table and interlock configuration across protocols / controllers. The device controller includes a temporary-effective switching module and a rollback module, which are used to restore the parameter version before the switch when the no-load verification fails, and generate an abnormal event record containing fixture judgment results, verification curve deviation, and failure reasons, which is then uploaded to the upper system.

[0020] A third aspect of the present invention provides a computer device including a processor, a graphics processing unit (GPU), and a memory, wherein the memory stores a computer program that, when executed by the processor and the GPU, causes the computer device to perform the method described thereon.

[0021] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a computer device, causes the computer device to perform the method described thereon.

[0022] By employing the aforementioned technical solutions, this invention achieves multi-dimensional and quantifiable comprehensive technical effects on the manufacturing site: First, for different actuator types such as hydraulic clamps, vacuum clamps, and electric clamps, a "usability fingerprint" for the clamps is constructed based on dynamic characteristics such as pressure step-leakage rate, evacuation / release step-time constant and springback, and displacement micro-jitter-stiffness and hysteresis. This allows for early rejection when a clamp is correctly identified but actually has hidden dangers such as leakage, springback, jamming, wear drift, etc., effectively reducing the risk of batch dimensional deviations, surface defects, and collision downtime caused by clamp misuse and using faulty clamps. Second, by encapsulating process parameters in an intermediate representation layer to complete the compilation and verification of unit consistency, upper and lower limit boundaries, interlock dependencies, and execution order constraints, and outputting mapping results consistent with the OPCUA / register / controller variable table, the complexity of heterogeneous equipment and multi-protocol environments can be significantly reduced. This invention addresses issues such as incorrect field mapping, missing interlocks, or partially effective parameters, improving the consistency and traceability of parameter issuance. Furthermore, after parameter issuance, a minimum no-load verification action set matching the drilling, milling, and assembly process sections is introduced (e.g., tool setting / deep jogging, low-speed idling and tool travel trial runs, fixture action sequence trial runs and positioning verification). Without significantly increasing cycle time, this allows for rapid joint inspection of "fixture dynamic characteristics—action sequence—parameter mapping—interlock conditions" at the lowest cost, enabling automatic rollback and reporting of cases where conditions are not met, preventing errors from entering the formal machining stage. Therefore, this invention significantly reduces the probability of incorrect machining and line stoppages / rework while improving changeover efficiency and automated issuance capabilities, increasing first-piece pass rate, machining consistency, and production line stability, and enhancing the engineering value of fixture lifecycle health management and abnormal closed-loop handling. Attached Figure Description

[0023] Figure 1 This is a structural block diagram of the IoT-based fixture validity determination and process parameter automatic distribution system of the present invention.

[0024] Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0026] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of the present invention.

[0027] I. Explanation of Terms and Key Concepts

[0028] Explanation of commonly used terms:

[0029] 1. Types of clamping actuators: The type of power and actuation method by which clamps perform clamping / releasing actions, including at least three types: hydraulic clamps, vacuum clamps, and electric clamps. Different types correspond to different typical failure modes (leakage, springback, jamming, hysteresis, etc.) and different observable dynamic characteristics.

[0030] 2. Fixture Effectiveness: The ability of a fixture to stably complete the expected clamping / positioning / release actions under the current workstation and operating conditions, while meeting the requirements for safety interlocking and machining accuracy. Fixture effectiveness is not determined solely by the identification ID, but should be comprehensively judged in conjunction with the dynamic response of the actuator.

[0031] 3. Perturbation excitation: The controller applies small-amplitude, short-duration or controllable excitation actions to the fixture actuator under no-load or low-risk conditions to obtain the dynamic response of the pneumatic-hydraulic circuit or electromechanical transmission chain, so as to achieve identification and health determination.

[0032] 4. Dynamic feature vector: The feature set extracted from the perturbation excitation response curve, including general features (rise time, overshoot, steady-state error, noise level, etc.) and type-specific features (hydraulic leakage rate, vacuum evacuation time constant, rebound characteristics, electrodynamic stiffness and hysteresis, etc.).

[0033] 4. Baseline Model: A dynamic characteristic statistical model for a specific fixture under healthy / qualified conditions and within the known operating condition label range, including version number, applicable window, and threshold strategy. Subsequent judgments are based on the "degree of deviation from the baseline model".

