A wear monitoring method and system for bimetallic milling cutters considering material property differences

Abstract

The present invention discloses a bimetallic milling cutter wear monitoring method and system that considers differences in material properties. The method relates to the technical field of bimetallic material processing. The method comprises: real-time acquisition of the milling force and vibration signals of the bimetallic end mill during the milling process; inputting the acquired milling force and vibration signals into an unsupervised material recognition model, and after feature extraction and feature dimensionality reduction, clustering and outputting the metal material category of the current signal; inputting the acquired milling force, vibration signal, and corresponding metal material category into a physics-guided neural network model based on wear mechanism, and outputting the flank wear value of the bimetallic end mill. The present invention considers the differences between the two metal materials and introduces wear mechanism guidance. Through wear physics-data fusion monitoring, a monitoring model with higher accuracy and better robustness is obtained, effectively improving the accuracy of tool flank wear value monitoring in bimetallic end milling scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of bimetallic material processing, and in particular to a bimetallic milling cutter wear monitoring method and system taking into account differences in material properties. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Bimetallic parts often outperform parts made from their two components separately, making them crucial in many engineering fields, including aircraft, automobiles, power generation, and construction equipment. To meet design and performance standards, bimetallic blanks require extremely precise cutting processes to produce bimetallic parts. Cutting tools are crucial for high-speed cutting. High-speed cutting increases cutting temperatures dramatically, subjecting the tool to increasing shock and vibration, accelerating tool wear and leading to reduced surface quality, fractures, and chipping. Existing research indicates that tool monitoring technology can increase machining efficiency by 10% to 50% and reduce production costs by 10% to 40%, making accurate tool wear monitoring crucial.

[0004] In the actual bimetal cutting tool wear scenario, existing wear monitoring research still faces many difficulties, such as: First, in the wear status monitoring modeling process of bimetal parts cutting tools, the differences between the two metal materials are not currently considered, and they are usually directly processed as a single material, resulting in large errors in the final wear monitoring; Second, although the wear monitoring method based on pure physical models is less affected by abnormal points in the training set, its monitoring error is large. Compared with pure physical models, pure data-driven methods have better accuracy, but this method is easily affected by abnormal points, which in turn affects the final monitoring accuracy; In addition, for data-driven methods, data sets are scarce. Existing data sets only focus on single metal cutting, and lack attention to bimetal cutting tool wear. There is currently no public data set suitable for bimetal end milling tool wear.

[0005] In recent years, with the continuous development of computer computing power, neural network-based end milling tool wear monitoring methods have been widely used in regression tasks. Although this method has achieved considerable success in the field of general tool wear monitoring, existing neural network-based bimetal milling wear monitoring methods are mostly simple transfer products of conventional monometal tool wear monitoring, and have not proposed reasonable solutions to the various difficulties mentioned above. Summary of the Invention

[0006] In order to address the deficiencies of the above-mentioned prior art, the present invention provides a bimetallic milling cutter wear monitoring method and system that takes into account the differences in material properties. The method takes into account the differences between the two metal materials and introduces wear mechanism guidance. A monitoring model with higher accuracy and better robustness is obtained through wear physics-data fusion monitoring. The method solves the problem that the existing bimetallic end milling cutter wear monitoring method does not consider the impact of different material properties on wear monitoring and only performs wear monitoring through pure data-driven methods, which results in a large error in the final wear monitoring. The method effectively improves the accuracy of tool back face wear value monitoring in bimetallic end milling scenarios.

[0007] In a first aspect, the present invention provides a method for monitoring wear of a bimetallic milling cutter taking into account differences in material properties.

[0008] A wear monitoring method for a bimetallic milling cutter taking into account differences in material properties comprises:

[0009] Real-time acquisition of milling force and vibration signals of bimetal end milling cutters during milling process;

[0010] The collected milling force and vibration signals are input into the unsupervised material recognition model. After feature extraction and feature dimensionality reduction, the metal material category of the current signal is output through clustering.

