Workpiece grinding management method, system and equipment

Abstract

The present invention relates to the field of metal grinding processing technology, and in particular to a workpiece grinding management method, system and equipment. The method includes the following steps: obtaining workpiece information and grinding processing information; establishing a multi-scale management model based on the workpiece information and grinding processing information, and setting a management threshold in the multi-scale management model; the multi-scale management model obtains multiple processing parameters in the grinding processing information at set time intervals, and then uses the processing parameters as initial features, fuses the initial features to obtain management features, and through kernel principal component analysis feature fusion and fuzzy control, it can dynamically identify the grinding wheel wear state and generate a nonlinear compensation strategy. Specifically, when the principal component extracted by the kernel principal component analysis shows that the grinding wheel enters the accelerated wear stage, the fuzzy controller will automatically adjust the compensation intensity according to the error change rate, avoiding the compensation deviation caused by the linear assumption of the traditional method.

Description

Technical Field

[0001] The present invention relates to the technical field of metal grinding processing, and in particular to a workpiece grinding management method, system and equipment. Background Art

[0002] With the continuous development of the grinding industry, traditional manual operations can no longer meet the needs of modern processing. Harsh environment, low efficiency, reliance on workers' experience, and low processing consistency have become urgent problems that need to be solved in the grinding and polishing industry. In order to meet the automation development requirements of the industry, the use of industrial automated processing systems to replace manual labor has become an inevitable trend.

[0003] In existing automated grinding systems, particularly in the later stages of grinding, wear on the grinding wheel or belt reduces the amount of workpiece removed. This reduction in workpiece removal no longer meets process requirements, necessitating adjustments to the grinding parameters. Specifically, existing grinding systems compensate for this loss by linearly increasing the grinding wheel or belt speed. However, wheel or belt wear is nonlinear, making this approach ineffective.

[0004] In addition, parameter detection in the traditional grinding process mainly relies on manual judgment, which is highly dependent on the workers' experience and requires shutdown for detection, affecting production efficiency. Therefore, it is very necessary to establish a workpiece grinding management method, system and equipment. Summary of the Invention

[0005] The main purpose of the present invention is to provide a workpiece grinding management method, system and equipment, which aims to manage the grinding wheel state during workpiece grinding and perform nonlinear compensation of the grinding process through PLC according to the grinding wheel state.

[0006] To achieve the above objectives, an embodiment of the present invention provides a workpiece grinding management method, which includes the following steps:

[0007] Obtain workpiece information and grinding process information;

[0008] Establish a multi-scale management model based on workpiece information and grinding process information, and set management thresholds within the multi-scale management model;

[0009] Acquiring multiple processing parameters in the grinding process information at set time intervals based on the multi-scale management model; wherein the processing parameters include grinding wheel speed, workpiece rotation speed, and grinding depth;

[0010] The processing parameters are used as the initial features, and the initial features are fused to obtain the management features. If the management features are greater than or equal to the management threshold, the correction management mode is entered. If the management features are less than the management threshold, the PI management mode is entered. The correction management mode includes: inputting the management features into the PLC, adjusting the weight ratio of the parameters in the management features, generating the management output through the fuzzy rule table, and converting the management output into the control quantity of the PLC after fuzzification through dequantization.

[0011] Optionally, the multi-scale management model includes a target layer, a data layer, a fusion layer, a management layer, and an interaction layer;

[0012] The multi-scale management model is established based on the workpiece information and the grinding process information, and the management threshold is set within the multi-scale management model, including:

[0013] Enter workpiece information in the target layer;

[0014] Receive the grinding process information collected by the acquisition module and construct it into a data layer together with the workpiece information;

[0015] In the fusion layer, the information in the data layer is sequentially preprocessed, feature extracted, normalized, and kernel principal component analysis feature fusion is performed to obtain management features;

[0016] Management thresholds are set within the management layer through the interaction layer.

[0017] Optionally, the process of kernel principal component analysis feature fusion includes:

[0018] Map the normalized feature matrix to a high-dimensional space and calculate the kernel matrix;

[0019] Centralize the kernel matrix;

[0020] Solve for the eigenvalues and eigenvectors of the kernel matrix;

[0021] Arrange in descending order of eigenvalues, select the principal components whose cumulative contribution rate exceeds the threshold, and obtain the principal component matrix after dimensionality reduction as the management feature.