[0034] 5. Consistent compilation: Convert the process parameter package into an intermediate representation (IR), and perform unit consistency verification, boundary constraint verification, interlock dependency verification, and execution order constraint verification at the IR layer. Then, output the variable mapping and interlock configuration for the target controller, so that "parameter writing - interlock logic - verification rules" remain consistent under multiple devices and multiple protocols.

[0035] 6. Minimum No-Load Verification Action Set: A set of minimal actions bound to a specific process section to verify whether the "fixture action sequence + key parameter mapping + interlocking conditions" are correctly effective, achieving high-confidence verification with the lowest possible cycle time cost.

[0036] 7. Temporary Storage Area / Effective Storage Area: A logical area within the controller used for parameter writing and effect switching. The temporary storage area receives new parameters and performs no-load verification; the effective storage area contains the parameter version used in formal processing.

[0037] 8. Rollback: When no-load verification fails, write exception occurs, or switchover exception occurs, the controller reverts to the previous effective version and enters a safe state. At the same time, it reports the exception event record to prevent incorrect parameters from entering formal processing.

[0038] II. Specific Implementation of System Structure

[0039] 2.1 System Composition (e.g.) Figure 1 (As shown)

[0040] like Figure 1 As shown, the system of the present invention includes:

[0041] (1) Fixture-side data acquisition component

[0042] Pressure sensor: range 0–16MPa, accuracy 0.5%FS, sampling frequency 100–500Hz; Vacuum sensor: range -100kPa to 0kPa, sampling frequency 100–500Hz; Displacement / stroke sensor: resolution 0.01mm; The fixture-side acquisition component can also be equipped with fixture type identification components, such as interface coding plugs, valve group model identification, fixture ID codes (QR codes / RFID / mechanical codes), etc., to assist in S1 type determination and traceability.

[0043] (2) Edge gateway

[0044] Used for: data acquisition and aggregation, curve preprocessing, dynamic feature extraction, baseline model management, parameter package generation and consistency compilation, no-load verification and judgment, and event logging and reporting. The edge gateway can be an industrial PC or an embedded gateway, equipped with an industrial Ethernet port, fieldbus interface, and local storage. The software should include at least: Acquisition service: subscribing to sensor / controller data; Feature service: performing filtering, alignment, and feature calculation; Model service: maintaining baseline model version and thresholds; Compilation service: IR generation, constraint verification, and mapping output; Event service: event code generation, storage, and reporting.

[0045] (3) Process parameter library / supervisor system

[0046] This system stores parameter templates, interlock rules, variable naming constraints, version records, etc., organized by work order, process segment, equipment capacity, and fixture type. It can be deployed on the MES / SCADA side or in a dedicated parameter service. It adopts a "template + differential" organization method: Templates describe the default parameters, interlock dependencies, and verification rules of a standard process segment; Differentials record the upper limit of single equipment / single workstation capacity, fixture type differences, material differences, etc.

[0047] (4) Equipment controller

[0048] It can be used with PLCs, CNCs, robot controllers, etc. It is used to execute perturbation excitations, receive parameters into a temporary storage area, execute the minimum no-load verification action set, complete the temporary storage-to-effective switching, rollback, safety locking, and status feedback. The controller side has "low-risk action modes," such as tool setting, jogging, low-speed idling, tool travel trial run, and no-load clamping cycle, and also supports "automatic cycle enable gating," meaning that automatic machining cannot begin unless verification is passed.

[0049] (5) Communication and time synchronization

[0050] Components can communicate using one or a combination of OPCUA, Modbus-TCP, EtherNet / IP, or vendor-specific protocols. This invention selects the edge gateway and controller for time synchronization (NTP or IEEE 1588) and assigns a unique task number to each perturbation and authentication. .

[0051] III. Specific Implementation of Methods and Steps (e.g., should) Figure 2 (As shown)

[0052] S1: Fixture type identification and working condition acquisition

[0053] S1.1 Fixture Type Identification

[0054] The controller selects at least one or more of the following information sources for identification: fixture interface code: for example, hydraulic / vacuum / electric type is determined by plug code or terminal code; valve group I / O point configuration: different sets of hydraulic valve / vacuum valve / servo drive points; fixture station parameters: fixture type recorded in the station configuration file; fixture ID: QR code / RFID / mechanical code, bound to the fixture type.