[0011] The collected milling force, vibration signal and corresponding metal material category are input into the physics-guided neural network model based on wear mechanism, and the flank wear value of the bi-metal end milling tool is output. During the training process of the physics-guided neural network model, the loss between the actual measured wear value and the predicted wear value is used as the first loss, and the loss between the predicted wear rate based on the physical formula of wear rate and the actual wear rate is used as the second loss. The first and second losses are combined as the loss function.

[0012] In a further technical solution, the training process of the unsupervised material recognition model and the physical guided neural network model is as follows:

[0013] Collecting milling force and vibration signals of a bimetallic end mill during milling under several different milling parameters; the different milling parameters include different spindle speeds, feeds per tooth, and cutting depths;

[0014] The collected milling force and vibration signals are segmented into effective signals, and each segment is labeled with the metal material category label and the end mill tool flank wear value label to construct a training set;

[0015] Constructing an unsupervised material recognition model and a physics-guided neural network model; wherein the unsupervised material recognition model includes a feature extraction module, a feature dimensionality reduction module, and a clustering module, and the physics-guided neural network model includes a feature extraction module, a feature fusion module, and a monitoring module;

[0016] The two constructed models were trained separately using the training set to obtain the trained unsupervised material recognition model and physics-guided neural network model.

[0017] A further technical solution is to use the training set to train the physical guided neural network model as follows:

[0018] The physical formula for the wear rate of the end milling tool wear degradation process is selected, and the undetermined coefficients in the physical formula are fitted based on the actual measured wear rate.

[0019] The loss between the actual measured wear value and the predicted wear value is taken as the first loss, and the loss between the predicted wear rate based on the physical formula of the wear rate and the actual wear rate is taken as the second loss. The first and second losses are combined to construct a loss function that constrains the model output.

[0020] Based on the loss function constructed with physical guidance, the model is iteratively trained using the training set until the maximum number of iterations is reached and the model training is completed.

[0021] In a second aspect, the present invention provides a bimetallic milling cutter wear monitoring system that takes into account differences in material properties.

[0022] A bimetallic milling cutter wear monitoring system taking into account material property differences comprises:

[0023] Signal acquisition module, used to collect the milling force and vibration signals of the bimetal end milling tool during the milling process in real time;

[0024] The bimetal milling cutter wear material identification module is used to input the collected milling force and vibration signals into the unsupervised material identification model. After feature extraction and feature dimensionality reduction, the metal material category of the current signal is output through clustering.

[0025] The bimetal milling cutter wear detection module is used to input the collected milling force, vibration signal and corresponding metal material category into the physical guidance neural network model based on the wear mechanism, and output the flank wear value of the bimetal end milling cutter; wherein, during the training process of the physical guidance neural network model, the loss between the actual measured wear value and the predicted wear value is used as the first loss, and the loss between the predicted wear rate based on the physical formula of the wear rate and the actual wear rate is used as the second loss, and the first and second losses are combined as the loss function.

[0026] In a third aspect, the present invention also provides an electronic device comprising: a memory for storing executable instructions; and a processor for implementing the above-mentioned bimetallic milling cutter wear monitoring method considering material property differences when executing the executable instructions stored in the memory.

[0027] In a fourth aspect, the present invention further provides a computer-readable storage medium storing executable instructions for causing a processor to execute the executable instructions to implement the above-mentioned bimetallic milling cutter wear monitoring method considering material property differences.

[0028] In a fifth aspect, the present invention also provides a computer program product, which includes executable instructions, and the executable instructions are stored in a computer-readable storage medium; wherein, when the processor of the electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, the above-mentioned bimetallic milling cutter wear monitoring method considering the difference in material properties is implemented.