[0022] Optionally, the multi-scale management model is communicatively connected to a neural network model, and the neural network model is used to divide the training set and the test set through historical processing data, and to dynamically update the management features.

[0023] Optionally, the hidden layer of the neural network model adopts a hyperbolic tangent function, and the output layer of the neural network model adopts a linear function.

[0024] Optionally, the management feature includes feature error and error change, and the correction management mode further includes:

[0025] The characteristic error and error change are normalized to the fuzzy domain through the quantization factor and input into the fuzzy controller with the correction factor. The correction factor is used to adjust the weight ratio of the characteristic error and the error change, and generate the control quantity through the fuzzy rule table.

[0026] Optionally, the PI management mode includes:

[0027] The control amount is calculated using the proportional gain and integral gain.

[0028] Optionally, the method further comprises: setting a management feature when the workpiece is not in contact with the grinding wheel as an abnormal feature, and issuing an alarm when the abnormal feature is triggered.

[0029] A workpiece grinding management system, comprising:

[0030] Information acquisition module, used to obtain workpiece information and grinding process information;

[0031] A threshold setting module is used to establish a multi-scale management model based on workpiece information and grinding process information, and to set a management threshold within the multi-scale management model;

[0032] A processing parameter acquisition module, which acquires multiple processing parameters in the grinding process information at set time intervals based on the multi-scale management model; wherein the processing parameters include grinding wheel speed, workpiece speed and grinding depth;

[0033] The data processing module is used to use the processing parameters as the initial features, fuse the initial features to obtain the management features, and enter the correction management mode if the management features are greater than or equal to the management threshold; if the management features are less than the management threshold, enter the PI management mode; wherein, the correction management mode includes: inputting the management features into the PLC, adjusting the weight ratio of the parameters within the management features, generating the management output through the fuzzy rule table, and converting the management output into the control quantity of the PLC after fuzzification through dequantization.

[0034] A computer device includes a memory and a processor, wherein the memory stores a computer program and the processor executes the computer program.

[0035] A workpiece grinding management method, system and equipment proposed in an embodiment of the present invention can dynamically identify the wear state of the grinding wheel and generate a nonlinear compensation strategy through kernel principal component analysis feature fusion and fuzzy control. Specifically, when the principal component extracted by kernel principal component analysis shows that the grinding wheel enters the accelerated wear stage, the fuzzy controller will automatically adjust the compensation intensity according to the error change rate, avoiding the compensation deviation caused by the linear assumption of the traditional method; in addition, the full link from data acquisition to decision execution is realized through a hierarchical structure. In the early stage of grinding wheel wear, the real-time vibration signal of the data layer is processed by the fusion layer to generate a low-dimensional management feature. The management layer determines that it does not exceed the threshold, and the PLC maintains the PI control mode; when the wear intensifies and the characteristic value exceeds the limit, the system automatically switches to the fuzzy control mode and dynamically adjusts the grinding wheel speed. The above process does not require human intervention, which solves the efficiency bottleneck of traditional reliance on shutdown detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 Schematic diagram of the process of the present invention.

[0037] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0039] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0040] In the present invention, unless otherwise specified or limited, the terms "connection" and "fixation" should be understood in a broad sense. For example, "fixation" can mean fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two elements or interaction between two elements, unless otherwise specified. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0041] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the meaning of "and / or" appearing throughout the text includes three parallel schemes. Taking "A and / or B" as an example, it includes scheme A, or scheme B, or a scheme in which A and B are satisfied at the same time. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0042] Example 1:

[0043] As attached Figure 1 As shown, this embodiment provides a workpiece grinding management method, which includes the following steps:

[0044] Obtain workpiece information and grinding process information;

[0045] Establish a multi-scale management model based on workpiece information and grinding process information, and set management thresholds within the multi-scale management model;

[0046] Acquiring multiple processing parameters in the grinding process information at set time intervals based on the multi-scale management model; wherein the processing parameters include grinding wheel speed, workpiece rotation speed, and grinding depth;

[0047] The processing parameters are used as the initial features, and the initial features are fused to obtain the management features. If the management features are greater than or equal to the management threshold, the correction management mode is entered. If the management features are less than the management threshold, the PI management mode is entered. The correction management mode includes: inputting the management features into the PLC, adjusting the weight ratio of the parameters in the management features, generating the management output through the fuzzy rule table, and converting the management output into the control quantity of the PLC after fuzzification through dequantization.

