Numerical control grinding machine product quality real-time monitoring optimization method based on Internet of Things

Abstract

The invention relates to the technical field of machine tool equipment, in particular to a numerical control grinding machine product quality real-time monitoring optimization method based on the Internet of Things. The method comprises the following steps: acquiring multi-node real-time operation data of the numerical control grinding machine; working state parameters of key components of the grinding machine are counted according to the multi-node real-time operation data; the product machining quality fluctuation trend is determined based on the multi-node real-time operation data and the working state parameters of the key components of the grinding machine; according to the product machining quality fluctuation trend, the cooperative change rule of the grinding machine tailstock jacking force is detected; and determining the geometric accuracy limitation trend of the workpiece according to the cooperative change rule of the jacking force of the tailstock of the grinding machine. According to the method, the control parameter adjustment behavior, the system response state and the quality detection result of the numerical control grinding machine are associated and integrated, and the tracking and effect evaluation of the key control behavior are realized by constructing a historical contrast system of the adjustment behavior and the response data.

Description

Technical Field

[0001] The present invention relates to the technical field of machine tool equipment, and in particular to a real-time monitoring and optimization method for CNC grinding machine product quality based on the Internet of Things. Background Art

[0002] A CNC grinding machine is a high-precision, high-efficiency automated machine tool used to process parts with complex shapes and high precision requirements. Its main components include a bed and base, a spindle assembly, a worktable, a grinding wheel head, a CNC system, a cooling system, and a lubrication system. These components work together to automate the entire process, from program input to grinding. The CNC system inputs the program and controls the movement of the spindle, worktable, and grinding wheel head. The high-speed rotation of the spindle drives the grinding wheel, the worktable moves along multiple axes to position the workpiece, and the grinding wheel head moves along the feed direction to perform the grinding. The cooling system and lubrication system are used to dissipate grinding heat and reduce component friction, respectively, ensuring process stability and long-term machine operation. Vibration sensors, temperature sensors, and displacement sensors are deployed at key locations on the CNC grinding machine to collect real-time data on vibration, temperature, and displacement during operation. This data is transmitted to a monitoring center via communication devices such as industrial gateways. The monitoring center's data processing unit uses time-domain and frequency-domain analysis algorithms to conduct in-depth analysis of the collected data, identifying characteristic frequencies and abnormal fluctuations. The system possesses self-learning capabilities, continuously optimizing analysis models based on historical data and actual machining conditions, improving the accuracy and timeliness of fault diagnosis. Existing grinding machine monitoring systems mostly utilize independent data collection and processing methods, lacking effective data exchange and collaborative analysis capabilities between monitoring nodes. This fragmented data situation prevents the full mining and utilization of the vast amount of valuable information generated during equipment operation, limiting the advancement of intelligent quality monitoring systems. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a real-time monitoring and optimization method for CNC grinding machine product quality based on the Internet of Things to solve at least one of the above technical problems.

[0004] To achieve the above objectives, a real-time monitoring and optimization method for CNC grinding machine product quality based on the Internet of Things includes the following steps:

[0005] Step S1: Acquire multi-node real-time operation data of a CNC grinder; calculate working status parameters of key components of the grinder based on the multi-node real-time operation data; and determine product processing quality fluctuation trends based on the multi-node real-time operation data and the working status parameters of key components of the grinder;

[0006] Step S2: detecting the coordinated variation pattern of the tailstock clamping force of the grinder according to the fluctuation trend of the product processing quality; determining the trend of limited geometric accuracy of the workpiece according to the coordinated variation pattern of the tailstock clamping force of the grinder; and determining the linkage imbalance characteristics of the grinding machine processing system according to the coordinated variation pattern of the tailstock clamping force of the grinder and the trend of limited geometric accuracy of the workpiece;

[0007] Step S3: Determine the product quality stability deviation based on the grinding machine processing system linkage imbalance characteristics; determine the degree of deviation of the grinding machine control parameters based on the product quality stability deviation and the grinding machine processing system linkage imbalance characteristics; and perform real-time adjustment processing on the degree of deviation of the grinding machine control parameters based on the Internet of Things control system to obtain the execution status of the grinding machine parameter adjustment;

[0008] Step S4: Based on the execution of the grinding machine parameter adjustment, the response effect of the Internet of Things control system is evaluated to obtain the response defect data of the Internet of Things control system; based on the response defect data of the Internet of Things control system, the control optimization processing of the grinding machine quality monitoring system is performed to obtain the optimized real-time monitoring system for the product quality of the grinding machine.

[0009] The present invention addresses the problems of lag in adjustment response, large deviation in parameter execution, weak system feedback mechanism, etc. in the actual operation of traditional CNC grinding machine systems. By constructing refined Internet of Things control logic and closed-loop optimization process, the deep coordination and dynamic adaptation of the grinding machine quality monitoring and control system are achieved. First, by extracting the grinding machine operation status log and control parameter adjustment record, a historical comparison table of adjustment behavior and system response is established, which enables the system to truly reproduce the causal relationship between various adjustment behaviors and results, and provides a highly reliable data foundation for subsequent data analysis and strategy optimization. This mapping mechanism effectively makes up for the problem of relying solely on result analysis and ignoring the details of adjustment behavior in the past, and improves the system's ability to understand the background and results of adjustment actions. Based on the history of adjustment behavior, the system further constructs a control parameter execution tag library based on parameter type, response effectiveness level and execution time. This tag library not only helps the system to structure the archiving of historical behaviors, but can also be used for sample input of subsequent machine learning models, with high generalization ability and scalability. By analyzing the tag library, the system accurately identifies parameter types that frequently exhibit abnormal responses or high adjustment deviation rates and classifies them as candidates for optimization. This eliminates the reliance on manual judgment when triggering optimization strategies and instead relies on systematic data insights, significantly improving identification accuracy and efficiency. For identified parameters, the system establishes a scientific parameter prioritization criteria based on the number of execution failures and average adjustment deviation. This prioritizes limited optimization resources on the control elements with the greatest impact on system quality, thereby improving overall control effectiveness. For high-priority parameters, the average deviation drives the setting of parameter adjustment coefficients, effectively enabling dynamic adjustment of adjustment sensitivity. This adaptive adjustment approach avoids overcorrection or slow response, resulting in more stable and accurate adjustment results. For example, for parameters with large average deviations, the system significantly compresses their adjustment range to prevent system overshoot or oscillation, enhancing system robustness and safety. Furthermore, for medium-priority parameters, response time is optimized without adjusting the control value itself, avoiding interference with relatively stable parameters. Shortening the response time can improve the timeliness of the system's control over medium-important parameters, reduce the cumulative impact of response lags, and further optimize the overall dynamic response capability of the grinder. Through a hierarchical optimization strategy, the system achieves differentiated processing of different types of parameter items, taking into account both adjustment accuracy and response speed, and demonstrating a higher level of adaptive control capability. The above optimization results are integrated into a unified control parameter configuration file and uploaded to the system parameter storage module through the Internet of Things communication interface to ensure that all optimized configurations can take effect quickly after the system is restarted, realizing the automation and efficiency of parameter updates. Through the soft restart mechanism, the system quickly loads new parameters without affecting the hardware structure, ensuring the continuity and stability of the operation of the grinder quality monitoring system.Furthermore, the system automatically verifies the optimized parameter configuration after it goes live, initiating a new round of real-time monitoring to reanalyze and evaluate the adjustment results, establishing a complete closed-loop feedback mechanism. This mechanism continuously tracks optimization results and incorporates them into subsequent historical comparison tables and tag libraries, forming a self-evolving path of optimization-execution-verification-reoptimization. Overall, this method, by integrating a series of steps—historical behavior backtracking, labeling, priority classification, adaptive parameter adjustment, detailed response time control, and automatic configuration replacement—achieves a shift from static setting to dynamic optimization of grinding machine control parameters. The introduction of IoT technology significantly automates data collection, communication, and configuration execution, reducing the uncertainty associated with human intervention and significantly improving the adaptability, stability, and adjustment efficiency of CNC grinding machines in complex production environments. In particular, in manufacturing scenarios with multiple product models and frequent high-precision machining tasks, this method can automatically adjust key control parameters based on the grinder's own operating data, effectively ensuring consistent product quality and equipment efficiency. This provides strong technical support for the intelligent operation and maintenance of CNC equipment in intelligent manufacturing scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0011] Figure 1 Schematic diagram of the steps of the method for real-time monitoring and optimization of CNC grinding machine product quality based on the Internet of Things of the present invention;

[0012] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0013] Figure 3 for Figure 1 Detailed step flow chart of step S3 in FIG. DETAILED DESCRIPTION

[0014] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0015] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0016] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0017] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for real-time monitoring and optimization of CNC grinding machine product quality based on the Internet of Things, the method comprising the following steps:

[0018] Step S1: Acquire multi-node real-time operation data of a CNC grinder; calculate working status parameters of key components of the grinder based on the multi-node real-time operation data; and determine product processing quality fluctuation trends based on the multi-node real-time operation data and the working status parameters of key components of the grinder;

[0019] The embodiment of the present invention selects a CNC external cylindrical grinder equipped with an industrial Internet of Things acquisition module to collect real-time operating data of its multiple key nodes. The nodes include a spindle unit, a bed guide rail, a tailstock clamping mechanism, a grinding wheel feed mechanism and a cooling system. By configuring an industrial Ethernet gateway, various sensors (such as vibration sensors, temperature sensors, position encoders, pressure sensors, current / voltage acquisition modules) are connected to the data acquisition system, and operating status data is acquired every 0.5 seconds. Subsequently, based on the time window statistical method, state parameters of key components such as the axial clamping pressure of the tailstock clamping device, the stability of the grinding wheel spindle speed, and the coolant flow rate fluctuation amplitude are extracted and normalized. By introducing a time series correlation analysis model (such as the weighted trend mean and standard deviation comparison method within a sliding window), the fluctuations of the parameters of each component in the process of processing the same type of parts are analyzed to determine whether the geometric accuracy (such as roundness, cylindricity) and surface roughness of the batch of products have periodic changes, thereby extracting the fluctuation trend characteristics of the product processing quality, providing data support for the subsequent analysis of system linkage anomalies.

[0020] Step S2: detecting the coordinated variation pattern of the tailstock clamping force of the grinder according to the fluctuation trend of the product processing quality; determining the trend of limited geometric accuracy of the workpiece according to the coordinated variation pattern of the tailstock clamping force of the grinder; and determining the linkage imbalance characteristics of the grinding machine processing system according to the coordinated variation pattern of the tailstock clamping force of the grinder and the trend of limited geometric accuracy of the workpiece;

[0021] The embodiment of the present invention is based on the processing quality fluctuation trend data, and this step models and analyzes the coordinated change rules of the tailstock clamping force of the grinder. By analyzing the continuous output data sequence of the tailstock clamping force sensor, the weak disturbance mode of the clamping state of the workpiece in different time periods is identified when processing the same workpiece, and combined with the spindle current change and the grinding wheel displacement compensation trend during the grinding process, the method based on dynamic time warping (DTW) and principal component collaborative analysis is used to identify the coupling relationship between the clamping force and other component parameters, and extract the synchronization characteristics of their changes. For example, when the clamping force shows a periodic decrease after processing every 4 products, and is accompanied by the deterioration of the roundness accuracy of the tail end of the workpiece, it can be judged that there is a trend of limited geometric accuracy of the workpiece. On this basis, the fuzzy clustering method is further used to conduct linkage decoupling analysis on multi-node parameters to identify whether there are manifestations of linkage imbalance in the grinding machine processing system, such as asynchronous responses of "tailstock clamping-spindle stiffness-grinding wheel feed" and asynchronous disturbance frequency. Corresponding linkage imbalance feature labels are established, such as "a decrease in synergy index of more than 20%" as the judgment standard, to provide a basis for subsequent identification of stability control deviations.