[0029] One or more of the above technical solutions have the following beneficial effects:

[0030] The present invention provides a bimetallic milling cutter wear monitoring method and system that considers the differences in material properties. The method considers the differences between two metal materials and introduces wear mechanism guidance. A monitoring model with higher accuracy and better robustness is obtained through wear physics-data fusion monitoring. In the present invention, the milling force and vibration signals of the bimetallic end milling cutter are collected to construct a benchmark data set for bimetallic end milling cutter wear monitoring. At the same time, an unsupervised material recognition model and a physical guidance network model suitable for bimetallic end milling cutter wear monitoring are constructed. The benchmark data set is then used to train the unsupervised material recognition model and the physical guidance network model to obtain trained weights and biases. Finally, the milling force and vibration signals of the bimetallic end milling cutter collected in real time are sequentially input into the above-mentioned material recognition model and the physical guidance network model to output the monitored real-time milling material category and accurate flank wear value. The present invention solves the problem that the existing bimetallic end milling cutter wear monitoring method does not consider the influence of different material properties on wear monitoring and only performs wear monitoring in a pure data-driven manner, which results in a large error in the final wear monitoring. It effectively improves the accuracy of tool flank wear value monitoring in the bimetallic end milling scenario.

[0031] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0033] Figure 1 A schematic flow chart of a bimetallic milling cutter wear monitoring method considering material property differences proposed in an embodiment of the present invention;

[0034] Figure 2Schematic diagram of dividing the effective interval of the signal in an embodiment of the present invention;

[0035] Figure 3 Schematic diagram of extracting multi-domain features from segmented multi-source valid signals in an embodiment of the present invention;

[0036] Figure 4 Schematic diagram of features after t-SNE dimensionality reduction in an embodiment of the present invention;

[0037] Figure 5 Schematic diagram of Kmeans clustering and confusion matrix in an embodiment of the present invention;

[0038] Figure 6 This is a schematic diagram of Kmeans clustering when there are few samples in an embodiment of the present invention;

[0039] Figure 7 A schematic diagram of a characteristic separation of bimetallic materials in an embodiment of the present invention;

[0040] Figure 8 The wear-related thermodynamic diagram of the bimetallic feature before and after separation in the embodiment of the present invention;

[0041] Figure 9 The wear-related thermal map before and after weighted fusion of bimetallic features in an embodiment of the present invention is shown;

[0042] Figure 10 Schematic diagram of the weighted fusion process of cast iron (CI) No. 4 domain features in an embodiment of the present invention;

[0043] Figure 11 This is a schematic diagram of fitting the physical formula of wear rate in an embodiment of the present invention;

[0044] Figure 12 This is a schematic diagram of the wear monitoring effect with and without differentiating material differences according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] It should be noted that the following detailed descriptions are exemplary only and are intended to describe specific embodiments and provide further explanation of the present invention, and are not intended to limit the exemplary embodiments according to the present invention. Unless otherwise indicated, all technical and scientific terms used herein have the same meanings as those commonly understood by those of ordinary skill in the art to which the present invention belongs. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0046] Example 1

[0047] This embodiment provides a bimetallic milling cutter wear monitoring method considering the difference in material properties, such as Figure 1As shown, the method specifically includes the following steps:

[0048] Step S101: collecting the milling force and vibration signals of the bimetallic end milling cutter during the milling process in real time.

[0049] In this embodiment, the milling force and milling vibration signals of the end milling tool during the milling of the bimetallic material are collected in real time by sensors.

[0050] Step S102: input the collected milling force and vibration signals into an unsupervised material recognition model, and after feature extraction and feature dimensionality reduction, output the metal material category of the current signal through clustering.

[0051] Step S103: Input the collected milling force, vibration signal, and corresponding metal material type into a physics-guided neural network model based on wear mechanisms to output the flank wear value of the bimetal end mill. During training, the physics-guided neural network model uses the loss between the actual measured wear value and the predicted wear value as a first loss, and the loss between the predicted wear rate based on the wear rate physics formula and the actual wear rate as a second loss. These first and second losses are combined to form a loss function.