[0048] It should be noted that existing grinding management methods and systems usually use linear compensation, such as increasing the grinding wheel speed at a fixed rate, but the grinding wheel wear actually presents nonlinear characteristics. The nonlinear characteristics are specifically reflected in slow wear in the early stage and accelerated wear in the later stage.

[0049] Based on the above problems, this embodiment can dynamically identify the wear status of the grinding wheel and generate a nonlinear compensation strategy through kernel principal component analysis feature fusion and fuzzy control. Specifically, when the principal component extracted by kernel principal component analysis shows that the grinding wheel enters the accelerated wear stage, the fuzzy controller will automatically adjust the compensation intensity according to the error change rate, avoiding the compensation deviation caused by the linear assumption of the traditional method.

[0050] More specifically, the method in this embodiment first constructs a multi-scale management model consisting of a target layer, a data layer, a fusion layer, a management layer, and an interaction layer. It integrates workpiece information and processing parameters in real time, and performs nonlinear feature dimensionality reduction on multi-source data through kernel principal component analysis to generate management features that reflect the wear status of the grinding wheel. On this basis, the system dynamically compares the management features with preset thresholds, triggering intelligent switching of the PLC control mode: when the feature value exceeds the threshold, fuzzy control is used to adjust the parameter weights and generate a nonlinear compensation strategy to cope with accelerated wear of the grinding wheel; when the feature value is below the threshold, it switches to PI control to maintain steady-state processing. This solution effectively solves the compensation deviation problem caused by nonlinear wear of the grinding wheel. At the same time, through real-time data acquisition and online decision-making, it eliminates the reliance on downtime of traditional manual inspection and significantly improves processing efficiency and consistency.

[0051] The system acquires workpiece information and grinding process information in real time through the acquisition module, and integrates this information into a multi-scale management model. The model includes the target layer, data layer, fusion layer, management layer and interaction layer, and each layer has a clear division of labor.

[0052] The target layer is responsible for entering the process requirements of the workpiece, including target size, surface roughness, etc., to form the processing benchmark;

[0053] The data layer integrates the real-time collected processing parameters and the process data of the target layer. For example, the real-time collected processing parameters and target layer data are integrated to build a structured data set. For example, the sensor collects the grinding wheel vibration signal at a frequency of 10 times per second and stores it synchronously with the speed data fed back by the PLC. The processing parameters include grinding wheel speed, workpiece speed, grinding depth, etc.

[0054] The fusion layer achieves feature fusion of multi-source data through preprocessing, feature extraction, standardization and kernel principal component analysis (KPCA). Preprocessing includes denoising and normalization; feature extraction includes extracting the frequency domain features of the vibration signal; standardization includes eliminating dimensional differences.

[0055] The management layer sets dynamic management thresholds and works in conjunction with the PLC to switch control modes;

[0056] The interaction layer provides a human-computer interface to adjust thresholds and model parameters.

[0057] The above-mentioned layered design ensures the continuity of the entire process from data collection to decision-making, laying a data foundation for subsequent intelligent control.

[0058] In addition, this embodiment achieves full-link connectivity from data collection to decision-making execution through a hierarchical structure. In the early stage of grinding wheel wear, the real-time vibration signal of the data layer is processed by the fusion layer to generate low-dimensional management features. The management layer determines that it does not exceed the threshold, and the PLC maintains the PI control mode; when the wear intensifies and the characteristic value exceeds the limit, the system automatically switches to fuzzy control mode and dynamically adjusts the grinding wheel speed. The above process does not require human intervention, which solves the efficiency bottleneck of traditional reliance on shutdown detection.

[0059] It should be noted that kernel principal component analysis reduces the multi-dimensional processing parameters to key management features through the following steps:

[0060] Nonlinear mapping: The standardized feature matrix is mapped to a high-dimensional space through a Gaussian kernel function to capture the nonlinear relationship between parameters.

[0061] Kernel matrix calculation and centering: Calculate the kernel matrix of high-dimensional space samples and perform centering on them to eliminate data offset.

[0062] Feature extraction: Calculate the eigenvalues and eigenvectors of the kernel matrix, sort by contribution rate, and select the principal components with a cumulative contribution rate exceeding 85% to form a management feature matrix after dimensionality reduction. For example, the original 10-dimensional parameters are compressed into three principal components after KPCA processing, reflecting the degree of grinding wheel wear, processing stability, and thermal effect, respectively.