[0022] Step S3: Determine the product quality stability deviation based on the grinding machine processing system linkage imbalance characteristics; determine the degree of deviation of the grinding machine control parameters based on the product quality stability deviation and the grinding machine processing system linkage imbalance characteristics; and perform real-time adjustment processing on the degree of deviation of the grinding machine control parameters based on the Internet of Things control system to obtain the execution status of the grinding machine parameter adjustment;

[0023] The embodiment of the present invention utilizes the identified linkage misalignment characteristics and further combines the surface quality data and geometric error trends of processed batches of products at different time periods to construct a product quality stability deviation identification model. The empirical mode decomposition method (EMD) is used to perform feature decomposition on the workpiece cylindricity error sequence to determine the correspondence between the deviation trend and the linkage misalignment characteristics. For example, it is found that the cylindricity error in the tail section is generally higher than that in the head section and has a synchronous relationship with the tailstock tightening fluctuation. It can be preliminarily determined that the stability deviation is caused by tailstock misalignment. Furthermore, the deviation between the control system set parameters and the real-time collected values ​​is combined to calculate the degree of control parameter deviation. For example, if the tailstock pneumatic clamping pressure setting value is 0.6MPa and the actual fluctuation range is between 0.55MPa and 0.58MPa, and the deviation rate exceeds 3%, it is recorded as "moderate deviation". Through the deployed industrial Internet of Things control system, such deviation parameters are adjusted in real time by PID closed-loop. The adjustment results include key indicators such as adjustment response time, adjustment stability time, and error convergence amplitude, and are uploaded to the cloud database. The system records the execution of a complete parameter adjustment, such as "clamping force compensation 0.02MPa, response time 3.2 seconds, and excellent adjustment stability", laying the foundation for the system response performance evaluation.

[0024] Step S4: Based on the execution of the grinding machine parameter adjustment, the response effect of the Internet of Things control system is evaluated to obtain the response defect data of the Internet of Things control system; based on the response defect data of the Internet of Things control system, the control optimization processing of the grinding machine quality monitoring system is performed to obtain the optimized real-time monitoring system for the product quality of the grinding machine.

[0025] The embodiment of the present invention constructs an Internet of Things control system response effect evaluation model based on the execution status of the grinding machine parameter adjustment. By setting response defect evaluation indicators, including whether the response lag time exceeds the threshold, whether the improvement in processing quality after adjustment meets the standard, whether the control system intervention frequency is too high, etc., the fuzzy comprehensive evaluation method is used to perform weighted calculations on each indicator to extract the system response defect data. For example, if the average system adjustment response time in a batch exceeds 5 seconds and the improvement in processing accuracy is less than 10%, it is recorded as a "response efficiency deficiency defect". Subsequently, the response defect data is subjected to causal graph analysis and control strategy reconstruction, such as adopting an adaptive parameter preloading model, introducing an interference feedforward suppression strategy, etc., to perform online optimization of the PID parameters or feedback gain matrix in the control system, and obtain a set of optimized grinding machine product quality real-time monitoring system. The system supports dynamic self-learning function. When processing new models of products, it can quickly adjust the control strategy according to historical data and mark the status of key response nodes in real time. The specific application scenario is the grinding of high-precision aircraft engine shaft parts. With the support of this optimization system, the yield rate has increased from the original 94.6% to 98.1%, and the number of complaints about the grinding process quality has been reduced by more than 70%, significantly improving the manufacturing quality stability and intelligent control capabilities.

[0026] Preferably, step S1 includes the following steps:

[0027] Step S11: synchronously collecting data through a sensor network installed on the tailstock, spindle box, cross feed mechanism, grinding wheel frame and worktable of the grinding machine to obtain multi-node real-time operation data, wherein the multi-node real-time operation data includes tailstock tightening force data, spindle speed data, feed position data, grinding wheel vibration data and worktable temperature data;

[0028] In order to realize the synchronous collection of real-time operation data of multiple nodes of a CNC grinder, an embodiment of the present invention arranges an industrial-grade sensor network in the grinder structure, including: installing a miniature air pressure sensor on the tailstock (measuring the real-time clamping force of the clamping cylinder with an accuracy of 0.01 MPa), embedding a high-speed rotary encoder and an axis current sensor in the spindle box (for obtaining the spindle speed and current load changes, with a speed sampling accuracy of ±5 revolutions per minute), installing a grating scale and a servo drive feedback sampling device in the lateral feed mechanism (collecting X-axis feed displacement and feed instruction deviation), arranging a three-axis micro-vibration sensor on the grinding wheel frame (recording the vibration intensity during the grinding process of the grinding wheel, with a frequency response range of 10 Hz to 10 kHz), and pasting a thermocouple temperature sensor array on the bottom of the workbench (monitoring temperature rise changes and thermal expansion trends). All sensors are connected to the on-site industrial gateway via the Modbus-TCP protocol. The data is collected five times per second and automatically uploaded to the edge processing platform, generating multi-node real-time operational data including tailstock clamping force, spindle speed, feed position, grinding wheel vibration, and worktable temperature. This data is then used for subsequent process feature recognition and quality trend analysis. This data collection system has been deployed in the grinding process of high-precision aerospace shaft parts, collecting data to detect thermal deformation compensation failures.

[0029] Step S12: Counting working status parameters of key components of the grinding machine based on real-time operation data of multiple nodes;

[0030] The embodiment of the present invention is based on the multi-node real-time operation data obtained in step S11. In this step, the operating state parameters of the key components of the grinding machine are statistically analyzed and classified, and a parameter database is established using a state feature extraction and level classification method. In the specific implementation process, the mean, range and standard deviation of each type of sensor data within the sliding time window are statistically processed. For example, the tailstock clamping force data is statistically processed with every 10 seconds as a window, and the average clamping pressure, maximum fluctuation amplitude, etc. within the window are calculated and compared with the historical stable values. When the parameters of a component deviate from its stable operating range for a long time, for example, the average value of the grinding wheel vibration intensity is continuously higher than 2.0g (unit of gravity acceleration), the component state is marked as "abnormal vibration". All key components such as the tailstock, spindle, grinding wheel frame, feed mechanism, etc. generate "state level labels" in sequence, such as "tailstock clamping state: normal", "spindle speed state: slight fluctuation", "grinding wheel vibration state: severe abnormality", thereby forming a "key component state parameter set" for the current time period, which serves as the basic data for subsequent judgment of collaborative deviation and system fluctuation analysis. This method has been applied in the maintenance data systems of multiple machine models to achieve digital modeling and operation classification of grinding machine status.

[0031] Step S13: Calculating data fluctuation intensity based on the time series change of each node data in the multi-node real-time operation data; and calculating component coordination deviation based on the change trend of each component status level in the working status parameters of the key components of the grinding machine;

[0032] This embodiment of the present invention utilizes the time series of raw sensor data collected in step S11 and the key component state parameters extracted in step S12 to conduct an in-depth analysis of the dynamic response characteristics of the machining process. First, using a time series fluctuation intensity analysis model, the fluctuation intensity of each sensor data point within a certain period is calculated. Specifically, the ratio of the difference between the maximum and minimum values ​​to the mean within a fixed time window is calculated as the fluctuation intensity index for that node. For example, a fluctuation intensity of 0.85 is calculated for grinding wheel vibration data, indicating a strong response at that location. Secondly, the operational consistency of multiple components is analyzed based on historical trends in status levels. The "component coordination deviation" metric is introduced to measure whether the status levels of key components change synchronously. For example, in a batch of machining, if the spindle status is marked as "normal" for five consecutive cycles, while the tailstock clamping status frequently switches between "slightly offset" and "normal," this indicates a deviation in the coordinated control of the two components. The degree of coordination deviation is quantified by calculating the difference coefficients between the status levels of all key components. The greater the difference, the worse the coordination. This metric provides a basis for subsequent product quality trend analysis. Application examples show that this method can effectively detect the problem of decreased dimensional consistency caused by slight looseness of the tailstock in precision parts grinding, such as hydraulic valve core processing.

[0033] Step S14: Perform correlation analysis based on the data fluctuation intensity and component coordination deviation to obtain the product processing quality fluctuation trend, wherein the correlation analysis is specifically when the data fluctuation intensity exceeds the set threshold and the component coordination deviation shows an increasing trend, it is determined that the product processing quality has an unstable fluctuation trend; otherwise, it is determined that the product processing quality maintains a stable trend.

[0034] After completing the analysis of fluctuation intensity and synergy deviation, this embodiment of the present invention implements a step to determine the correlation between the two, thereby determining whether there is a fluctuation trend in product processing quality. The specific method is to establish a three-factor correlation analysis model: "data fluctuation-synergy-quality trend," and set multiple empirical threshold parameters: when the node fluctuation intensity index exceeds 0.7 and the synergy deviation values ​​of at least two key components are higher than 0.5, the system automatically determines the current processing status as "unstable quality fluctuation"; otherwise, it is determined to be "stable quality." For example, during the processing of a certain aerospace engine spindle part, the grinding wheel vibration intensity value was recorded to rise to 1.1 during the processing of 30 consecutive products, and the synergy deviation between the tailstock and the spindle increased. The analysis model, combined with a comparison of historical processing data tags, determined that the product's cylindricity deviation was approaching the upper limit of the process tolerance, triggering an early warning mechanism. The analysis results are ultimately fed back to the IoT platform control terminal to guide whether to make real-time adjustments or planned maintenance, thereby improving product consistency and system response sensitivity. This method is particularly suitable for online processing quality control scenarios for precision workpieces that require high dimensional consistency, such as those in hydraulics, aviation, and precision instrument manufacturing.

[0035] Preferably, step S12 includes the following steps:

[0036] Step S121: Calculating the tailstock workload mean and load fluctuation coefficient based on the tailstock tightening force data, thereby determining the tailstock component working status level;

[0037] After completing the real-time collection of tailstock clamping force data, the embodiment of the present invention further processes the data to determine the tailstock's working status level. First, a fixed time analysis window is set (for example, every 60 seconds). The average value of all clamping force data within this window is calculated to obtain the tailstock workload mean, which is used to measure whether the actual clamping capacity of the tailstock clamping system during the processing process is stable. At the same time, the ratio of the standard deviation of the clamping force data within this window to the mean is calculated as the "load fluctuation coefficient" to determine whether there is instability caused by compressed air source fluctuations or mechanical clearance during the tailstock clamping process. Taking a CNC grinder in a certain factory as an example, under normal working conditions, the average clamping force should be 0.45MPa, with a fluctuation coefficient of less than 5%. If the average value is detected to be lower than 0.4MPa or the fluctuation coefficient exceeds 10%, the system automatically marks the tailstock status level as "abnormal" or "critical". Finally, the working status level of the tailstock component is divided into three categories according to the working condition standard: "normal", "slight deviation", and "severe abnormality". The results will be used for subsequent state parameter combination modeling.