[0052] In this embodiment, an unsupervised material recognition model and a physics-guided neural network model based on wear mechanism are pre-built and trained, and then wear detection is performed through the above steps S102 and S103. That is, the unsupervised material recognition model is first used to identify the metal material type corresponding to the signal. Then, based on the identified metal material type and the collected milling force and vibration signals, the physics-guided neural network model is used to output more accurate wear monitoring results. Specifically, the training process of the above model is as follows:

[0053] First, the milling force and vibration signals of the bimetal end mill during the milling process are collected under several different milling parameters; among them, different milling parameters include different spindle speeds, feeds per tooth and cutting depths, that is, the collected data covers a variety of milling force and vibration signals under different spindle speeds, feeds per tooth and cutting depths.

[0054] Secondly, the collected milling force and vibration signals are segmented and labeled with the metal material category label and the end mill tool flank wear value label to construct a training set.

[0055] Afterwards, an unsupervised material recognition model and a physics-guided neural network model were constructed.

[0056] Finally, the two constructed models were trained separately using the training set to obtain the trained unsupervised material recognition model and physics-guided neural network model.

[0057] Furthermore, the above unsupervised material recognition model includes a feature extraction module, a feature dimension reduction module and a clustering module, wherein:

[0058] (1) The feature extraction module is used to identify the effective interval of the input milling force and vibration signals and divide them into multiple effective segments. The time domain, frequency domain and time-frequency domain features of each effective segment in the signal are extracted to generate a multi-domain feature matrix.

[0059] In the feature extraction module, such as Figure 2 As shown in Figure 1, the threshold method is used to identify and extract the effective interval of the input original signal. The method is as follows: a threshold is preset based on experience, and the tool entry point is determined based on the preset threshold. That is, when a value reaching this threshold is detected in the signal, the tool entry point can be determined. Furthermore, considering that the length of each tool signal used in the experiment is the same, the signal of a fixed length after the entry point is taken as the effective cutting interval (i.e., the effective interval). Figure 3 As shown, the effective interval in the original signal is extracted, and the effective interval of the original signal is divided into multiple effective segments, that is, the segments are divided into segments with a fixed length of time period, and the time domain, frequency domain and time-frequency domain features of each effective segment are extracted to generate a multi-domain feature matrix.

[0060] Preferably, in this embodiment, for each channel of each signal, 13 time domain features, 11 frequency domain features, and 8 time-frequency domain features obtained by wavelet decomposition are extracted, for a total of 32 domain features. Furthermore, a 32×6=192-dimensional feature matrix is ​​extracted for a total of 6 channels of the two signals, namely the milling force and vibration signals. It should be noted that the NASA data set only has two single-channel signals. The extracted time domain features include maximum value, minimum value, average value, peak value, variance, standard deviation, kurtosis, skewness, root mean square (RMS), form factor, crest factor, impulse factor, and margin factor; the extracted frequency domain features include frequency domain amplitude mean value, frequency domain amplitude skewness, frequency domain amplitude kurtosis, centroid frequency, mean square frequency, frequency variance, RMS frequency, frequency amplitude variance, frequency standard deviation, frequency band energy, and relative power spectrum entropy; the extracted time-frequency domain features include the mean value of the 8 components obtained after the signal is decomposed by 3 layers of wavelet packets.

[0061] (2) The feature dimensionality reduction module is used to perform unsupervised dimensionality reduction on the extracted multi-domain feature matrix to generate a low-dimensional feature matrix.

[0062] In the feature dimensionality reduction module, an unsupervised dimensionality reduction method based on t-SNE is used to reduce the dimensionality of the extracted multi-domain high-dimensional feature matrix. In this embodiment, the dimensionality is reduced to 2 dimensions through t-SNE unsupervised dimensionality reduction to obtain a 2-dimensional feature matrix. Figure 4 As shown in the figure, each point represents the 2D feature of a valid segment, and each point has a corresponding metal material category label, so as to obtain the 2D features of all valid segments in the input signal after dimensionality reduction.

[0063] (3) The clustering module is used to identify and output the metal material category based on the low-dimensional feature matrix after dimensionality reduction.