[0063] It can be understood that KPCA effectively identifies the stage characteristics of grinding wheel wear through nonlinear mapping, including but not limited to initial linear wear and later accelerated wear. When the grinding wheel enters the accelerated wear stage, the principal component extracted by KPCA will show a mutation trend, triggering the fuzzy controller to increase the compensation intensity. Compared with traditional linear PCA, KPCA's ability to analyze nonlinear relationships makes the management characteristics closer to the actual wear state, and the compensation accuracy is improved by more than 20%.

[0064] In some embodiments, the workpiece information includes workpiece material, processing size, surface roughness, etc.

[0065] In some embodiments, the grinding process information includes grinding wheel speed, workpiece rotation speed, grinding depth, etc.

[0066] In some embodiments, after the processing parameters in the fusion layer are standardized, kernel principal component analysis (KPCA) is used to perform nonlinear feature dimensionality reduction. KPCA can effectively capture the nonlinear characteristics of grinding wheel wear by mapping data into a high-dimensional space and extracting principal components, thereby generating management features that reflect the actual state of the grinding wheel. Compared with traditional linear principal component analysis, KPCA significantly improves the accuracy of feature extraction and is particularly suitable for complex nonlinear working conditions in the grinding process.

[0067] In some embodiments, the nonlinear characteristic includes an abrupt signal change during an accelerated wear phase.

[0068] In some embodiments, the system dynamically switches the control mode of the PLC based on the comparison results of the management characteristics and the preset threshold.

[0069] Specifically, when the management feature exceeds the threshold, the system enters the fuzzy control mode. By adjusting the weight ratio of the error and the error change and combining the fuzzy rule table, a nonlinear compensation strategy is generated. The introduction of fuzzy control can adapt to the nonlinear characteristics of grinding wheel wear and avoid the over-compensation or under-compensation problems caused by traditional linear compensation.

[0070] Likewise, when the management characteristic is below a threshold, proportional-integral (PI) control is employed to maintain steady-state processing, balancing response speed and stability.

[0071] In this embodiment, the multi-scale management model includes a target layer, a data layer, a fusion layer, a management layer, and an interaction layer;

[0072] The multi-scale management model is established based on the workpiece information and the grinding process information, and the management threshold is set within the multi-scale management model, including:

[0073] Enter workpiece information in the target layer;

[0074] Receive the grinding process information collected by the acquisition module and construct it into a data layer together with the workpiece information;

[0075] In the fusion layer, the information in the data layer is sequentially preprocessed, feature extracted, normalized, and kernel principal component analysis feature fusion is performed to obtain management features;

[0076] Management thresholds are set within the management layer through the interaction layer.

[0077] In some embodiments, the management threshold includes an upper limit on grinding wheel wear.

[0078] In some embodiments, the acquisition module includes a power sensor, a vibration sensor, a temperature sensor, a current sensor, a displacement sensor, a roughness monitor, etc., wherein the vibration sensor acquires signals at a sampling frequency of 10kHz and inputs the signals into the system after denoising through Kalman filtering.

[0079] In some embodiments, the preprocessing process is used to eliminate noise interference in the original data, unify the data scale, and provide high-quality input for subsequent feature extraction. Specifically, it includes:

[0080] Data cleaning to eliminate outliers or invalid data. For example, if the grinding wheel speed collected by the sensor exceeds the rated range of the equipment, the system will automatically mark it as abnormal data and eliminate it;

[0081] Denoising uses the Kalman filter, which is suitable for real-time denoising of dynamic signals. Its state equation and observation equation are:

[0082] ;

[0083] in, For the system at time k The state vector represents the real physical quantity that needs to be estimated, for example, the instantaneous amplitude of the grinding wheel vibration;

[0084] A is the state transition matrix, indicating that the system state changes from k -1 moment evolution to k time;

[0085] is the process noise;

[0086] Observed values, such as the data actually measured by the sensor, such as the vibration sensor reading;

[0087] H As the observation matrix, the system state Mapping to observation space;

[0088] is the observation noise.

[0089] It can be understood that the Kalman filter outputs the optimal estimate through recursive calculation, combining model predictions and actual observations.

[0090] For non-stationary signals, such as sudden vibrations in the grinding process, Daubechies wavelet basis is used for multi-scale decomposition, and soft threshold processing is performed on high-frequency coefficients to retain effective signal components.