[0055] The "multi-source consistency" strategy is adopted: multiple samples are taken within the recognition time window. If the consistency ratio is lower than the threshold (e.g., 80%), the recognition is judged to be unstable, the "requires verification" status is output and the entry into S2 is blocked; if a conflict occurs (e.g., the interface code indicates hydraulic pressure but the valve group feedback is vacuum), the "type inconsistency" event code is directly output.

[0056] S1.2 Operating Condition Data Acquisition and Tagging

[0057] Before entering the perturbation excitation, the following operating parameters were collected: temperature. Oil supply pressure Gas supply pressure Power supply voltage ,humidity These are used to create operating condition labels. For example:

[0058] Operating condition tags: .

[0059] Operating condition labels are used to select the baseline model version or threshold strategy. For example, the evacuation time constant of a vacuum fixture. Sensitive to temperature and pump start-up status; a wider window is allowed at low temperatures or when the pump is just starting up; hydraulic clamp leakage rate. Sensitive to oil temperature and viscosity, can be based on Apply threshold correction.

[0060] S1.3 Safety Pre-lock Interlock and Perturbation Mode Limitation

[0061] The controller performs safety checks at the S1 end: access control closure, emergency stop reset, spindle stop, axis stop, personnel presence confirmation, etc. Limitations in perturbation mode: upper limit of hydraulic pressure, upper limit of vacuum, upper limit of electric micro-stroke amplitude; upper limit of total perturbation duration (e.g., 2–5s); triggering is only permitted in "no-load / low-risk" states (e.g., automatic cycle not started).

[0062] Output: Fixture type, fixture ID, working condition label, work order number, process section type, equipment identifier, task number .

[0063] S2: Apply perturbation excitation according to the actuator type and collect the response (as shown in Figure 3).

[0064] S2.1 Hydraulic clamp:

[0065] The controller performs a pressure step in perturbation mode, changing the pressure from Step to and maintain Collect pressure curves To maintain the leakage rate characteristics of the retention segment, a logarithmic decay form is used:

[0066] ;

[0067] in: Leakage rate characteristics ( ); Maintain the initial pressure of the section (MPa); Maintain the end pressure of the section (MPa); Hold time (s).

[0068] To enhance robustness, the pressure holding slope can be calculated in parallel. With steady-state fluctuations :

[0069] ;

[0070] To maintain the standard deviation of segment pressure.

[0071] when or When the value is significantly higher than normal, it often corresponds to problems such as aging seals, pipeline leakage, or internal leakage of the valve core.

[0072] S2.2 Vacuum clamps: Evacuation / release step and evacuation time constant and springback characteristics.

[0073] The controller performs a vacuum step and collects the vacuum level. (kPa, negative values ​​are for vacuum). Exponential model estimates the pumping time constant. :

[0074] ;

[0075] in: : Evacuation begins to establish vacuum; Steady-state vacuum degree; : Evacuation time constant (s).

[0076] rebound characteristics It can be defined as the time window after the discharge. The amplitude or rate of vacuum recovery within the vacuum.

[0077] or .

[0078] when Significantly increased or Abnormalities may correspond to issues such as aging suction cups, air leakage, workpiece surface contamination, failure of partition valves, or decreased efficiency of vacuum pumps.

[0079] S2.3 Electric clamp: Displacement micro-stroke jitter + stiffness and hysteresis characteristics;

[0080] The controller applies micro-stroke jitter to the displacement and collects the displacement. With motor current And estimate the clamping force based on the motor constant. Alternatively, a force sensor can be used directly to estimate the equivalent stiffness. :

[0081] ;

[0082] in: Equivalent stiffness (N / mm); : Changes in internal force during micro-strokes (N); Displacement change (mm).

[0083] Hysteresis area Defined as a period The area enclosed by the curve reflects friction, clearance, and jamming. Reduced stiffness or increased hysteresis often indicates increased lead screw clearance, guide rail wear, contamination of the clamping surface, or loose jaw assembly.

[0084] S2.4 Sampling, Filtering and Trigger Alignment

[0085] Sampling rate 100–500Hz. To avoid comparison discrepancies caused by inconsistent action start times, the edge gateway can use trigger point alignment: for example, pressure exceeding... Or vacuum reaches Time as Filtering can employ low-pass or Kalman filtering, and outlier removal can be based on the three-standard-deviation principle. It is recommended to archive the original curve, the filtered curve, and intermediate values ​​from feature calculations together for diagnostic and traceability purposes.