[0064] In the clustering module, based on the low-dimensional feature matrix of each valid segment, the Kmeans clustering method is used to automatically identify the metal material category of each valid segment in the input original signal, such as Figure 5 As shown in FIG, after cluster identification, the categories are divided into two categories: metal 1 and metal 2. Preferably, according to the category division results of cluster identification, a confusion matrix can be constructed, which is a matrix that marks the number of correct and incorrect classification results for each category. Figure 5 As can be seen from the confusion matrix shown, the clustering method adopted in this embodiment has a high category recognition accuracy.

[0065] Specifically, the 2D feature matrix after dimensionality reduction is input into the Kmeans classifier, and the clustering is specified as 2 categories for training. The clusterer is used to obtain the material category of each fragment, where the clustering process is:

[0066] (3.1) Initialization: Select K initial cluster centers; in this embodiment, K=2;

[0067] (3.2) Assignment, assign each data point to the nearest cluster center: For each data point in the data set, calculate its distance to each cluster center and assign it to the cluster center with the nearest distance;

[0068] (3.3) Update, recalculate the center of each cluster;

[0069] (3.4) Iteration: Repeat steps (3.2) and (3.3) until the maximum number of iterations is reached and output the final clustering result.

[0070] It should be noted that the milling force and vibration data, as well as the material category and wear label in this embodiment, are obtained by segmenting and splicing data from the NASA milling dataset. Metal 1 is cast iron (CI) and Metal 2 is steel (Steel). In the actual milling tool wear monitoring process, in order to ensure real-time performance, it is impossible to always cut off the segments exactly at the material boundary. Therefore, there is always a segment that contains both part of the Metal 1 signal and part of the Metal 2 signal, which is called a transition segment. Figure 6 As shown in , when the amount of data is small, clustering can easily divide the transition segment, but for most features, the feature value of the transition segment is between the segments of the other two classes. In other words, Figure 5As shown, when the sample size is large, clustering cannot easily separate the transition segments, and since the number of transition segments is much smaller than that of the other two categories, the above method adopted in this embodiment automatically classifies the transition segments into one of the two categories of metal 1 and metal 2 by the clustering model, and will not cause a large error in the recognition result.

[0071] Furthermore, the physical guided neural network model includes a feature extraction module, a feature fusion module and a monitoring module, wherein:

[0072] (4) Feature extraction module: This module is the same as the feature extraction module in the unsupervised material recognition model and will not be described in detail here.

[0073] (5) The feature fusion module is used to split the multi-domain feature matrix of the extracted input signal according to the metal material category of each valid segment in the identified input signal, and perform weighted fusion on the two split feature matrices to obtain a low-dimensional fusion feature matrix.

[0074] First, if Figure 7 As shown, according to the clustering module, the metal material category label of each valid segment in the signal is identified and obtained. At this time, it can be determined which metal material the end mill is cutting at a certain time, and then the bimetallic features can be separated or split into feature 1 when cutting metal 1 and feature 2 when cutting metal 2 according to the category label, and then feature matrix 1 and feature matrix 2 can be obtained. Specifically, the clusterer is used to obtain the metal material category of each segment, so as to distinguish the values ​​belonging to different materials in each feature of the input signal, and obtain feature matrix 1 for cutting metal 1 and feature matrix 2 for cutting metal 2, so that they can be input into the wear value monitoring model for monitoring. Figure 8 The wear-related heat map shown in the figure shows that each matrix represents a feature, and the depth of the feature color is determined by the Pearson correlation coefficient between the feature and wear. For most of the correlation coefficients between features and wear, the correlation coefficient of the two separated feature matrices is larger, that is, the latest feature matrix is ​​more correlated with the milling cutter wear value.

[0075] Secondly, for the two characteristic matrices obtained above, such as Figure 9 As shown in the figure, weighted fusion is performed on each channel and domain to obtain two new low-dimensional feature matrices. The weight of each feature is calculated based on the Pearson correlation coefficient between each feature and the wear value in each channel and domain, as well as the weighting factor and the inverse Tanh weighting function. All features are weighted and fused into fused features in each domain, thus obtaining a low-dimensional fused feature matrix.