[0091] Normalization scales parameters of different dimensions to a uniform range. For example, grinding depth and grinding wheel speed are normalized using minimum-maximum normalization:

[0092] For feature extraction, key features reflecting the grinding wheel status are extracted from the preprocessed data, covering the time domain, frequency domain and statistical characteristics.

[0093] The statistics of time domain features include mean, variance, kurtosis, skewness and waveform indicators;

[0094] Frequency domain features include fast Fourier transform and power spectral density, such as converting vibration signals from the time domain to the frequency domain and extracting the amplitude and frequency of the main frequency components.

[0095] For normalization, the dimensional differences of different features are eliminated to avoid some features dominating the model due to their large numerical range. In this embodiment, Z-score normalization is preferably used to convert the feature data into a distribution with a mean of 0 and a standard deviation of 1:

[0096] ;

[0097] Among them, μ is the characteristic mean, σ is the standard deviation, x is the characteristic parameter.

[0098] Grinding wheel speed For example, the mean is 1500rpm and the standard deviation is 200rpm. The corresponding values are:

[0099] .

[0100] It can be understood that after standardization, parameters such as grinding wheel speed and grinding depth are comparable in the model, avoiding the bias of KPCA principal components towards large numerical features due to dimensional differences.

[0101] In this embodiment, the process of kernel principal component analysis feature fusion includes:

[0102] Map the normalized feature matrix to a high-dimensional space and calculate the kernel matrix;

[0103] Centralize the kernel matrix;

[0104] Solve for the eigenvalues and eigenvectors of the kernel matrix;

[0105] Arrange in descending order of eigenvalues, select the principal components whose cumulative contribution rate exceeds the threshold, and obtain the principal component matrix after dimensionality reduction as the management feature.

[0106] Among them, the processing parameters include grinding wheel speed, workpiece speed, and grinding depth.

[0107] For kernel principal component analysis:

[0108] The nonlinear mapping practical Gaussian kernel function is used to map the standardized feature matrix to a high-dimensional space to capture the nonlinear relationship between parameters.

[0109] In some embodiments, the main steps of kernel principal component analysis include: data preparation, kernel matrix calculation, kernel matrix centering, eigendecomposition, principal component extraction, and data dimensionality reduction.

[0110] Among them, the obtained multiple processing parameters are defined as the characteristic matrix of the original data X , the dimension is ,in N is the sample size, D is the original feature dimension, which can be: grinding wheel speed , workpiece speed , grinding depth d , vibration amplitude wait.

[0111] The original data feature matrix at this time satisfies:

[0112] .

[0113] For the kernel matrix, the Gaussian kernel function (RBF kernel) is used to map the standardized feature matrix X to a high-dimensional space:

[0114] ;

[0115] Among them, σ is the kernel width parameter, which controls the smoothness of the Gaussian function.

[0116] For samples and The square of the Euclidean distance.

[0117] Kernel Matrix K When calculated, its dimension is ;

[0118] And satisfy:

[0119] ;

[0120] For the i samples and j The similarity of samples in high-dimensional space.

[0121] For kernel matrix centering, the mean of the data in high-dimensional space is made to be 0, satisfying:

[0122] ;

[0123] in, Dimension An all-1 matrix.

[0124] For eigendecomposition, center the matrix Decomposed into eigenvalue λ and eigenvector α.

[0125] For principal component extraction, select c The eigenvector corresponding to the largest eigenvalue is used to obtain the data after dimensionality reduction:

[0126] ;

[0127] in, c is the dimension after dimensionality reduction,

[0128] is the management feature matrix after dimensionality reduction.

[0129] Taking the actual grinding process of a certain workpiece as an example, the kernel technique is used to capture nonlinear relationships, which can more accurately characterize the wear state of the grinding wheel during grinding. Compared with traditional PCA, the correlation coefficient between its principal components and the actual working conditions is increased by 15% to 20%.

[0130] Example 2:

[0131] The multi-scale management model is connected to a neural network model for communication. The neural network model is used to divide the training set and the test set through historical processing data, and is used to dynamically update the management features.

[0132] In this embodiment, the hidden layer of the neural network model adopts a hyperbolic tangent function, and the output layer of the neural network model adopts a linear function.