[0086] S3: Dynamic Feature Vector Extraction and Fixture Validity Determination

[0087] S3.1 Feature Vector Construction

[0088] For different fixture types, a combination of "general features + special features" is used: General feature: rise time Overshoot Steady-state error ,noise Special features: Hydraulic , , ;vacuum , ;electric , .form: .

[0089] S3.2 Comparison with baseline model;

[0090] Using Mahalanobis distance Measuring the degree of deviation:

[0091] ;

[0092] in: Baseline mean vector; Baseline covariance matrix; Distance metric; the smaller the value, the closer to the baseline. For ease of engineering demonstration, confidence levels can be mapped. And output the level.

[0093] S3.3 Judgment Rules and Grading Strategies

[0094] 1) When : Determined to be "valid";

[0095] 2) When If the system determines that "review is required", a more stringent set of no-load verification actions will be enforced or a manual confirmation will be prompted.

[0096] 3) When If the message is deemed "invalid," the transmission will be blocked and an anomaly will be reported.

[0097] threshold , It can be set based on the quantile of the calibrated sample, such as the 95th percentile or the 99th percentile, and adjusted according to the wear cycle of the fixture.

[0098] S3.4 Operating Condition Correction and Drift Management

[0099] When the operating condition label exceeds the baseline applicable window, temperature-sensitive characteristics (such as vacuum) can be adjusted. Threshold corrections are applied to avoid misjudging normal temperature drift as failure; for indicators showing a trend of deterioration (such as... Continue to rise Continuously increasing (Continuously increase) Output maintenance warnings. The output results should include "dominant anomaly factors" and "evidence fragments" to improve interpretability and maintainability.

[0100] S4: Generation and Consistent Compilation of Process Parameter Packages (as shown in Figure 3)

[0101] This step addresses issues that can arise when distributing data across multiple devices, protocols, and controllers, such as "incorrect field mapping, mixed units, missing interlocks, and sequence conflicts," ensuring that parameters can be correctly written to the controller and have verifiable constraints.

[0102] S4.1 Process Parameter Package Generation

[0103] The edge gateway retrieves templates from the parameter library and generates a process parameter package based on the work order number, process segment type (drilling / milling / assembly), equipment capacity constraints (spindle maximum speed, axis travel, tool magazine capacity, fixture interface capability, etc.), and fixture type. The parameter package must include at least: program references: NC subroutine number or robot motion script version; fixture motion sequence parameters: valve group on / off sequence, delay, and holding time; key machining parameters: speed, feed, depth of cut, torque, press-fit displacement, etc.; interlocking conditions: clamping in place, pressure / vacuum compliance, access control / emergency stop, etc.; and verification rules: windows, reference curves, or thresholds for S5 judgment.

[0104] S4.2 Intermediate representation of IR and constraint verification

[0105] Normalize the parameter package to IR; fields may include... , , , , , , , And so on. And execute at the IR layer:

[0106] 1) Unit consistency check: For example, mm / min and mm / rev cannot be used interchangeably;

[0107] 2) Boundary verification: Parameters do not exceed equipment capacity and process window;

[0108] 3) Interlock Dependency Verification: High-risk actions must have pre-interlocks, and the dependent signal source must be accessible in the controller variable table.

[0109] 4) Sequence constraint verification: The action sequence and dependencies are acyclic and conflict-free. Upon failure, the output should clearly state the failure item, the reason for the failure, and suggested repair directions.

[0110] S4.3 Cross-protocol variable mapping output and readback confirmation

[0111] According to the target controller protocol, output variable mapping table: OPCUA: node path, write method; Modbus: register address, scaling factor. Byte order; Controller variable table: variable name, type, write area. The mapping table should carry... and To avoid name conflicts, read back the key fields after writing to confirm the mapping is correct.

[0112] S5: Temporary write and process segment minimum no-load verification action set

[0113] S5.1 Temporary Write

[0114] The controller receives parameters and writes them to the temporary storage area, keeping the effective storage area unchanged. After the write is complete, the write result is sent back. If any field write fails (out of bounds, type mismatch, variable not existing), it is directly judged as a failure and enters S6 rollback.