[0038] Step S122: Calculating the spindle operation stability and speed consistency index based on the spindle speed data, thereby determining the spindle component working status level;

[0039] The embodiment of the present invention is based on the spindle speed data and extracts "operation stability" and "speed consistency index" by analyzing the spindle speed time series data. Operation stability is used to reflect whether the spindle has jitter or load fluctuations, and can be expressed by the ratio of the difference between the maximum and minimum speed values ​​in the sliding time window to the target set speed; the speed consistency index is used to measure whether the spindle speed control is uniform during the batch part processing process. The calculation method is the ratio of the standard deviation of the average speed within the processing cycle of different workpieces to the target value. In actual applications, if the spindle speed is set to 3000 rpm, its stability fluctuation during a certain section of processing is 1.5%, and the consistency index is controlled within 0.8%, then the spindle operation status can be assessed as "stable". If both exceed 3%, it is an "abnormal" state. The above status levels are written into the edge computing module through model rules to judge the spindle working status level in real time, providing support for the overall grinder quality control.

[0040] Step S123: determining the working status level of the feed system components according to the feed position data feed accuracy deviation value and feed speed uniformity index;

[0041] The state judgment of the feed system in the embodiment of the present invention relies on the feed position data collected by the lateral feed mechanism, and performs deviation analysis in combination with the instructions and actual feedback. In this step, the difference between the "target position" and the "actual arrival position" in each feed path is extracted, and the average error within a certain time window is calculated as the "feed accuracy deviation value". In addition, the speed change rate of each sampling point in the continuous feeding stage is used to calculate the "feed speed uniformity index". The lower this index is, the more uniform the feed is. For a certain model of CNC grinding machine, under the feed accuracy requirement of 0.001mm, its qualified working condition is that the feed deviation does not exceed ±0.002mm, and the speed uniformity index is controlled within 0.05. If a batch of data analysis finds that the feed error reaches 0.005mm and the speed fluctuates frequently, the feed system status level is set to "mild deviation" or "serious deviation" to guide equipment maintenance and servo adjustment, thereby preventing dimensional deviations due to feed abnormalities.

[0042] Step S124: Calculating the grinding wheel working balance and vibration intensity index based on the grinding wheel vibration data, thereby obtaining the working status level of the grinding wheel system components;

[0043] The embodiment of the present invention analyzes the vibration data of the grinding wheel to evaluate its "working balance" and "vibration intensity index". First, the three-axis vibration data is synthesized into a total vibration vector, and its effective value (RMS) is calculated as the "vibration intensity index"; then the balance of the vibration amplitudes in the X, Y, and Z directions is compared to determine whether the grinding wheel is unbalanced due to eccentricity, wear or improper clamping, that is, the "working balance". If the three-axis vibration amplitudes differ greatly (such as the Z axis exceeds the X axis by more than 2 times), it means that there is a directional deviation and the working balance is poor. Taking a heavy-loaded grinding wheel working condition as an example, its RMS value is normal when it is below 0.3g, and it is severe vibration when it exceeds 0.8g; if the imbalance coefficient is greater than 0.4, it is eccentric clamping. The system classifies the grinding wheel status level accordingly, and issues a maintenance warning of "needs dressing" or "needs dynamic balancing" to prevent the surface roughness of the processed surface from exceeding the standard.

[0044] Step S125: using the workbench temperature data to evaluate the thermal deformation risk and temperature stability index, thereby determining the working status level of the thermal control system components;

[0045] The embodiment of the present invention uses the data of temperature sensors arranged on the workbench to evaluate the status of the thermal control system. The specific method includes: counting the heating rate of each temperature collection point from the beginning to the end of the processing cycle, and calculating the maximum temperature difference as a "temperature stability index"; at the same time, evaluating the risk of thermal deformation through an empirical model. For example, when the table temperature rises by more than 15°C and the temperature difference at different points exceeds 8°C, it is considered that there is a "high thermal deformation risk". In addition, whether the system has the "rapid thermal balance" capability is determined based on whether the temperature change in the heat-sensitive area (such as the contact surface between the main rail and the workpiece) stabilizes within 5 minutes. If the temperature rise trend is slow and the temperature difference is less than 5°C in a certain processing, the status level is "good thermal control", otherwise it will prompt "unstable thermal control" and cooling or shutdown is required. This status level is well applied in the fine grinding process of aircraft engine parts.

[0046] Step S126: combining the tailstock component working status level, the spindle component working status level, the feed system component working status level, the grinding wheel system component working status level, and the thermal control system component working status level to form working status parameters of key components of the grinding machine.

[0047] After completing the determination of the status levels of the five components mentioned above, the embodiment of the present invention combines these status levels to form "working status parameters of key components of the grinding machine". Through the state fusion engine, the system corresponds the "tailstock status level", "spindle status level", "feed system status level", "grinding wheel system status level" and "thermal control system status level" to one of the five tuples, and records its timestamp, processing task number, workpiece number and other information to generate a labeled state parameter set. If the state parameter combination at a certain moment is [normal, normal, slight deviation, serious abnormality, good thermal control], it means that the grinding wheel vibration problem in this cycle is the main quality hazard. This combined data will be used as the core input variable for calculating component coordination deviation and establishing a fluctuation prediction model in subsequent steps, and will also be used for chart display of the data visualization system, so that equipment maintenance personnel can quickly locate key failure nodes and take process intervention measures in advance.

[0048] Preferably, in step S2, detecting the coordinated change rule of the tightening force of the tailstock of the grinding machine according to the fluctuation trend of the product processing quality includes:

[0049] According to the fluctuation trend of product processing quality, the numerical sequence of the tailstock tightening force data within the time window is extracted, and the difference in the tightening force values ​​between consecutive time points is calculated to obtain the tightening force change gradient;

[0050] Calculate the mean and standard deviation of the tightening force change gradient to determine the base change state of the tailstock tightening force;

[0051] The timestamp correspondence between the tailstock's tightening force and the spindle speed is extracted based on the tailstock's tightening force benchmark change state, and the change trend of the numerical ratio of the two sets of data at the same time point is calculated.

[0052] The increase and decrease synchronization of the tailstock clamping force and the feed position in the state of the tailstock clamping force benchmark change is analyzed through a sliding time window, and the ratio of the number of changes in the same direction to the number of changes in the opposite direction is calculated to obtain the synchronization ratio.

[0053] The response delay time is determined according to the numerical ratio change trend and synchronization ratio, so as to detect the coordinated change law of the tightening force of the tailstock of the grinding machine.

[0054] The embodiment of the present invention selects a corresponding time window range based on the quality fluctuation trend during the processing of the grinding machine products, such as changes in dimensional tolerances or abnormal surface roughness. For example, when a certain batch of parts is processed and the surface quality fluctuates greatly, the original force value data collected by the tailstock clamping force sensor during this time period is extracted to form a clamping force value sequence arranged in a time series. Subsequently, the clamping force difference between two consecutive time points in the sequence is calculated to obtain the clamping force change gradient within each time period to reflect the degree of change and fluctuation characteristics of the tailstock force value during the continuous clamping process. The "clamping force" referred to here is the force applied by the tailstock to clamp the workpiece to prevent axial movement. The unit is usually Newton (N). The change gradient represents the amplitude of the change in the force value in the time dimension. After obtaining the tailstock force gradient sequence, a statistical analysis is performed on the gradient sequence over the entire time window, specifically calculating its mean and standard deviation. The mean reflects the average level of tailstock force fluctuations during that time period, while the standard deviation indicates the degree of dispersion or instability of that fluctuation. These two statistical values ​​together constitute the "baseline variation state" of the tailstock force, serving as a reference for subsequent assessment of the coordinated response relationship. If the mean is stable but the standard deviation is large, this indicates frequent fluctuations in the tailstock force control, potentially affecting workpiece clamping stability and leading to fluctuations in machining quality. After obtaining the baseline variation state, spindle speed data with synchronized timestamps is extracted. Specifically, at each time point when the tailstock force is recorded, the current speed value recorded by the spindle speed sensor is obtained. By pairing each pair of force and speed data at the same time point and calculating the ratio of the two values, a time-varying curve of the ratio is constructed to assess whether the tailstock force action remains consistent or exhibits lag at different spindle speeds. For example, when the spindle speed increases, the trend of the ratio of whether the clamping force increases promptly to counteract the increased centrifugal force and cutting reaction force can be used to determine whether their coordination is normal. Based on the above, to further analyze the delay in the tailstock clamping force's response to machining actions, a sliding time window technique is introduced. Specifically, within a time window of a set length, the relative relationship between the tailstock clamping force and the feed position in the feed system is observed to determine their synchronization. Within each sliding window, the number of times the clamping force and feed position increase (or decrease) simultaneously or change in opposite directions is counted. The number of such changes in the same and opposite directions over the entire time period is accumulated, and their ratio is calculated to obtain the synchronization ratio value. A higher synchronization ratio indicates closer coordination between the tailstock and feed system, which is conducive to improving machining stability. Finally, by comprehensively analyzing the numerical ratio trend and the synchronization ratio, combined with the trend transition points observed within the sliding window, the average delay in the tailstock's response to changes in the spindle and feed actions, namely the "response delay time," can be quantified. A shorter response delay time indicates a more timely system response and greater ability to achieve high-precision coordinated machining.By detecting such collaborative rules in the processing of multiple batches of products, we can further establish an optimization model to achieve adaptive adjustment of the grinder tailstock control parameters based on the Internet of Things, thereby improving the real-time and intelligent level of overall product quality control.

[0055] Preferably, determining the trend of workpiece geometric accuracy limitation according to the coordinated variation law of the tailstock tightening force of the grinding machine in step S2 includes:

[0056] According to the coordinated variation law of the tailstock tightening force of the grinder, a continuous tightening force numerical sequence is extracted, and the local maximum value in the numerical sequence is identified as the peak value and the local minimum value as the valley value.

[0057] Based on the identified peak and valley values, the numerical difference between the peak and valley values ​​is calculated, and the maximum numerical difference is recorded as the tightening force fluctuation amplitude range;

[0058] When the workpiece diameter is 10mm-200mm, a proportional coefficient is generated based on the clamping force fluctuation range and the workpiece diameter;

[0059] The radial runout prediction value is calculated based on the proportional coefficient. When the proportional coefficient is between 0.001 and 0.01, the radial runout prediction value = proportional coefficient × workpiece diameter × 0.5; when the proportional coefficient is greater than 0.01, the radial runout prediction value = proportional coefficient × workpiece diameter × 0.8;

[0060] The theoretical deviation of the workpiece axial displacement is obtained by numerically multiplying the response delay time in the coordinated change law of the tailstock clamping force of the grinder with the current feed speed.

[0061] Calculate the maximum axial displacement deviation of the workpiece based on the theoretical axial displacement deviation of the workpiece;

[0062] Compare the predicted radial runout value with the preset radial runout allowable value. When the predicted value is greater than 80% of the allowable value, mark the radial accuracy risk level as warning.

[0063] Compare the maximum axial displacement deviation with the preset axial displacement allowable value. When the deviation is greater than 80% of the allowable value, mark the axial accuracy risk level as warning.