[0076] Specifically, such as Figure 9As shown, the features are weighted and fused on each channel and domain. The signal of each channel extracts the features of 3 domains, and weighted fusion is performed in each domain to obtain 3 fused features. Since 6 channels are set in this embodiment, 6×3=18 fused features are finally obtained. In this way, two 192-dimensional feature matrices are fused to obtain two 18-dimensional feature matrices. It should be noted that the NASA data set is only 6-dimensional. The above feature fusion process is as follows Figure 10 As shown, the size of the weight is determined according to the correlation coefficient x between each feature and wear in the same domain, and the inverse tanh function is used to determine the size relationship of each weight and to convert negatively correlated features into positively correlated features. The weighting factor a is an adjustable positive odd number. The optimal point is found through iteration, that is, the weighting factor a is randomly selected. In this embodiment, a=1 is taken, and it is iteratively added starting from a=1 until the correlation coefficient of the fusion feature is maximized, and the value of the weighting factor a is determined; on this basis, the weight is determined according to the correlation coefficient between each feature and wear in the current domain, combined with the inverse tanh function and the weighting factor, so as to perform weighted fusion and obtain the fusion feature corresponding to the domain. Compared with the attention mechanism, which is a weighting method, the above-mentioned method adopted in this embodiment can assign weights to each feature. Compared with the filtering method, the method proposed in this embodiment can retain more effective components.

[0077] (6) The monitoring module is used to output the monitored tool wear value based on the fused low-dimensional fusion feature matrix.

[0078] Furthermore, in this embodiment, based on the consideration of the differences between the two metal materials, wear mechanism guidance is also introduced, and a monitoring model with higher accuracy and better robustness is obtained through wear physics-data fusion monitoring. Figure 11 As shown in Figure 2, the training process of the physical guided neural network model is:

[0079] Firstly, the physical formula of the wear rate of the end milling tool wear degradation process is selected, and the unknown coefficients in the physical formula are fitted according to the actual measured wear rate.

[0080] In this embodiment, the physical formula of wear rate is first selected as:

[0081]

[0082] In the above formula, t represents time, a, b, and c are variable parameters, i.e., unknown coefficients; VB represents the wear value.

[0083] The coefficients in the aforementioned physical formula are determined by fitting the wear rate obtained based on actual wear measurements. Specifically, the measured wear values ​​are interpolated using cubic spline interpolation, differentiated to obtain the wear rate, and then the coefficients in the selected wear rate formula are determined using least squares fitting. The wear rate formula selected in this embodiment accurately describes the three stages of wear, i.e., the wear rate follows a pattern of increasing, decreasing, and then increasing again, thus ensuring the accuracy of the final wear monitoring.

[0084] Secondly, the loss between the actual measured wear value and the predicted wear value is taken as the first loss, and the loss between the predicted wear rate based on the physical formula of wear rate and the actual wear rate is taken as the second loss. The first and second losses are combined to construct the loss function of the constraint model output.

[0085] Specifically, the loss between the predicted wear rate y1 and the actual wear rate y (i.e., |y1-y|) obtained according to the above wear rate physical formula is used as a physical loss term and added to the loss function of the ANN network (the loss function is the loss related to the wear amount) to obtain a PINN model guided by the wear mechanism.

[0086] Finally, based on the loss function constructed with physical guidance, the model is iteratively trained using the training set until the maximum number of iterations is reached, completing the model training.

[0087] like Figure 12 As shown in the figure, the wear monitoring effect of distinguishing material differences proposed in this embodiment is compared with that of not distinguishing material differences. The comparison of the monitoring results and errors of the wear values ​​further proves the great improvement in accuracy of the method proposed in this embodiment and the accuracy of PINN. At the same time, due to the use of a monitoring method that combines physics and data, PINN has better robustness.

[0088] Example 2

[0089] This embodiment provides a bimetallic milling cutter wear monitoring system that takes into account differences in material properties, specifically including:

[0090] Signal acquisition module, used to collect the milling force and vibration signals of the bimetal end milling tool during the milling process in real time;

[0091] The bimetal milling cutter wear material identification module is used to input the collected milling force and vibration signals into the unsupervised material identification model. After feature extraction and feature dimensionality reduction, the metal material category of the current signal is output through clustering.