[0115] S5.2 Minimum No-Load Verification Action Set Definition

[0116] Drilling section: Includes at least "tool setting action + depth jogging". Tool setting action can be triggering the tool setter or reaching the tool setting coordinates; depth jogging is a short-stroke downward probe with low feed and immediate retraction, used to verify depth parameters, feed parameters, coordinate system mapping, and interlock conditions. It can also simultaneously verify whether the Z-axis following error and current peak value are within the window.

[0117] Milling section: This section should include at least "low-speed idling + tool feed test run". Low-speed idling verifies spindle speed limits and start / stop interlocks; the tool feed test run executes a short segment of the critical path at a safe height to verify program references, tool compensation, feed limits, and limit interlocks. Spindle current and low-frequency vibration indicators can be collected as anomaly alerts.

[0118] Assembly section: This section should include at least "clamping action sequence trial run + positioning verification". The trial run is an unloaded clamping-holding-releasing cycle to verify the valve group sequence, delay, positioning signal and holding stability; if press fitting / tightening is involved, a minimum verification action of low torque idling or low force preload can be added.

[0119] S5.3 Conformity Verification and Deviation Indicators

[0120] During the execution of the action, key response curves (pressure holding phase, vacuum evacuation phase, position following error, current, etc.) are collected, and deviation indicators are calculated. And compare with the allowed window:

[0121] ;

[0122] in: : Measured response quantity; : By baseline model or Derived reference trajectory / allowed center; : Verification time window; : Allowable threshold.

[0123] when The verification is considered successful if the interlock signal sequence meets expectations. Verification failures are categorized as follows: mapping error, missing interlock, fixture performance abnormality, equipment capacity exceeding limits, etc., reducing downtime for troubleshooting.

[0124] S6: Temporary storage—effective switching, processing enablement, failure rollback and reporting.

[0125] This step is used to ensure that "processing only after verification" and to quickly restore a safe state in case of anomalies.

[0126] S6.1 effective switching

[0127] After S5 verification is successful, the controller will atomically switch the parameters in the temporary storage area to the active area and mark it as the new version. The version number and summary information are returned for traceability.

[0128] S6.2 Machining Enable Gating

[0129] Once the switching is complete and the interlock conditions are met, the controller allows the machining program to enter the enabled state (allowing spindle start, allowing automatic cycle / robot automatic cycle). If the interlock is not met, the controller remains in "activated but not enabled" and indicates a missing signal.

[0130] S6.3 Failure Rollback and Security Lock

[0131] When S5 verification fails or the switch fails, the controller rolls back to... It also locks the automatic cycle, allowing only manual / maintenance mode. Simultaneously, it reports abnormal events, including at least: work order number, equipment identifier, fixture ID, fixture type, operating condition label, failed action segment, and deviation. Failure code, timestamp. If automatic retries are allowed, the number of retries should be limited (e.g., 1–2 times), and S2–S3 should be re-executed for each retrieval to avoid reusing old decisions.

[0132] S6.4 Model Maintenance and Repair Linkage

[0133] For samples that recur but have been confirmed to have acceptable deviations, they can be included in the baseline model candidate set for updating; for indicators of deteriorating trends, maintenance recommendations and fixture discontinuation markers are automatically generated to achieve full lifecycle management of fixtures.

[0134] IV. Examples and Comparative Examples

[0135] 4.1 Test Objects and Unified Test Conditions

[0136] 1) Production line object: Discrete manufacturing unit, including 1 vertical machining center (for drilling / milling) and 1 set of robot assembly unit. The controllers support parameter storage area and effective area, and support low-risk action mode (tool setting, jogging, low-speed idling, tool test run, no-load clamping cycle).

[0137] 2) Fixture types: A: Hydraulic fixtures; B: Vacuum fixtures; C: Electric fixtures.

[0138] 3) Sampling and Preprocessing: Sampling Rate Hz; Trigger alignment threshold Set to 1% of full scale; low-pass cutoff frequency 20Hz; outlier removal uses a 3-standard-deviation criterion.

[0139] 4) Number of repetitions: Each working condition combination must be repeated at least once. Times; the statistical results give the mean. with standard deviation .

[0140] 5) Example of a threshold: Hydraulic leakage rate threshold: For passing; Vacuum evacuation time constant threshold: For passing; Electric clamp hysteresis area threshold: To pass; No-load verification deviation threshold: Among them, the drilling section Milling section Assembly section .

[0141] 6) Key indicator definition: Hydraulic leakage rate characteristics Vacuum evacuation time constant model Equivalent stiffness of electric clamps No-load verification deviation index .