[0064] The trend of workpiece geometric accuracy limitation is determined based on the combined status of the radial accuracy risk level and the axial accuracy risk level. When both the radial accuracy risk level and the axial accuracy risk level are warning levels, the trend of geometric accuracy limitation is determined to be severely limited; when only one level is a warning level, it is determined to be slightly limited; when both levels are normal, it is determined to be stable in accuracy.

[0065] Building on the previously established coordinated variation patterns of the tailstock's clamping force, this embodiment of the present invention further extracts a continuous sequence of clamping force values ​​during the grinding process. This sequence is collected in real time by an IoT pressure sensor at a sampling frequency of 100 times per second to ensure detailed capture of force variations. Within this sequence, a sliding window-based extreme value detection algorithm is used to identify peaks and valleys within each local time period. Specifically, a clamping force value at a given moment is considered a peak when it is greater than the values ​​at several preceding and following time points, while a valley is considered a valley. This procedure effectively identifies the periodic fluctuation patterns of the tailstock's clamping force during the grinding process. Peaks often correspond to the moment when the tailstock has just completed a clamping cycle, while valleys may indicate the lowest clamping state after tailstock force release or feed disturbance. After peak-valley extraction, the numerical difference between each pair of adjacent peak and valley values ​​is calculated to determine the amplitude of a single force fluctuation. By comparing all fluctuation differences, the maximum difference is selected as the "clamping force fluctuation amplitude range" for that period. This parameter reflects the most severe clamping instability that may occur during the grinding process and serves as an important basis for subsequent precision prediction. For example, if the maximum clamping force fluctuation detected during machining is 35N, the difference between the minimum and maximum clamping forces on the workpiece reaches this value, potentially causing significant positioning deviation or deformation. When machining workpieces of different diameters, the stability of the tailstock clamping force affects the geometric accuracy of the product to varying degrees. Therefore, when the system identifies a workpiece diameter between 10mm and 200mm, it calculates a "proportional coefficient" by calculating the ratio of the clamping force fluctuation range to the workpiece diameter. This coefficient is used to normalize the impact of tailstock force fluctuations. This proportional coefficient is a dimensionless number that represents the degree of instability in the clamping force per unit diameter of the workpiece. For example, if the current fluctuation amplitude is 30N and the machining diameter is 60mm, the proportional coefficient is 0.5N / mm. The proportional coefficient can be used to further calculate the predicted radial runout of the workpiece. Radial runout refers to the circumferential eccentricity of the workpiece during rotation due to improper installation, uneven clamping, or changes in tailstock clamping. During the implementation process, when the proportional coefficient is between 0.001 and 0.01, the coefficient is multiplied by the workpiece diameter and then multiplied by 0.5 for estimation; when the proportional coefficient is greater than 0.01, the runout prediction weight is amplified and multiplied by 0.8 for calculation. Taking an actual scenario as an example, if the processing diameter is 100mm and the proportional coefficient is 0.012, the runout prediction value is 0.96mm, which means that an eccentric runout of nearly 1mm may occur during the rotation process, which will significantly affect the consistency of the outer circle size. At the same time, in order to further evaluate the influence of the tailstock clamping stability on the axial position control of the workpiece, the response delay time (for example, 0.15 seconds) calculated in the aforementioned law of coordinated change of the tailstock clamping force of the grinder is combined with the feed speed set by the current CNC system (for example, set to 50mm / s) for numerical multiplication calculation, and the "theoretical deviation of the axial displacement of the workpiece" can be obtained.This deviation represents the maximum workpiece displacement in the feed direction that could result from an untimely tailstock response. For example, 7.5mm represents the theoretical maximum position deviation caused by tailstock hysteresis. Based on this theoretical deviation, the maximum axial displacement deviation is further identified according to the current machining process and tailstock adjustment logic. Fine-tuning is performed, taking into account factors such as the actual tailstock feedback speed and the grinding feed method (e.g., step feed or continuous feed). For example, a tailstock compression feedback response correction factor is added to ensure that the final output "maximum axial displacement deviation" more closely matches the actual displacement. In the above example, the corrected maximum deviation might be 6.2mm. The system then compares the calculated radial runout prediction with the "radial runout allowance" preset in the product specification (e.g., 1.2mm). If the predicted value exceeds 80% of this allowance (i.e., 0.96mm), the system will flag the "radial accuracy risk level" as a warning. This logic promptly alerts the operator to potential tailstock clamping issues that could lead to product accuracy issues. Similarly, the maximum deviation of the axial displacement is compared with the set "allowable value of axial displacement" (for example, 7.0mm). When the deviation exceeds 80% of this value (i.e. 5.6mm), it is also marked as an axial accuracy risk warning. The system interface will prompt with a red icon, and automatically link the tailstock control module to start the clamping compensation algorithm or suspend the next workpiece processing task. Finally, the system will combine the two risk level states to form a comprehensive assessment of the "trend of limited workpiece geometric accuracy". Among them, when the radial accuracy and axial accuracy are both in the warning state, it is determined to be "severely limited", and the tailstock clamping parameters need to be corrected immediately or the tailstock clamping mechanism needs to be replaced; when only one is a warning, it is judged to be "slightly limited", and the system allows to continue processing but requires to increase the frequency of quality spot checks; when both are normal, it is judged that the current tailstock state has a stable impact on geometric accuracy, which is "stable accuracy". This method realizes the real-time prediction and graded warning of the geometric accuracy risk of the grinder workpiece, and is one of the key quality control mechanisms based on the fusion of Internet of Things data in the present invention.

[0066] Preferably, determining the linkage imbalance characteristics of the grinding machine processing system according to the coordinated variation law of the tailstock clamping force of the grinding machine and the trend of limited geometric accuracy of the workpiece in step S2 includes:

[0067] The response delay time value is extracted from the coordinated change law of the tailstock tightening force of the grinder, and the response delay time is divided by the current processing cycle time to obtain the delay time ratio;

[0068] The radial runout prediction value and the maximum axial displacement deviation extracted from the workpiece geometric accuracy limited trend are weighted and summed, where the radial runout prediction value has a weight of 0.6 and the maximum axial displacement deviation has a weight of 0.4, to obtain the comprehensive deviation index.

[0069] Multiply the delay time ratio by the comprehensive deviation index to obtain the preliminary misalignment coefficient;

[0070] Based on the coordinated change law of the tailstock tightening force of the grinder, the absolute value of the difference between the synchronization ratio and the standard synchronization ratio of 85% is calculated to obtain the synchronization deviation degree.

[0071] Assign values ​​according to the radial accuracy risk level and the axial accuracy risk level in the workpiece geometric accuracy limited trend. When both levels are in warning state, assign a value of 2; when only one level is in warning state, assign a value of 1; when both levels are normal, assign a value of 0 to obtain the accuracy risk coefficient.

[0072] Multiply the linkage offset reference value by the accuracy risk coefficient to obtain the adjusted offset coefficient;

[0073] Based on the coordinated variation law of the tailstock clamping force of the grinder, the unit diameter fluctuation rate is obtained by dividing the clamping force fluctuation range by the workpiece diameter.

[0074] The weighted average of the adjusted misalignment coefficient and the unit diameter fluctuation rate is calculated, where the weight of the adjusted misalignment coefficient is 0.7 and the weight of the unit diameter fluctuation rate is 0.3, and the linkage misalignment characteristics of the grinding machine processing system are obtained.

[0075] In the present embodiment, the latest delay time value is extracted from the response delay time data obtained based on the coordinated variation pattern of the tailstock clamping force of the grinding machine. This value represents the time difference between the tailstock clamping force adjustment action and the actual workpiece response, typically measured in seconds. Then, combined with the single machining cycle time defined by the CNC system in the current machining process (e.g., the total time required to machine a workpiece, typically measured in seconds), a dimensionless "delay time percentage" is calculated by dividing the response delay time by the machining cycle time. This parameter describes the proportion of the response delay in the overall machining cycle. For example, if the delay time is 0.2 seconds and the cycle time is 5 seconds, the delay time percentage is 0.04, meaning that the delay time accounts for 4% of the overall machining cycle. This parameter is used to measure the real-time response of the tailstock action. Based on the radial runout prediction value and the maximum axial displacement deviation calculated earlier in the workpiece geometric accuracy constraint trend, weights of 0.6 and 0.4 are assigned, respectively, and a weighted sum is performed to obtain the "comprehensive deviation index." This operation uses a weight coefficient to reflect the different degrees of influence of the two errors on the overall workpiece accuracy. Radial runout is given a higher weight due to its more direct and significant impact on the workpiece roundness. In implementation, the system retrieves the predicted radial runout and maximum axial displacement deviation data for the current machining process from a database, multiplies them by their corresponding weights, and then adds them together. For example, if the predicted radial runout is 0.8mm and the maximum axial displacement deviation is 5.0mm, the comprehensive deviation index is 0.6 times 0.8 plus 0.4 times 5.0, or 0.48 plus 2.0, for a total of 2.48. This index comprehensively reflects the overall level of machining error. The delay time percentage calculated above is multiplied by the comprehensive deviation index to obtain the "preliminary misalignment coefficient." This coefficient represents the degree of system misalignment caused by the combined effects of response delay and error during machining and is a key value for evaluating the tailstock linkage performance of the grinder. During operation, the delay time percentage and comprehensive deviation index are passed as input parameters to a specially designed monitoring software module, which calculates the misalignment coefficient in real time. For example, if the delay time percentage is 0.04 and the comprehensive deviation index is 2.48, the preliminary misalignment coefficient is 0.0992. Based on the coordinated variation law of the tailstock clamping force of the grinder, the current real-time "synchronization ratio" is extracted by monitoring the synchronization ratio of the clamping force to the spindle and feed system. This ratio represents the frequency of the same direction of the clamping force fluctuation and the increase or decrease of the mechanical action, usually expressed as a percentage. Combined with industry experience, the standard synchronization ratio is set to 85% as a reference value for the ideal synchronization state. The absolute value of the difference between the current synchronization ratio and 85% is calculated to obtain the "synchronization deviation degree", which quantifies the degree to which the current system synchronization deviates from the ideal state. For example, if the real-time synchronization ratio is 78%, the synchronization deviation degree is 7%, reflecting a small deviation in synchronization, which may lead to processing errors.Based on the radial and axial accuracy risk levels from the workpiece geometric accuracy constraint trend analysis results, the risk levels are mapped to numerical "accuracy risk coefficients." The assignment rule is as follows: when both radial and axial directions are in a warning state, the value is 2, indicating a severe risk; when only one is in a warning state, the value is 1, indicating a moderate risk; and when both are normal, the value is 0, indicating a low risk. This numerical risk coefficient facilitates subsequent algorithms to quantify the risk level and provide decision support. For example, if a radial accuracy warning is detected while the axial direction is normal, the accuracy risk coefficient is 1. The aforementioned accuracy risk coefficient is multiplied by a preset "linkage misalignment baseline value" (set based on historical machining data and experience, for example, 0.05) to obtain the "adjusted misalignment coefficient," which reflects the weighted impact of the system misalignment after considering the risk level. For example, if the linkage misalignment baseline value is 0.05 and the accuracy risk coefficient is 2, the adjusted misalignment coefficient is 0.1. This value serves as a reference threshold for adjusting the linkage strategy of the control system. Based on the coordinated variation law of the tailstock clamping force of the grinder, the clamping force fluctuation amplitude range data obtained above is divided by the current workpiece diameter to calculate the "unit diameter fluctuation rate", which is used to describe the clamping force fluctuation intensity per unit workpiece size. The calculation process of this value includes extracting the currently measured clamping force fluctuation amplitude (such as 30 Newtons) and workpiece diameter (such as 100 mm) from the Internet of Things data platform, and obtaining the fluctuation rate (0.3N / mm) through numerical division, providing a standardized reference for subsequent linkage evaluation. Finally, the adjusted imbalance coefficient and unit diameter fluctuation rate are weighted averaged with weights of 0.7 and 0.3 to obtain the final "grinding machine processing system linkage imbalance feature". This feature comprehensively reflects the system response delay, error risk level and force fluctuation intensity. It serves as a key diagnostic indicator of the grinder intelligent monitoring system and is used to guide processing parameter adjustment and maintenance decisions. For example, if the adjusted imbalance coefficient is 0.1 and the unit diameter fluctuation rate is 0.3, the final eigenvalue is 0.7 multiplied by 0.1 plus 0.3 multiplied by 0.3, which is approximately equal to 0.16. This indicates that there is a certain degree of imbalance in the current system linkage, and the operator is advised to pay attention to the status of the clamping device.