[0133] It can be understood that the technical solution in this embodiment, based on Example 1, further introduces neural network dynamic optimization and fuzzy-PI dual-mode control technology, which systematically solves the problems of control lag, insufficient compensation accuracy and strong dependence on manual parameter adjustment caused by nonlinear wear of the grinding wheel in traditional grinding processing, while significantly improving the system's adaptability, resource utilization efficiency and safety.

[0134] Specifically,

[0135] After the neural network model is introduced in this embodiment, the system can use historical data to dynamically optimize management features, enhance adaptive capabilities, and achieve long-term effects that become more accurate with use. The modular design of the multi-scale model supports rapid adaptation to different processing scenarios and reduces deployment costs; and the abnormal feature detection mechanism greatly improves equipment safety. Through precise wear compensation and resource optimization, the life of the grinding wheel is extended by 15% to 20%, and the cost of consumables is significantly reduced. In summary, the technical solution in this embodiment not only overcomes the technical bottleneck of traditional grinding, but also provides an efficient, reliable and economical technical path for the intelligent upgrade of the metal processing industry through closed-loop control, self-learning capabilities and scalability design.

[0136] More specifically,

[0137] The historical processing data is divided into a training set and a test set in a ratio of 7:3. The training set contains each process parameter and its corresponding optimal compensation amount. .

[0138] The input layer receives the management feature matrix output by the multi-scale management model The hidden layer uses the hyperbolic tangent activation function to enhance the nonlinear fitting ability and satisfy:

[0139] ;

[0140] in, is the weight, is the bias, For the i input features, No. j The bias term of the hidden layer neurons, For the j The output value of the hidden layer neurons, n is the dimension of the input features.

[0141] Finally, the output layer uses a linear function to output the compensation amount ,

[0142] .

[0143] It should be noted that this embodiment also has a dynamic update mechanism. After every 100 processing cycles, the system automatically fine-tunes the network weights with the newly added data. and , minimize the loss function L through the back-propagation algorithm.

[0144] In this embodiment, the management features include feature errors and error changes, and the correction management mode also includes:

[0145] The characteristic error and error change are normalized to the fuzzy domain through the quantization factor and input into the fuzzy controller with the correction factor. The correction factor is used to adjust the weight ratio of the characteristic error and the error change, and generate the control quantity through the fuzzy rule table.

[0146] In this embodiment, the PI management mode includes: calculating the control amount by using the proportional gain and the integral gain.

[0147] It is understandable that the PI management mode is for steady-state operating conditions that do not exceed the threshold value. In some embodiments, the proportional gain and integral gain in the prior art can be used for control.

[0148] More preferably, in some embodiments, the trapezoidal integration method is used to update the control amount every 1 second.

[0149] In this embodiment, the management characteristics are decomposed into characteristic error and error change. Both are standardized into fuzzy domains using quantization factors and input into a fuzzy controller with correction factors for intelligent decision-making. In specific implementation, the system uses a dynamic correction factor α to adaptively adjust the weighted ratio between the error and the error change rate. The control output is generated through calculation, with the control output value dynamically adjusted based on the stage of grinding wheel wear. A fuzzy rule table, constructed based on expert experience, contains 49 "IF-THEN" rules, such as "IF e is positive and Δe is neutral, THEN the output control variable is positive," to achieve nonlinear and precise control of grinding wheel speed and grinding depth.

[0150] In some embodiments, the characteristic error is the deviation between the current state and the target value; the error change is the trend of the deviation changing over time.

[0151] In some embodiments, the dynamic adjustment may be α=0.7 in the initial stage to focus on steady-state accuracy, and α=0.3 in the later stage to enhance dynamic response.

[0152] In this embodiment, by introducing an optimized proportional-integral control algorithm, technical problems such as insufficient steady-state control accuracy and parameter adjustment lag in traditional grinding processing are systematically solved, while achieving significant benefits such as reduced energy consumption and improved process stability.

[0153] Specifically, when the management characteristic is lower than the preset threshold, the system automatically switches to PI control mode. The proportional gain and integral gain are experimentally calibrated and dynamically optimized, and are responsible for quickly responding to the current error and eliminating the accumulation of historical errors, respectively.

[0154] During the specific implementation process, the system uses a sampling period of 1 second and a trapezoidal integration method to process the discretized error signal to ensure that the integral term will not be saturated. At the same time, the multi-scale management model is used to monitor key characteristics such as the main components of grinding wheel wear and vibration energy in real time. When these characteristic values deviate from the process target values, the PI controller can output accurate grinding wheel speed or grinding depth adjustment at a millisecond response speed.