[0142] 4.2 Baseline Model Establishment

[0143] Table 1 Statistics of Fixture Baseline Model

[0144]

[0145] 4.3 Example Set

[0146] Example 1

[0147] S1: Operating Condition: The oil supply pressure is stable at ℃, and the clamp is a hydraulic swing clamp.

[0148] S2: Pressure Step MPa MPa, maintain s, collection ;

[0149] S3: Calculation , And compare it with the baseline to determine its validity;

[0150] S4: Generate drilling parameter package;

[0151] S5: Execute the minimum set of "tool setting and fixed depth inching" actions, and calculate... ;

[0152] S6: The switch will then take effect and processing will be allowed.

[0153] Example 2

[0154] Verification of the milling section was conducted on the same machining center: S5: The minimum motion set was changed to "low-speed idling, tool feed test run"; the verification focused on the spindle current window, tool feed error, and interlock sequence.

[0155] After approval, the switch takes effect and enters automatic loop.

[0156] Example 3

[0157] The fixture is a zoned vacuum adsorption: S2: evacuation step acquisition. and fitting S3: When s and The window indicates the operation is valid; S5: Low-speed idling and tool feed test run, verification. .

[0158] Example 4

[0159] Operating conditions: At ℃, the pump takes a relatively long time to stabilize after startup. S1: The operating condition label triggers the "low temperature correction strategy," which... Thresholds are adjusted for temperature or the process enters the "required for verification" branch; S5: If the fast check passes but the confidence level is insufficient, an additional deep check is performed to reduce false releases.

[0160] Example 5

[0161] The fixture motion was verified in the robot assembly unit: S2: displacement micro-jitter ±0.1mm, data was collected. and estimate ,calculate and S5: The minimum action set is "no-load clamping - holding - releasing cycle + position signal stability check", and is calculated. Once approved, the automatic assembly cycle can begin.

[0162] Example 6

[0163] The upper-level system uses OPCUA to the edge gateway, and the edge gateway uses Modbus-TCP to the controller: S4: Perform IR consistency compilation and cross-protocol mapping output; S5: After writing, read back the key fields to confirm the mapping consistency, and then execute the minimum action set;

[0164] 4.4 Comparative Group

[0165] Comparative Example 1

[0166] The RFID / QR code identification fixture ID is retained, but perturbation excitation and dynamic feature determination are not performed; after the parameters are issued, only "successful write receipt" is used as the pass condition, and the minimum no-load verification action set is not executed.

[0167] Comparative Example 2

[0168] The same perturbation action is used for hydraulic, vacuum, and electric clamps. The extraction of strongly correlated mechanism features such as hydraulic leakage rate, vacuum time constant, and hysteresis is not targeted. The judgment is based on a single general feature and is still judged according to the same threshold.

[0169] Comparative Example 3

[0170] Perform S2–S3 dynamic validity determination, but after the parameters are written to the temporary storage area, do not perform the minimum action set verification bound to drilling / milling / assembly, and directly switch to take effect and enter the machining process.

[0171] 4.5 Data Statistics Table

[0172] Table 2. Statistics of Fixture Dynamic Validity Judgment Data (S2–S3)

[0173]

[0174] Table 3. Statistics on Minimum No-Load Verification (S5) for Process Sections

[0175]

[0176] Table 4 Comparison of Risk Event Statistics

[0177]

[0178] 4.6 Exception Event Code Table

[0179] Table 5. Definitions and Handling Strategies for Exception Event Codes

[0180]

[0181] 4.7 Summary of the overall technical effects of the embodiments / comparative examples

[0182] As shown in Tables 2-4, this invention effectively identifies latent failures such as fixture leakage, air leakage, and hysteresis through "strongly correlated perturbation excitation of the actuator + dynamic feature judgment"; significantly reduces field mismapping and interlocking omissions in multi-protocol and multi-controller environments through "IR consistent compilation + mapping readback confirmation"; and verifies the actual effectiveness of parameters and the correctness of interlocking sequences on the controller side at a low cycle cost through "minimum no-load verification action set bound to the process segment". This moves anomalies forward as much as possible to intercept them before processing, significantly reducing the number of incorrect processing risk events in production, increasing the first-piece pass rate, and shortening the anomaly recovery time, demonstrating comprehensive technical effects with significant engineering value for flexible manufacturing sites.