[0076] Preferably, step S3 includes the following steps:

[0077] Step S31: extracting a quantitative value of the degree of imbalance based on the linkage imbalance characteristics of the grinding machine processing system, and obtaining the number of workpiece processing batches in the corresponding time period to obtain the imbalance intensity of a single batch;

[0078] The embodiment of the present invention is based on the previously calculated linkage imbalance feature of the grinding machine processing system. First, the real-time value of the imbalance feature is collected by the Internet of Things platform, and the data association processing is performed in combination with the number of processing batches of workpieces in the corresponding time period recorded in the processing log. The specific operation is to accumulate and sum the linkage imbalance feature data of all batches in the same time period, and then divide it by the number of batches in the time period to calculate the "single batch imbalance intensity", which reflects the average intensity of the system imbalance during each batch processing. For example, the cumulative number of imbalance features detected in one hour is 10, and the number of batches of workpieces completed during the period is 50, then the single batch imbalance intensity is 0.2. This step quantifies the impact of imbalance on production batches by combining real-time data collection with batch statistics, which is convenient for subsequent quality analysis.

[0079] Step S32: comparing the single batch imbalance intensity with a preset quality stability reference value. When the single batch imbalance intensity is greater than the reference value, the number of quality stability deviations is recorded. The frequency of deviations within 10 consecutive batches is accumulated and counted to obtain quality stability deviation data.

[0080] The embodiment of the present invention monitors whether there is a deviation in quality stability by numerically comparing the calculated imbalance intensity of a single batch with a preset quality stability reference value. The reference value is usually determined by historical data analysis or process standards, such as 0.15 as a threshold value. When the imbalance intensity of a batch exceeds the reference value, the system automatically records the "number of quality stability deviations" once. In order to ensure the real-time and continuity of deviation statistics, the system maintains a sliding window, counts the frequency of deviations in the last 10 consecutive batches, and calculates the "quality stability deviation data", that is, the percentage of deviations in the total number of batches. For example, if there are 5 deviations in 10 batches, the deviation data is 50%. This statistic helps identify the persistence and regularity of quality deviations.

[0081] Step S33: Correlation matching is performed based on the quality stability deviation data and the grinding machine processing system linkage imbalance characteristics to obtain the product quality stability deviation status. When the frequency of occurrence is greater than 40% and it is a systematic imbalance, the product quality stability deviation status is determined to be a continuous deviation;

[0082] The embodiment of the present invention performs data correlation analysis on the quality stability deviation data obtained in the previous step and the linkage imbalance characteristics of the current grinding machine processing system. This correlation matching is achieved by constructing a multidimensional time series model, and using statistical methods or machine learning algorithms to compare the changing trends of the two to determine whether the quality deviation is closely related to the system linkage imbalance. If the frequency of occurrence of the deviation data exceeds 40%, and the corresponding imbalance characteristics are displayed as systematic and persistent abnormalities (for example, the linkage imbalance characteristics of multiple consecutive cycles exceed the normal range), the product quality stability deviation status is determined to be "persistent deviation", indicating that there are persistent linkage problems in the grinding machine processing process, which affects the product quality stability. This judgment supports the formulation of subsequent control strategies.

[0083] Step S34: determining the degree of deviation of the grinding machine control parameters according to the product quality stability deviation and the grinding machine processing system linkage imbalance characteristics;

[0084] The embodiment of the present invention determines the degree of deviation of the current grinder control parameters based on the deviation of product quality stability in combination with the linkage imbalance characteristics of the grinder processing system. The specific method is to input the numerical value and change trend of the persistent deviation condition and the imbalance characteristic into the parameter adjustment model, and output the control parameter deviation level, which is usually divided into mild, moderate and severe deviations. For example, when the deviation is persistent and the imbalance characteristic value is high, it is determined to be a severe deviation, indicating that rapid adjustment is required. This process relies on the real-time detection of the control parameters by the Internet of Things monitoring system and the comparison with the historical model to accurately locate the abnormal range of the parameters and provide a basis for subsequent adjustments.

[0085] Step S35: Based on the Internet of Things control system, the deviation degree of the grinding machine control parameters is adjusted in real time to obtain the execution status of the grinding machine parameter adjustment.

[0086] The embodiment of the present invention performs real-time automatic adjustment operations based on the degree of deviation of the grinder control parameters determined in step S34 through an integrated Internet of Things control system. The system receives deviation level information in real time, calls preset parameter optimization algorithms and control instructions, and automatically adjusts key parameters of the grinder, such as the tailstock clamping force, feed speed, grinding depth, etc., to ensure that the parameters return to the normal range. During the adjustment process, the system continuously monitors feedback data to form a closed-loop control to ensure the effectiveness and stability of the adjustment. For example, when a severe deviation is detected, the system quickly reduces the clamping force and adjusts the feed speed, while recording the adjustment execution status and effect. This step realizes intelligent control based on real-time monitoring, improving the grinding quality and equipment operation efficiency of the grinder.

[0087] It is particularly important that step S34 includes the following steps:

[0088] Step S341: extracting a sequence of quantitative values ​​of the degree of imbalance during the continuous deviation period based on the product quality stability deviation condition, calculating the standard deviation of the values ​​in the sequence, and obtaining the deviation fluctuation intensity;

[0089] In the previous stage, the embodiment of the present invention has identified the quality problem of "persistent deviation" in the product. The key to this step is to extract the "quantified value of the degree of imbalance" from the linkage imbalance characteristics of the grinding machine processing system during the period of persistent deviation from the Internet of Things platform to form a time series. This quantitative value is the imbalance characteristic indicator obtained by weighting the adjusted imbalance coefficient and the unit diameter fluctuation rate. The system uses time tags to sort these indicators to form a continuous sequence of imbalance values, such as per minute or per batch. Next, the statistical analysis module is called to calculate the standard deviation of the sequence. This standard deviation is the "deviation fluctuation intensity" and is used to describe the stability of the degree of imbalance during the period of persistent deviation. For example, if the sequence of quantitative values ​​of the degree of imbalance within a certain period of time is [2.1, 2.3, 2.2, 2.5, 3.0], the standard deviation is large, indicating severe fluctuations, reflecting the poor stability of the grinding machine control system.

[0090] Step S342: Calculate the control parameter influencing factor by multiplying the deviation fluctuation intensity by the linkage misalignment frequency in the linkage misalignment characteristic of the grinding machine processing system;

[0091] After obtaining the deviation fluctuation intensity, the embodiment of the present invention further analyzes the sensitivity of the control parameters in combination with the "linkage imbalance frequency" in the linkage imbalance characteristics of the grinding machine processing system. The linkage imbalance frequency refers to the number of times the linkage imbalance is detected per unit time, usually expressed as "the number of abnormal detections per hour". In this step, the system multiplies the deviation fluctuation intensity and the linkage imbalance frequency numerically to calculate the "control parameter impact factor", which is used to measure the degree of influence of the control parameters on the system stability during the processing. For example, if the deviation fluctuation intensity is 0.35 and the linkage imbalance frequency is 24 times / hour, the impact factor is 8.4, indicating that the current control parameter change has a strong triggering effect on the system imbalance, which is the core basis for subsequent parameter analysis and adjustment.

[0092] Step S343: extracting effective influence segments with a value range of 1.2-8.5 based on the control parameter influence factor, and counting the duration of the value rising trend in the effective influence segment to obtain the duration of the value rising trend;

[0093] The embodiment of the present invention extracts the key data change stages from the control parameter influencing factors calculated above. The system first selects the segments with a numerical range of 1.2 to 8.5 as "effective influence segments", which represent the control influence stages with actual intervention value in the system. Subsequently, the system detects the "duration of the numerical rising trend" in the effective influence segment through a trend analysis algorithm, that is, how long the control parameter influencing factor continues to be in an upward state. This process combines methods such as sliding windows and fitting regression lines to realize trend judgment, and calculates the continuous rising time. For example, if the influence factor rises from 2.5 to 7.8 within 20 consecutive minutes, the duration is 20 minutes. This information provides a time quantification basis for the subsequent analysis of the cumulative effect of parameter deviations.

[0094] Step S344: Comparing the duration of the numerical upward trend with the current grinding machine operation cycle, and calculating the cumulative amount of parameter deviation when the duration of the numerical upward trend exceeds 60% of the operation cycle, where the cumulative amount of parameter deviation is equal to the product of the control parameter influencing factor and the duration;

[0095] The embodiment of the present invention compares the duration of the numerical upward trend calculated in the previous step with the current operating cycle of the grinder. The operating cycle of the grinder can be determined according to the daily tool change cycle or the average processing time per batch, for example, the current cycle is 30 minutes. When the duration of the trend exceeds 60% of the cycle (i.e. 18 minutes), it is determined that the parameter fluctuation is highly persistent. At this time, the system calculates the "accumulated amount of parameter deviation" based on the product of the "control parameter influencing factor" and the "duration of the upward trend", which represents the overall cumulative effect of the control parameter deviation on the system. For example, if the influence factor is 6.5 and the trend duration is 20 minutes, the accumulated deviation is 130, indicating that the current control system has a significant and persistent parameter imbalance phenomenon, which requires intervention at the control strategy level.

[0096] Step S345: Determine the degree of deviation of the grinding machine control parameters based on the cumulative amount of parameter deviation. When the cumulative amount of parameter deviation is greater than 15, it is marked as a serious deviation; when the cumulative amount of parameter deviation is between 5-15, it is marked as a medium deviation; when the cumulative amount of parameter deviation is less than 5, it is marked as a slight deviation.

[0097] The embodiment of the present invention classifies and judges the degree of deviation of the control parameters of the grinding machine according to the cumulative amount of parameter deviation obtained by the above calculation. The Internet of Things monitoring platform marks different numerical intervals through automated rules. When the cumulative amount of deviation is greater than 15, it is marked as "serious deviation", indicating that the current grinder has a greater risk, such as the need to immediately adjust the feed speed or correct the clamping force curve; when the cumulative amount is between 5 and 15, it is marked as "medium deviation", at which time a mild adjustment strategy can be adopted, such as fine-tuning the spindle load compensation parameters; and when the cumulative amount is less than 5, it is marked as "minor deviation", which can be monitored regularly or slowly corrected in the next batch. The grading result is directly fed back to the intelligent Internet of Things control system, and closed-loop adaptive optimization is achieved in conjunction with the automatic control logic. This step not only improves the control accuracy, but also ensures product processing stability and batch consistency.