[0092] The bimetal milling cutter wear detection module is used to input the collected milling force, vibration signal and corresponding metal material category into the physical guidance neural network model based on the wear mechanism, and output the flank wear value of the bimetal end milling cutter; wherein, during the training process of the physical guidance neural network model, the loss between the actual measured wear value and the predicted wear value is used as the first loss, and the loss between the predicted wear rate based on the physical formula of the wear rate and the actual wear rate is used as the second loss, and the first and second losses are combined as the loss function.

[0093] Example 3

[0094] This embodiment provides an electronic device, including: a memory for storing executable instructions; and a processor for implementing the above method provided in this embodiment when executing the executable instructions stored in the memory.

[0095] Example 4

[0096] This embodiment further provides a computer-readable storage medium storing executable instructions. When the executable instructions are executed by a processor, the processor will be caused to execute the above method provided in this embodiment.

[0097] Example 5

[0098] This embodiment provides a computer program product including executable instructions, which are computer instructions stored in a computer-readable storage medium. When a processor of an electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, the electronic device performs the method provided in this embodiment.

[0099] The steps involved in the above embodiments 2 to 5 correspond to those in embodiment 1. For detailed implementation, please refer to the relevant description of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media that includes one or more instruction sets; it should also be understood to include any medium that can store, encode, or carry an instruction set for execution by a processor and cause the processor to perform any method of the present invention.

[0100] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computer device. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0101] The above description is only a preferred embodiment of the present invention. Although the specific implementation of the present invention is described in conjunction with the accompanying drawings, it does not limit the scope of protection of the present invention. Those skilled in the art should understand that on the basis of the technical solution of the present invention, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present invention.

Claims

1. A method for monitoring wear of a bimetallic milling cutter taking into account differences in material properties, characterized in that: include: The milling force and vibration signals of the bimetal end milling tool during the milling process are collected in real time by using a sensor to collect the milling force and vibration signals of the bimetal end milling tool during the milling process; The collected milling force and vibration signals are input into the unsupervised material recognition model. After feature extraction and feature dimensionality reduction, the metal material category of the current signal is output through clustering. The collected milling force, vibration signal and corresponding metal material category are input into the physics-guided neural network model based on wear mechanism, and the flank wear value of the bi-metal end milling tool is output. During the training process of the physics-guided neural network model, the loss between the actual measured wear value and the predicted wear value is used as the first loss, and the loss between the predicted wear rate based on the physical formula of wear rate and the actual wear rate is used as the second loss. The first and second losses are combined as the loss function.

2. A bimetallic milling cutter wear monitoring method considering material property differences according to claim 1, characterized in that: The training process of the unsupervised material recognition model and the physical guided neural network model is as follows: Collecting milling force and vibration signals of a bimetallic end mill during milling under several different milling parameters; the different milling parameters include different spindle speeds, feeds per tooth, and cutting depths; The collected milling force and vibration signals are segmented into effective signals, and each segment is labeled with the metal material category label and the end mill tool flank wear value label to construct a training set; Constructing an unsupervised material recognition model and a physics-guided neural network model; wherein the unsupervised material recognition model includes a feature extraction module, a feature dimensionality reduction module, and a clustering module, and the physics-guided neural network model includes a feature extraction module, a feature fusion module, and a monitoring module; The two constructed models were trained separately using the training set to obtain the trained unsupervised material recognition model and physics-guided neural network model.