[0183] The foregoing description of embodiments of the present invention, through which those skilled in the art are able to implement or use the present invention, will be readily apparent to those skilled in the art. Various modifications to these embodiments will be readily apparent to those skilled in the art. The general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novelty disclosed herein.

Claims

1. A method for fixture identification and automatic distribution of process parameters based on the Internet of Things, characterized in that, include: S1, the controller selects perturbation excitation according to the type of clamping actuator and collects the response, forming a dynamic feature vector of the clamp and comparing it with the baseline model to determine the effectiveness of the clamp. The perturbation excitation of the hydraulic clamp is the pressure step and the leakage rate feature is extracted. The perturbation excitation of the vacuum clamp is the evacuation / release step and the evacuation time constant and rebound feature are extracted. The perturbation excitation of the electric clamp is the displacement micro-stroke jitter and the stiffness and hysteresis features are extracted. S2, after passing the judgment, generates a process parameter package according to the work order and equipment capacity and compiles it into variable mapping and interlocking conditions in a consistent manner; S3, after the parameters are sent to the controller's temporary storage area, execute the minimum no-load verification action set corresponding to the process segment. The drilling segment must include at least tool setting / deep jogging, the milling segment must include at least low-speed idling and tool feed test run, and the assembly segment must include at least fixture action sequence test run and positioning verification. Only after the verification is passed can machining be enabled; otherwise, rollback and anomaly report will be sent.

2. The method according to claim 1, characterized in that: The perturbation excitation is any one or a combination of clamping / releasing pulses, pressure or vacuum setpoint steps, and displacement micro-stroke jitter, and includes frequency scanning or phase perturbation to form the dynamic transmission characteristics of the fixture.

3. The method according to claim 1, characterized in that: The dynamic feature vector includes at least one or a combination of rise time, overshoot, steady-state error, frequency domain peak / bandwidth, and hysteresis loop area, and outputs the judgment result with a similarity threshold and confidence level.

4. The method according to claim 1, characterized in that: The fixture baseline model includes fixture number, workstation number, and temperature / gas supply pressure condition labels. The edge gateway adaptively updates the threshold based on fixture wear or condition drift and records the version.

5. The method according to claim 1, characterized in that: The consistency compilation includes converting the parameter package into an intermediate representation (IR), performing unit consistency, upper and lower bound boundary, interlock dependency and execution order constraint checks at the IR layer, and outputting mapping results for OPCUA nodes, Modbus registers or controller internal variable tables.

6. The method according to claim 1, characterized in that: The no-load verification process further includes performing low-speed jogging or idling verification on the spindle / axis system, and comparing the collected current, vibration or position following error with the allowable window derived from the parameter package.

7. A fixture identification and automatic process parameter distribution system based on the Internet of Things, characterized in that, The system is used to implement the method described in any one of claims 1-6, comprising: a fixture-side acquisition component, an edge gateway, a process parameter library, and an equipment controller; wherein the fixture-side acquisition component is used to acquire clamping pressure / vacuum degree / displacement response; the edge gateway is used to generate perturbation excitation commands, extract dynamic feature vectors and compare them with the baseline model, generate process parameter packages and compile them into variable mappings and interlocking conditions; the equipment controller is used to receive parameters into a temporary storage area and execute an unloaded verification process, and only after the verification is passed can machining be enabled.

8. The system according to claim 7, characterized in that: The edge gateway includes a dynamic identification module and a model management module. The dynamic identification module is used for time-domain / frequency-domain feature extraction, and the model management module is used for baseline model versioning, operating condition label association, and wear drift update. The edge gateway also includes a consistency compilation module, which is used to generate intermediate representations (IR) and complete unit consistency, boundary and interlock dependency verification, and output a consistent variable mapping table and interlock configuration across protocols / controllers. The device controller includes a temporary-effective switching module and a rollback module, which are used to restore the parameter version before the switch when the no-load verification fails, and generate an abnormal event record containing fixture judgment results, verification curve deviation, and failure reasons, and upload it to the upper system.

9. A computer device, characterized in that, The computer device includes a processor, a graphics processing unit (GPU), and a memory, wherein the memory stores a computer program that, when executed by the processor and the GPU, causes the computer device to perform the method as described in any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, the computer program, when executed by a computer device, causing the computer device to perform the method as described in any one of claims 1 to 6.

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