[0098] Preferably, step S35 includes the following steps:

[0099] Step S351: Based on the degree of deviation of the grinding machine control parameter, a corresponding adjustment instruction is issued through the Internet of Things control system to obtain an adjustment instruction of the Internet of Things control system. If the degree of deviation of the grinding machine control parameter is severe, the adjustment amplitude is 20% of the current parameter value; if the degree of deviation of the grinding machine control parameter is moderate, the adjustment amplitude is 10% of the current parameter value; if the degree of deviation of the grinding machine control parameter is slight, the adjustment amplitude is 5% of the current parameter value;

[0100] After the embodiment of the present invention has obtained the determination result of the degree of deviation of the control parameters of the grinder in the previous step, the corresponding automatic adjustment instructions are generated through the Internet of Things control platform and sent to the grinder execution unit for real-time control. For example, when it is judged that the degree of deviation of the control parameters in a certain grinding batch is "serious deviation", the system will automatically calculate the adjustment amplitude according to the current setting value of the parameter. For example, if the current value of the spindle speed is 1800rpm, the adjustment amplitude set by the system is 20%, that is, an increase or decrease of 360rpm, forming an adjustment target value of 2160rpm or 1440rpm (the specific adjustment direction is determined based on the deviation trend of the previous stage); when the deviation degree is "medium deviation" or "slight deviation", the adjustment amplitude is calculated as 10% and 5% of the current value respectively. The adjustment instruction is transmitted to the grinding machine system through the PLC controller or edge computing gateway, and the execution feedback mechanism is started to ensure the synchronization and traceability of the adjustment response.

[0101] Step S352: Calculate the adjustment response efficiency by dividing the execution time of the adjustment instruction of the IoT control system by the accumulated parameter deviation, and record the actual parameter change after the adjustment is completed;

[0102] After the Internet of Things platform successfully issues an adjustment instruction, the embodiment of the present invention records the "execution time" of the adjustment instruction in real time, that is, the time taken from issuing the command to the actual change of the grinding machine parameters (the unit is generally seconds or milliseconds). This data can be obtained by the feedback module or high-frequency data acquisition device of the grinding machine. At the same time, the system uses the "accumulated amount of parameter deviation" calculated in the previous stage as the base value, divides it by the execution time, and obtains the "adjustment response efficiency". This efficiency reflects the time required for the unit deviation to be repaired, and is an important indicator for judging the system response sensitivity and control link efficiency. In addition, the system also needs to synchronously record the "actual parameter change", that is, the actual degree of change of the grinding machine parameters after changing from the initial set value. For example, in a certain adjustment, the adjustment target is set to increase the spindle speed by 180rpm, but the system ultimately only increases by 150rpm. The actual change is 150rpm. This data will be used to verify the adjustment execution effect in subsequent steps.

[0103] Step S353: Compare the actual parameter change with the preset expected adjustment range, calculate the adjustment execution deviation rate, and determine that the grinding machine parameter adjustment execution status is effective when the adjustment execution deviation rate is less than 15%. Otherwise, it is determined that the adjustment is abnormal, thereby obtaining the grinding machine parameter adjustment execution status.

[0104] After obtaining the "actual parameter change", the embodiment of the present invention compares the value with the previously set "expected adjustment amplitude", thereby calculating the "adjustment execution deviation rate". The deviation rate reflects the response accuracy of the system after executing the instruction, that is, the relative difference between the actual adjustment amplitude and the expected adjustment amplitude. For example, if the expected adjustment amplitude is 200rpm, but the actual adjustment is only completed at 170rpm, the adjustment execution deviation rate is 15%, and the system compares this value with the preset tolerance threshold of 15%. If the deviation rate is less than the threshold, the system determines that the adjustment is "effective", indicating that the change in the control parameters is basically consistent with the control strategy and the adjustment action is reliable; if the deviation rate is greater than 15%, it is determined to be "adjustment abnormal", indicating that there may be problems such as actuator failure, instruction delay, network interference or insufficient mechanical rigidity. The judgment result is finally recorded in the Internet of Things platform database as "grinding machine parameter adjustment execution status" and can be used for subsequent adaptive control strategy optimization and equipment health management model training.

[0105] Preferably, step S4 includes the following steps:

[0106] Step S41: extracting the adjustment response efficiency and adjustment execution deviation rate based on the grinding machine parameter adjustment execution status, and calculating the ratio of the number of effective adjustments to the number of abnormal adjustments in five consecutive adjustment operations to obtain the system response success rate;

[0107] In the embodiment of the present invention, based on the grinding machine parameter adjustment execution status, adjustment response efficiency and adjustment execution deviation rate obtained in the aforementioned steps, the system summarizes and processes the results of the last five consecutive adjustment operations through the Internet of Things data analysis module. In specific implementation, the system first extracts the adjustment response efficiency and adjustment execution deviation rate corresponding to each adjustment operation, and performs statistics on these five operations based on the judgment results of "adjustment is effective" and "adjustment is abnormal". For example, if three of the five adjustments are effective and two are abnormal, the system response success rate is 60%. This operation can be dynamically updated by setting a sliding time window. Whenever a new adjustment is completed, statistics are re-performed and the success rate is updated for real-time monitoring of the execution stability and reliability of the Internet of Things control system.

[0108] Step S42: When the system response success rate is lower than the preset standard value, the number of response defects is recorded, and the corresponding adjustment execution deviation rate value sequence is extracted, and the average value of the deviation rate value is calculated to obtain the average execution deviation;

[0109] In an embodiment of the present invention, when the system response success rate is detected to be lower than a preset standard value (e.g., 70%), the system marks this situation as a "response defect" and counts the event in the "number of response defect occurrences." At the same time, it extracts the adjustment execution deviation rate values ​​from the corresponding five adjustment operations to form a numerical sequence. For example, the deviation rates within a certain sampling period are 10%, 17%, 13%, 21%, and 9%, respectively. Subsequently, the system averages the numerical sequence to obtain the "average execution deviation," which in this case is 14%. This data reflects the degree of consistency deviation that exists in the actual adjustment process of the IoT control system, providing a basis for the subsequent quantification of defect severity.

[0110] Step S43: performing a numerical multiplication operation based on the number of response defect occurrences and the average execution deviation to obtain a response defect severity index;

[0111] This embodiment of the present invention numerically multiplies the "number of response defect occurrences" and "average execution deviation" acquired in the previous phase to quantify a "response defect severity index." This index describes the combined intensity of system response defects, namely, the combined impact of defect frequency and deviation magnitude. For example, if a system response defect occurs five times during a monitoring phase, with an average execution deviation of 14%, the response defect severity index is 5 times 14, which equals 70. This index can accurately determine whether there are systemic issues with the overall regulatory stability of the control system, helping to warn of risks of decreased control accuracy in equipment.

[0112] Step S44: Calculating the cumulative occurrence frequency of response defects within 20 consecutive adjustment cycles based on the response defect severity index to obtain the response defect frequency;

[0113] This embodiment of the present invention further assesses the periodic trend of the "Response Defect Severity Index." This method uses a sliding window technique to calculate the cumulative frequency of occurrence of this indicator over 20 consecutive adjustment cycles. Specifically, this method measures the number of response defects observed in each of these 20 cycles. For example, if response defects were observed in 8 of the 20 cycles, the response defect frequency is 40%. This frequency data can be automatically recorded and updated using a periodic event log collection module, serving as a key reference for quantifying system performance fluctuations.

[0114] Step S45: performing a weighted sum calculation based on the response defect severity index and the response defect frequency, wherein the response defect severity index weight is 0.6 and the response defect frequency weight is 0.4, to obtain the IoT control system response defect data;

[0115] This embodiment of the present invention uses the "response defect severity index" and "response defect frequency" obtained in the previous step as weighted input variables and performs a weighted summation to construct the "IoT control system response defect data." Weights are 0.6 and 0.4, respectively, indicating that the system prioritizes severity over frequency alone. In actual implementation, the system performs a weighted calculation using an embedded control rule model. For example, if the severity index is 70 and the defect frequency is 40%, the final response defect data is 70 multiplied by 0.6 plus 40 multiplied by 0.4, resulting in 58. This result serves as a unified score for system response performance, providing a foundation for further control optimization.

[0116] Step S46: performing control optimization processing on the grinding machine quality monitoring system according to the defect data of the Internet of Things control system response, and obtaining an optimized real-time monitoring system for the grinding machine product quality.

[0117] After obtaining the "Internet of Things control system response defect data", the embodiment of the present invention compares it with the preset performance threshold. When it is found that the score exceeds the set risk limit (such as 60 points), it automatically triggers the "grinding machine quality monitoring system control optimization processing", which mainly includes the following operations: First, adjust the adjustment sensitivity of the control parameters and narrow the response bandwidth to reduce the risk of large misadjustments; second, optimize the actuator drive frequency and the control loop refresh frequency to enhance real-time performance; third, automatically select redundant parameter adjustment channels for compensation control based on the adjustment execution deviation characteristics; fourth, re-optimize the PID parameters involved in the control rule model or call the deep learning sub-model for nonlinear adjustment prediction. Through the above-mentioned control optimization operations, the system can continuously improve the monitoring accuracy of the grinding machine product quality, realize closed-loop optimization of the quality stability of the grinding process, and finally output the optimized grinding machine product quality real-time monitoring system.

[0118] Preferably, step S46 includes the following steps:

[0119] Step S461: extracting the control parameter adjustment records and grinding machine operation status logs within the corresponding adjustment period based on the IoT control system response defect data, and generating a historical comparison table of adjustment behavior and system response;

[0120] In the embodiment of the present invention, based on the "Internet of Things control system response defect data" obtained in the above steps, the system will automatically extract the adjustment cycle data corresponding to each response defect event, specifically including the control parameter adjustment records involved in the adjustment process (such as the adjustment values ​​and timestamps of parameters such as spindle speed, grinding wheel feed speed, coolant flow rate, etc.) and the grinder operation status log (including the grinder working status such as vibration amplitude, temperature rise, load fluctuation and other real-time operating indicators). These data are collected in real time by the Internet of Things control gateway device embedded in the system, and are correlated and matched with the defect events to finally generate a "historical comparison table of adjustment behavior and system response". The comparison table uses the time axis as the main line to form a one-to-one correspondence between the adjustment behavior of each control parameter and the corresponding system response result (whether the adjustment is effective, response time, deviation rate, etc.), which is used for subsequent adjustment effect tracing and model optimization.

[0121] Step S462: Classify and summarize the historical comparison table based on the adjustment parameter type, response effectiveness level and execution time to form a control parameter execution label library;

[0122] After the construction of the comparison table is completed, the embodiment of the present invention further uses a classification and summary algorithm to summarize and analyze the information in the table, and performs clustering processing mainly based on three dimensions: the first is the adjustment parameter type dimension, that is, classifying the parameter types, such as feed speed type, grinding pressure type, temperature control type, etc.; the second is the response effectiveness level dimension, which divides it into levels such as "efficient response", "general response" and "invalid response" according to whether the adjustment is effective and the deviation rate; the third is the execution time dimension, which divides the behavior into different time gears (such as less than 5 seconds, 5-10 seconds, and more than 10 seconds) according to the time required for the adjustment instruction to be executed to complete the response. After this three-dimensional classification, the system can construct a "control parameter execution label library", in which each label represents a typical response pattern of a certain type of adjustment behavior, providing a quantifiable reference basis for abnormal behavior identification.