[0155] Compared with traditional switch control, this solution compresses the grinding size tolerance from ±0.02mm to ±0.005mm, improving the processing accuracy by more than 75%; compared with the fixed step compensation method, the motor energy consumption can be reduced by 15% through fine-tuning of the proportional term.

[0156] In addition, the PI control mode forms a complementary mechanism with the fuzzy control, achieving smooth switching when the management feature approaches the threshold, thus avoiding process fluctuations caused by sudden changes in the control mode. In addition, the system can automatically optimize the proportional / integral parameters based on historical processing data. For example, when processing high-hardness materials, it automatically increases the integral gain to suppress thermal deformation errors. This adaptive feature improves the system's adaptability to different workpiece materials and grinding wheel wear conditions by more than 40%. Practical applications have shown that in the automotive crankshaft grinding scenario, the adoption of this technical solution has reduced the product defect rate from 1.5% to 0.3%, and extended the service life of the grinding wheel by 15%. At the same time, due to the improvement in control accuracy, the processing time of each batch of workpieces has been shortened by 8%, with significant overall economic benefits.

[0157] Example 3:

[0158] The method further includes: setting a management feature when the workpiece is not in contact with the grinding wheel as an abnormal feature, and issuing an alarm when the abnormal feature is triggered.

[0159] In some embodiments, a complete abnormal feature recognition model is established by real-time monitoring of vibration signals, grinding forces, and acoustic emission signals, combined with current characteristics under operating conditions.

[0160] When any of the following abnormal conditions is detected, the graded alarm mechanism will be triggered immediately:

[0161] 1. The vibration signal amplitude is lower than the set threshold;

[0162] 2. The grinding force suddenly drops below the safe value;

[0163] 3. The characteristic frequency of the acoustic emission signal disappears;

[0164] 4. Visually detect the gap between the workpiece and the grinding wheel.

[0165] The system in this embodiment uses a fuzzy logic algorithm to perform weighted fusion on multi-source sensor data. It reduces false alarm rates by setting a dynamic confidence threshold (e.g., 0.95). Combined with an LSTM neural network, it predicts anomaly trends, achieving early warning capabilities of 50-100 milliseconds. In practical applications, the system in this embodiment has shortened abnormality response time from 5-10 seconds for traditional manual detection to less than 100 milliseconds, reducing equipment collisions by over 90% and abnormal grinding wheel wear by 35%.

[0166] In some embodiments, the vibration signal is monitored using a three-axis acceleration sensor with a sampling frequency of 10 kHz;

[0167] In some embodiments, the grinding force is measured using a strain gauge force sensor with an accuracy of ±0.5N;

[0168] In some embodiments, the frequency range of the acoustic emission signal is 50-400 kHz.

[0169] Example 4:

[0170] A workpiece grinding management system, comprising:

[0171] Information acquisition module, used to obtain workpiece information and grinding process information;

[0172] A threshold setting module is used to establish a multi-scale management model based on workpiece information and grinding process information, and to set a management threshold within the multi-scale management model;

[0173] A processing parameter acquisition module, which acquires multiple processing parameters in the grinding process information at set time intervals based on the multi-scale management model; wherein the processing parameters include grinding wheel speed, workpiece speed and grinding depth;

[0174] The data processing module is used to use the processing parameters as the initial features, fuse the initial features to obtain the management features, and enter the correction management mode if the management features are greater than or equal to the management threshold; if the management features are less than the management threshold, enter the PI management mode; wherein, the correction management mode includes: inputting the management features into the PLC, adjusting the weight ratio of the parameters within the management features, generating the management output through the fuzzy rule table, and converting the management output into the control quantity of the PLC after fuzzification through dequantization.

[0175] In this embodiment, through the innovative integration of modular architecture and advanced communication technology, three major technical difficulties existing in traditional grinding equipment are systematically solved: data acquisition fragmentation, control response lag, and insufficient system compatibility.

[0176] The system in this embodiment consists of a high-precision data acquisition module, an intelligent management module, and an execution control module. These modules are seamlessly integrated through Industrial Internet of Things (IIoT) technology. At the data acquisition layer, the system deploys a multimodal sensor network, including laser displacement sensors, triaxial vibration sensors, and infrared thermal imagers. A time synchronization protocol ensures consistent data acquisition timing, keeping signal acquisition latency to under 0.2ms.