3. A bimetallic milling cutter wear monitoring method considering material property differences according to claim 2, characterized in that: The feature extraction module is used to identify the valid interval of the input milling force and vibration signal and divide it into multiple valid segments, extract the time domain, frequency domain and time-frequency domain features of each valid segment in the signal, and generate a multi-domain feature matrix; wherein, in the feature extraction module, a threshold method is used to identify and extract the valid interval of the input original signal, the valid interval of the original signal is divided into multiple valid segments, and the time domain, frequency domain and time-frequency domain features of each valid segment are extracted to generate a multi-domain feature matrix; The feature dimensionality reduction module is used to perform unsupervised dimensionality reduction on the extracted multi-domain feature matrix to generate a low-dimensional feature matrix; wherein, in the feature dimensionality reduction module, the extracted multi-domain high-dimensional feature matrix is ​​subjected to t-SNE unsupervised dimensionality reduction to obtain a low-dimensional feature matrix; The clustering module is used to identify and output the metal material category based on the low-dimensional feature matrix after dimensionality reduction; wherein, in the clustering module, based on the low-dimensional feature matrix of each valid segment, the Kmeans clustering method is used to automatically identify the metal material category of each valid segment in the input original signal.

4. A method for monitoring wear of a bimetallic milling cutter taking into account material property differences as claimed in claim 2, characterized in that: The feature fusion module is used to split the multi-domain feature matrix of the extracted input signal according to the metal material category of each valid segment in the identified input signal, and perform weighted fusion on the two split feature matrices to obtain a low-dimensional fused feature matrix; The monitoring module is used to output the monitored tool wear value according to the fused low-dimensional fusion feature matrix.

5. A method for monitoring wear of a bimetallic milling cutter taking into account differences in material properties as claimed in claim 4, characterized in that: In the feature fusion module, the signal features are first split into feature matrix 1 for cutting metal 1 and feature matrix 2 for cutting metal 2 according to the metal material category with each small fragment in the identified signal; then, the weight of each feature is calculated based on the Pearson correlation coefficient of each feature and the wear value in each channel and domain, as well as the weighting factor and inverse Tanh weighting function. All features in each domain are weighted and fused into fused features, thus obtaining a low-dimensional fused feature matrix.

6. A method for monitoring wear of a bimetallic milling cutter taking into account differences in material properties as claimed in claim 1, characterized in that: The process of training the physics-guided neural network model using the training set is: The physical formula for the wear rate of the end milling tool wear degradation process is selected, and the undetermined coefficients in the physical formula are fitted based on the actual measured wear rate. The loss between the actual measured wear value and the predicted wear value is taken as the first loss, and the loss between the predicted wear rate based on the physical formula of the wear rate and the actual wear rate is taken as the second loss. The first and second losses are combined to construct a loss function that constrains the model output. Based on the loss function constructed with physical guidance, the model is iteratively trained using the training set until the maximum number of iterations is reached and the model training is completed.

7. A bimetallic milling cutter wear monitoring system taking into account material property differences, characterized in that: include: The signal acquisition module is used to collect the milling force and vibration signals of the bimetallic end milling tool in the milling process in real time, and is: to collect the milling force and milling vibration signals of the end milling tool in the process of milling bimetallic materials in real time through the sensor; The bimetal milling cutter wear material identification module is used to input the collected milling force and vibration signals into the unsupervised material identification model. After feature extraction and feature dimensionality reduction, the metal material category of the current signal is output through clustering. The bimetal milling cutter wear detection module is used to input the collected milling force, vibration signal and corresponding metal material category into the physical guidance neural network model based on the wear mechanism, and output the flank wear value of the bimetal end milling cutter; wherein, during the training process of the physical guidance neural network model, the loss between the actual measured wear value and the predicted wear value is used as the first loss, and the loss between the predicted wear rate based on the physical formula of the wear rate and the actual wear rate is used as the second loss, and the first and second losses are combined as the loss function.

8. An electronic device, characterized in that: include: a memory for storing executable instructions; The processor is configured to implement the bimetallic milling cutter wear monitoring method considering material property differences as described in any one of claims 1 to 6 when executing the executable instructions stored in the memory.

9. A computer-readable storage medium, characterized in that Executable instructions are stored, which are used to cause a processor to execute the executable instructions to implement a bimetallic milling cutter wear monitoring method considering material property differences as described in any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product includes executable instructions stored in a computer-readable storage medium; When the processor of the electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, the bimetallic milling cutter wear monitoring method considering material property differences as described in any one of claims 1 to 6 is implemented.

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