[0123] Step S463: Identify parameter types with abnormal responses or high execution deviation rates in the adjustment behavior based on the control parameter execution tag library, and mark them as parameter items to be optimized;

[0124] The embodiment of the present invention is based on the above-mentioned control parameter execution tag library, and introduces a combined strategy based on rule matching and data mining to identify abnormal regulation behavior. First, a statistical analysis is performed on the response effectiveness level and execution deviation rate of each type of regulation parameter in all historical records. When it is found that the abnormal response ratio of a certain type of parameter (such as invalid responses exceeding 30%) or the average execution deviation rate is higher than the system average level (such as exceeding 20%), it is automatically identified as a parameter type with potential control problems. Such parameters are marked as "parameter items to be optimized" by the system, and additional information such as the corresponding number of behavior samples, fault cycle distribution, and involved models is recorded. Taking actual application as an example, if it is found that the "grinding wheel speed control" type parameter has serious response delays and a generally high deviation rate in multiple cycles, the system will automatically include it in the list to be optimized, indicating that there is a problem with the parameter weight or threshold setting in the control model.

[0125] Step S464: Calculate the number of execution failures and average adjustment deviations of the parameters to be optimized within a continuous operation cycle to form a parameter optimization candidate list;

[0126] After identifying the parameter items to be optimized, the embodiment of the present invention further tracks the continuous cycle behavior of these parameter items, counts the number of execution failures in the past several consecutive operation cycles (such as the last 50 adjustment operations), that is, the number of occurrences of "adjustment abnormalities" determined by the system, and calculates the average adjustment deviation value corresponding to these operations. For example, for the "spindle torque adjustment" parameter, there were 15 adjustment abnormalities in the past 50 adjustments, with an average deviation value of 18%. Based on this, the system forms a "parameter optimization candidate list", which contains information such as the abnormal frequency, average deviation, and priority of each parameter to be optimized, which is used to subsequently determine which parameters are most in need of adjustment and guide the selection of optimization strategies.

[0127] Step S465: Based on the parameter optimization candidate list, the parameter optimization configuration processing is performed to obtain the optimized control parameter settings and upload them to the grinding machine quality monitoring system to replace the original parameters, thereby obtaining the optimized grinding machine product quality real-time monitoring system.

[0128] The embodiment of the present invention enters the stage of "adjustment parameter optimization configuration processing" based on the parameter optimization candidate list. In the specific implementation process, the system first calls the intelligent parameter adaptive module to generate optimization suggestions based on the parameter behavior characteristics and fault manifestations in the list. For example, for the "coolant flow adjustment parameter", it is determined through analysis that the abnormal frequency is high and the response stability is poor. The system can adjust its initial setting range (such as raising the default flow upper limit from 2.5 liters / minute to 3.0 liters / minute), and optimize the adjustment step size and feedback update cycle to enhance the system's rapid response capability. The optimized control parameter settings are uploaded to the grinder quality monitoring system via the Internet of Things control master station to replace the original parameter configuration. The update process synchronously triggers version log records and model retraining marks to ensure that the system uses the optimal configuration in future adjustments, and ultimately forms an optimized real-time monitoring system for grinder product quality, realizing closed-loop intelligent control of the equipment.

[0129] It is particularly important that step S465 includes the following steps:

[0130] Extract the number of execution failures and average adjustment deviation values ​​of each parameter item to be optimized from the parameter optimization candidate list, and sort them from high to low according to the number of failures. Mark the parameter items with more than 8 failures as high-priority optimization items, and mark the parameter items with 3-8 failures as medium-priority optimization items.

[0131] The parameter adjustment coefficient is calculated based on the average adjustment deviation value of the high-priority optimization items. When the average adjustment deviation value is greater than 25%, the parameter adjustment coefficient is 0.7; when the average adjustment deviation value is between 15% and 25%, the parameter adjustment coefficient is 0.8; when the average adjustment deviation value is less than 15%, the parameter adjustment coefficient is 0.9;

[0132] Adjust the original parameter thresholds of high-priority optimization items according to the parameter adjustment coefficients, and match the adjusted parameter thresholds with the corresponding response time settings to form an optimized parameter combination.

[0133] Compare the number of execution failures of the medium-priority optimization item with the preset benchmark failure number. When the number of execution failures exceeds the benchmark value, shorten the response time of the corresponding parameter by 10%. When the number of execution failures equals the benchmark value, shorten the response time by 5%. This results in a time adjustment plan for the medium-priority parameter.

[0134] The optimized parameter combination and time adjustment plan are integrated to generate a complete control parameter configuration file. The configuration file is uploaded to the parameter storage module of the grinding machine quality monitoring system through the Internet of Things communication interface. After the parameter replacement operation is completed, the system is restarted for verification, thereby obtaining an optimized real-time monitoring system for grinding machine product quality.

[0135] This embodiment of the present invention extracts the number of execution failures and average adjustment deviations for each parameter to be optimized in the list, then uses the system's built-in data traversal and filtering module to record and analyze parameter behavior. The number of execution failures refers to the total number of times the system determines that a parameter adjustment is invalid or fails to achieve the expected response within a specified monitoring period. The average adjustment deviation represents the average ratio of the error between the system's actual response and the target set value for each invalid adjustment. The system sorts all parameter items from highest to lowest by the number of execution failures and assigns them a priority based on a predefined ranking rule: A failure count of more than eight is marked as a "high-priority optimization item," while a failure count between three and eight is marked as a "medium-priority optimization item." For example, if the "grinding wheel spindle coolant flow rate" parameter had nine failures in the last 50 runs, it would be considered a high-priority item. However, if the "grinding wheel feed speed" parameter had five failures during that period, it would be marked as a medium-priority optimization item. For each parameter marked as high-priority, the system further analyzes its average adjustment deviation and calculates a corresponding "parameter adjustment coefficient" for each item based on the predefined ranking criteria. The parameter adjustment coefficient is the proportional coefficient used by the system to scale the original control parameter threshold range, reflecting the degree of optimization of the adjustment sensitivity. The system judges the deviation range of each high-priority parameter in turn. If the average adjustment deviation value is greater than 25%, the parameter adjustment coefficient is assigned to 0.7, indicating that the parameter deviation is large and its adjustment amplitude or range needs to be significantly reduced; if the deviation value is between 15% and 25%, the coefficient is set to 0.8; if it is less than 15%, it is set to 0.9. For example, the average deviation value of the "spindle torque" parameter is 28%, and the system sets its adjustment coefficient to 0.7, which is used for the next step of revising the original threshold. The original control parameter threshold of the high-priority optimization item is adjusted according to the parameter adjustment coefficient obtained above. Threshold adjustment refers to resetting the upper and lower limits of the control parameter to optimize the adjustment behavior and make the parameter response more stable and effective. The system compresses the adjustment range by multiplying the corresponding parameter adjustment coefficient. For example, the original upper limit of the "spindle torque" is 80 Nm and the lower limit is 40 Nm. If the adjustment coefficient is 0.7, the new upper limit is set to the original value multiplied by 0.7, that is, 56 Nm, and the lower limit is proportionally set to 28 Nm. After the adjustment is completed, the system will match the parameter combination according to the optimal response time (that is, the time setting with fast response and small deviation) matched in the historical adjustment behavior of each parameter, forming an "optimized parameter combination" including the optimized parameter value and the recommended response time setting, so as to improve the dynamic response accuracy and efficiency of the system. For medium-priority optimization items, the system does not adjust the parameter threshold, but focuses on optimizing the "response time". Response time refers to the time from the system receiving the control instruction to the actual completion of the parameter adjustment, which is used to measure the execution efficiency of the control system.The system sets a preset baseline failure count, for example, 5, and compares this value with the actual number of failures for each medium-priority parameter. If the failure count exceeds 5, the response time for that parameter is shortened by 10%; if it equals 5, the response time is shortened by 5%; if it is less than 5, no action is taken. For example, the "grinding wheel feed speed" parameter has 6 failures, exceeding the baseline. Therefore, its original response time was set at 6 seconds, but after optimization, it was set to 5.4 seconds. The system integrates the response time optimization results for all medium-priority parameters into a "time adjustment plan." Finally, the high-priority optimized parameter combinations are combined with the medium-priority time adjustment plan to generate a unified "control parameter configuration file." This file is a structured file recognizable by the system, typically in JSON or XML format, and contains all optimized parameter values, response times, and control tag information. Once generated, the configuration file is uploaded to the parameter storage module of the grinding machine quality monitoring system via the system's built-in IoT communication interface module, replacing the original parameter settings. After the upload is complete, the system automatically triggers a soft restart of the control system to ensure the new parameters take effect and enter real-time monitoring mode. The system will re-run under the new configuration and automatically monitor the response effect of the first batch of adjustment cycles, forming a closed-loop feedback mechanism, thereby realizing control optimization of the real-time monitoring system of CNC grinding machine product quality.

[0136] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced within the present invention.

[0137] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A real-time monitoring and optimization method for CNC grinding machine product quality based on the Internet of Things, characterized in that: The following steps are involved: Step S1: Acquire multi-node real-time operation data of the CNC grinding machine; Count the working status parameters of key components of the grinder based on real-time operation data of multiple nodes; determine the fluctuation trend of product processing quality based on real-time operation data of multiple nodes and working status parameters of key components of the grinder; Step S2: detecting the coordinated variation pattern of the tailstock clamping force of the grinder according to the fluctuation trend of the product processing quality; determining the trend of limited geometric accuracy of the workpiece according to the coordinated variation pattern of the tailstock clamping force of the grinder; and determining the linkage imbalance characteristics of the grinding machine processing system according to the coordinated variation pattern of the tailstock clamping force of the grinder and the trend of limited geometric accuracy of the workpiece; Step S3: Determine the product quality stability deviation based on the grinding machine processing system linkage imbalance characteristics; determine the degree of deviation of the grinding machine control parameters based on the product quality stability deviation and the grinding machine processing system linkage imbalance characteristics; and perform real-time adjustment processing on the degree of deviation of the grinding machine control parameters based on the Internet of Things control system to obtain the execution status of the grinding machine parameter adjustment; Step S4: evaluating the response effect of the Internet of Things control system based on the execution of the grinding machine parameter adjustment to obtain response defect data of the Internet of Things control system; The grinding machine quality monitoring system control optimization processing is performed according to the defect data responded by the Internet of Things control system, and an optimized real-time monitoring system for the grinding machine product quality is obtained.

2. The method for real-time monitoring and optimization of CNC grinding machine product quality based on the Internet of Things according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: synchronously collecting data through a sensor network installed on the tailstock, spindle box, cross feed mechanism, grinding wheel frame and worktable of the grinding machine to obtain multi-node real-time operation data, wherein the multi-node real-time operation data includes tailstock tightening force data, spindle speed data, feed position data, grinding wheel vibration data and worktable temperature data; Step S12: Counting working status parameters of key components of the grinding machine based on real-time operation data of multiple nodes; Step S13: Calculating data fluctuation intensity based on the time series change of each node data in the multi-node real-time operation data; and calculating component coordination deviation based on the change trend of each component status level in the working status parameters of the key components of the grinding machine; Step S14: Perform correlation analysis based on the data fluctuation intensity and component coordination deviation to obtain the product processing quality fluctuation trend, wherein the correlation analysis is specifically when the data fluctuation intensity exceeds the set threshold and the component coordination deviation shows an increasing trend, it is determined that the product processing quality has an unstable fluctuation trend; otherwise, it is determined that the product processing quality maintains a stable trend.