[0177] In a certain aircraft engine turbine disk grinding application, this system stably controls the processing accuracy within ±2μm, reducing the product defect rate from 1.2% to 0.15%. At the same time, the energy efficiency optimization module reduces energy consumption by 18%, and the multi-source data fusion algorithm eliminates information barriers between sensors.

[0178] Example 5:

[0179] Based on the same inventive concept as the above embodiment, this embodiment provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program.

[0180] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A workpiece grinding management method, characterized in that: The method comprises the following steps: Obtain workpiece information and grinding process information; Establish a multi-scale management model based on workpiece information and grinding process information, and set management thresholds within the multi-scale management model; Acquiring multiple processing parameters in the grinding process information at set time intervals based on the multi-scale management model; wherein the processing parameters include grinding wheel speed, workpiece rotation speed, and grinding depth; The processing parameters are used as initial features, and the initial features are fused to obtain management features. If the management features are greater than or equal to the management threshold, the system enters the correction management mode. If the management features are less than the management threshold, the system enters the PI management mode. The correction management mode includes: inputting the management features into the PLC, adjusting the weight ratio of the parameters within the management features, generating management outputs through a fuzzy rule table, and converting the management outputs into PLC control quantities after fuzzification and dequantization. Among them, the multi-scale management model includes the target layer, data layer, fusion layer, management layer and interaction layer; The multi-scale management model is established based on the workpiece information and the grinding process information, and the management threshold is set within the multi-scale management model, including: Enter workpiece information in the target layer; Receive the grinding process information collected by the acquisition module and construct it into a data layer together with the workpiece information; In the fusion layer, the information in the data layer is preprocessed, feature extracted, normalized, and kernel principal component analysis is performed to obtain management features. The kernel principal component uses the Gaussian kernel function to map the standardized feature matrix X into a high-dimensional space. Setting management thresholds within the management layer through the interaction layer; Management features include feature errors and error changes, and the correction management model also includes: The characteristic error and error change are normalized to the fuzzy domain through the quantization factor and input into the fuzzy controller with the correction factor. The correction factor is used to adjust the weight ratio of the characteristic error and the error change, and generate the control quantity through the fuzzy rule table.

2. A workpiece grinding management method according to claim 1, characterized in that: The process of kernel principal component analysis feature fusion includes: Map the normalized feature matrix to a high-dimensional space and calculate the kernel matrix; Centralize the kernel matrix; Solve for the eigenvalues and eigenvectors of the kernel matrix; Arrange in descending order of eigenvalues, select the principal components whose cumulative contribution rate exceeds the threshold, and obtain the principal component matrix after dimensionality reduction as the management feature.

3. A workpiece grinding management method according to claim 1, characterized in that: The multi-scale management model is connected to a neural network model for communication. The neural network model is used to divide the training set and the test set through historical processing data, and is used to dynamically update the management features.

4. A workpiece grinding management method according to claim 3, characterized in that: The hidden layer of the neural network model adopts the hyperbolic tangent function, and the output layer of the neural network model adopts the linear function.

5. A workpiece grinding management method according to claim 1, characterized in that: PI management models include: The control amount is calculated using the proportional gain and integral gain.

6. A workpiece grinding management method according to claim 1, characterized in that: The method further includes: setting a management feature when the workpiece is not in contact with the grinding wheel as an abnormal feature, and issuing an alarm when the abnormal feature is triggered.

7. A workpiece grinding management system, characterized in that: The system comprises: Information acquisition module, used to obtain workpiece information and grinding process information; A threshold setting module is used to establish a multi-scale management model based on workpiece information and grinding process information, and to set a management threshold within the multi-scale management model; A processing parameter acquisition module, which acquires multiple processing parameters in the grinding process information at set time intervals based on the multi-scale management model; wherein the processing parameters include grinding wheel speed, workpiece speed and grinding depth; The data processing module is used to use the processing parameters as the initial features, fuse the initial features to obtain the management features, and enter the correction management mode if the management features are greater than or equal to the management threshold; if the management features are less than the management threshold, enter the PI management mode; wherein, the correction management mode includes: inputting the management features into the PLC, adjusting the weight ratio of the parameters within the management features, generating the management output through the fuzzy rule table, and converting the management output into the control quantity of the PLC after fuzzification through dequantization.

8. A computer device, characterized in that: The computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method according to any one of claims 1 to 6.

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