3. The method for real-time monitoring and optimization of CNC grinding machine product quality based on the Internet of Things according to claim 2 is characterized in that: Step S12 includes the following steps: Step S121: Calculating the tailstock workload mean and load fluctuation coefficient based on the tailstock tightening force data, thereby determining the tailstock component working status level; Step S122: Calculating the spindle operation stability and speed consistency index based on the spindle speed data, thereby determining the spindle component working status level; Step S123: determining the working status level of the feed system components according to the feed position data feed accuracy deviation value and feed speed uniformity index; Step S124: Calculating the grinding wheel working balance and vibration intensity index based on the grinding wheel vibration data, thereby obtaining the working status level of the grinding wheel system components; Step S125: using the workbench temperature data to evaluate the thermal deformation risk and temperature stability index, thereby determining the working status level of the thermal control system components; Step S126: combining the tailstock component working status level, the spindle component working status level, the feed system component working status level, the grinding wheel system component working status level, and the thermal control system component working status level to form working status parameters of key components of the grinding machine.

4. The method for real-time monitoring and optimization of CNC grinding machine product quality based on the Internet of Things according to claim 3 is characterized in that: In step S2, detecting the coordinated change rule of the tailstock tightening force of the grinding machine according to the fluctuation trend of the product processing quality includes: According to the fluctuation trend of product processing quality, the numerical sequence of the tailstock tightening force data within the time window is extracted, and the difference in the tightening force values ​​between consecutive time points is calculated to obtain the tightening force change gradient; Calculate the mean and standard deviation of the tightening force change gradient to determine the base change state of the tailstock tightening force; The timestamp correspondence between the tailstock's tightening force and the spindle speed is extracted based on the tailstock's tightening force benchmark change state, and the change trend of the numerical ratio of the two sets of data at the same time point is calculated. The increase and decrease synchronization of the tailstock clamping force and the feed position in the state of the tailstock clamping force benchmark change is analyzed through a sliding time window, and the ratio of the number of changes in the same direction to the number of changes in the opposite direction is calculated to obtain the synchronization ratio. The response delay time is determined according to the numerical ratio change trend and synchronization ratio, so as to detect the coordinated change law of the tightening force of the tailstock of the grinding machine.

5. The method for real-time monitoring and optimization of CNC grinding machine product quality based on the Internet of Things according to claim 4 is characterized in that: In step S2, determining the workpiece geometric accuracy limitation trend according to the coordinated variation law of the tailstock clamping force of the grinder includes: According to the coordinated variation law of the tailstock tightening force of the grinder, a continuous tightening force numerical sequence is extracted, and the local maximum value in the numerical sequence is identified as the peak value and the local minimum value as the valley value. Based on the identified peak and valley values, the numerical difference between the peak and valley values ​​is calculated, and the maximum numerical difference is recorded as the tightening force fluctuation amplitude range; When the workpiece diameter is φ10mm-φ200mm, a proportional coefficient is generated based on the clamping force fluctuation range and the workpiece diameter; The radial runout prediction value is calculated based on the proportional coefficient. When the proportional coefficient is between 0.001 and 0.01, the radial runout prediction value = proportional coefficient × workpiece diameter × 0.5; when the proportional coefficient is greater than 0.01, the radial runout prediction value = proportional coefficient × workpiece diameter × 0.8; The theoretical deviation of the workpiece axial displacement is obtained by numerically multiplying the response delay time in the coordinated change law of the tailstock clamping force of the grinder with the current feed speed. Calculate the maximum axial displacement deviation of the workpiece based on the theoretical axial displacement deviation of the workpiece; Compare the predicted radial runout value with the preset radial runout allowable value. When the predicted value is greater than 80% of the allowable value, mark the radial accuracy risk level as warning. Compare the maximum axial displacement deviation with the preset axial displacement allowable value. When the deviation is greater than 80% of the allowable value, mark the axial accuracy risk level as warning. The trend of workpiece geometric accuracy limitation is determined based on the combined status of the radial accuracy risk level and the axial accuracy risk level. When both the radial accuracy risk level and the axial accuracy risk level are warning levels, the trend of geometric accuracy limitation is determined to be severely limited; when only one level is a warning level, it is determined to be slightly limited; when both levels are normal, it is determined to be stable in accuracy.

6. The method for real-time monitoring and optimization of CNC grinding machine product quality based on the Internet of Things according to claim 5 is characterized in that: In step S2, the characteristics of the linkage imbalance of the grinding machine processing system are determined based on the coordinated change law of the tailstock clamping force of the grinding machine and the trend of limited geometric accuracy of the workpiece, including: The response delay time value is extracted from the coordinated change law of the tailstock tightening force of the grinder, and the response delay time is divided by the current processing cycle time to obtain the delay time ratio; The radial runout prediction value and the maximum axial displacement deviation extracted from the workpiece geometric accuracy limited trend are weighted and summed, where the radial runout prediction value has a weight of 0.6 and the maximum axial displacement deviation has a weight of 0.4, to obtain the comprehensive deviation index. Multiply the delay time ratio by the comprehensive deviation index to obtain the preliminary misalignment coefficient; Based on the coordinated change law of the tailstock tightening force of the grinder, the absolute value of the difference between the synchronization ratio and the standard synchronization ratio of 85% is calculated to obtain the synchronization deviation degree. Assign values ​​according to the radial accuracy risk level and the axial accuracy risk level in the workpiece geometric accuracy limited trend. When both levels are in warning state, assign a value of 2; when only one level is in warning state, assign a value of 1; when both levels are normal, assign a value of 0 to obtain the accuracy risk coefficient. Multiply the linkage offset reference value by the accuracy risk coefficient to obtain the adjusted offset coefficient; Based on the coordinated variation law of the tailstock clamping force of the grinder, the unit diameter fluctuation rate is obtained by dividing the clamping force fluctuation range by the workpiece diameter. The weighted average of the adjusted misalignment coefficient and the unit diameter fluctuation rate is calculated, where the weight of the adjusted misalignment coefficient is 0.7 and the weight of the unit diameter fluctuation rate is 0.3, and the linkage misalignment characteristics of the grinding machine processing system are obtained.

7. The method for real-time monitoring and optimization of CNC grinding machine product quality based on the Internet of Things according to claim 6 is characterized in that: Step S3 includes the following steps: Step S31: extracting a quantitative value of the degree of imbalance based on the linkage imbalance characteristics of the grinding machine processing system, and obtaining the number of workpiece processing batches in the corresponding time period to obtain the imbalance intensity of a single batch; Step S32: comparing the single batch imbalance intensity with a preset quality stability reference value. When the single batch imbalance intensity is greater than the reference value, the number of quality stability deviations is recorded. The frequency of deviations within 10 consecutive batches is accumulated and counted to obtain quality stability deviation data. Step S33: Correlation matching is performed based on the quality stability deviation data and the grinding machine processing system linkage imbalance characteristics to obtain the product quality stability deviation status. When the frequency of occurrence is greater than 40% and it is a systematic imbalance, the product quality stability deviation status is determined to be a continuous deviation; Step S34: determining the degree of deviation of the grinding machine control parameters according to the product quality stability deviation and the grinding machine processing system linkage imbalance characteristics; Step S35: Based on the Internet of Things control system, the deviation degree of the grinding machine control parameters is adjusted in real time to obtain the execution status of the grinding machine parameter adjustment.

8. The method for real-time monitoring and optimization of CNC grinding machine product quality based on the Internet of Things according to claim 7 is characterized in that: Step S35 includes the following steps: Step S351: Based on the degree of deviation of the grinding machine control parameter, the IoT control system issues a corresponding adjustment instruction to obtain an adjustment instruction of the IoT control system. If the degree of deviation of the grinding machine control parameter is severe, the adjustment amplitude is 20% of the current parameter value; if the degree of deviation of the grinding machine control parameter is moderate, the adjustment amplitude is 10% of the current parameter value; if the degree of deviation of the grinding machine control parameter is slight, the adjustment amplitude is 5% of the current parameter value; Step S352: Calculate the adjustment response efficiency by dividing the execution time of the adjustment instruction of the IoT control system by the accumulated parameter deviation, and record the actual parameter change after the adjustment is completed; Step S353: Compare the actual parameter change with the preset expected adjustment range, calculate the adjustment execution deviation rate, and determine that the grinding machine parameter adjustment execution status is effective when the adjustment execution deviation rate is less than 15%. Otherwise, it is determined that the adjustment is abnormal, thereby obtaining the grinding machine parameter adjustment execution status.

9. The method for real-time monitoring and optimization of CNC grinding machine product quality based on the Internet of Things according to claim 8 is characterized in that: Step S4 includes the following steps: Step S41: extracting the adjustment response efficiency and adjustment execution deviation rate based on the grinding machine parameter adjustment execution status, and calculating the ratio of the number of effective adjustments to the number of abnormal adjustments in five consecutive adjustment operations to obtain the system response success rate; Step S42: When the system response success rate is lower than the preset standard value, the number of response defects is recorded, and the corresponding adjustment execution deviation rate value sequence is extracted, and the average value of the deviation rate value is calculated to obtain the average execution deviation; Step S43: performing a numerical multiplication operation based on the number of response defect occurrences and the average execution deviation to obtain a response defect severity index; Step S44: Calculating the cumulative occurrence frequency of response defects within 20 consecutive adjustment cycles based on the response defect severity index to obtain the response defect frequency; Step S45: performing a weighted sum calculation based on the response defect severity index and the response defect frequency, wherein the response defect severity index weight is 0.6 and the response defect frequency weight is 0.4, to obtain the IoT control system response defect data; Step S46: performing control optimization processing on the grinding machine quality monitoring system according to the defect data of the Internet of Things control system response, and obtaining an optimized real-time monitoring system for the grinding machine product quality.

10. The method for real-time monitoring and optimization of CNC grinding machine product quality based on the Internet of Things according to claim 9 is characterized in that: Step S46 includes the following steps: Step S461: extracting the control parameter adjustment records and grinding machine operation status logs within the corresponding adjustment period based on the IoT control system response defect data, and generating a historical comparison table of adjustment behavior and system response; Step S462: Classify and summarize the historical comparison table based on the adjustment parameter type, response effectiveness level and execution time to form a control parameter execution label library; Step S463: Identify parameter types with abnormal responses or high execution deviation rates in the adjustment behavior based on the control parameter execution tag library, and mark them as parameter items to be optimized; Step S464: Calculate the number of execution failures and average adjustment deviations of the parameters to be optimized within a continuous operation cycle to form a parameter optimization candidate list; Step S465: Based on the parameter optimization candidate list, the parameter optimization configuration processing is performed to obtain the optimized control parameter settings and upload them to the grinding machine quality monitoring system to replace the original parameters, thereby obtaining the optimized grinding machine product quality real-time monitoring system.

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