Tool wear monitoring method, device, equipment and readable storage medium

Abstract

The present invention provides a tool wear monitoring method, device, equipment, and readable storage medium, relating to the field of manufacturing technology. The tool wear monitoring method comprises: obtaining cutting process parameters and the surface roughness of a target workpiece when a target tool is machining the target workpiece; inputting the cutting process parameters and the surface roughness into a trained tool wear model to obtain a predicted tool wear value output by the trained tool wear model; inputting the cutting process parameters and the predicted tool wear value into a trained tool spindle power model to obtain a predicted spindle power value output by the trained tool spindle power model; and obtaining a tool wear monitoring result for the target tool based on a comparative relationship between the predicted spindle power value and the real-time spindle power value. The present invention can achieve real-time monitoring of tool wear status during the actual machining process without interfering with the actual machining process, with low cost and high efficiency.

Description

Technical Field

[0001] The present invention relates to the field of production and manufacturing technology, and in particular to a tool wear monitoring method, device, equipment and readable storage medium. Background Art

[0002] Tools are a crucial component of traditional parts processing. However, current decisions regarding tool replacement and part surface accuracy are largely based on the production staff's own experience. Especially when tool wear reaches critical levels, production staff must frequently stop the machine to check if the tool is still usable. This reduces production efficiency and increases the probability of part damage.

[0003] Among the tool wear prediction methods currently under study, most of the data signals used (such as cutting force signals, acoustic emission signals, vibration signals, etc.) are not only expensive to obtain, but the installation of the signal acquisition equipment will also cause unnecessary interference to actual production and processing.

[0004] In the selection of prediction models, different prediction model combinations have emerged, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTMN), Support Vector Machines (SVM), Deep Belief Networks (DBN), Generative Adversarial Networks (GAN), Back Propagation Networks (BP), etc. Most researchers only focus on the accuracy of the model and use complex input signals (signals fused from multiple sensor signals) for training, ignoring the training speed and prediction speed of the model, which greatly increases the training and prediction time, making it difficult to monitor the wear status of the tool in real time in actual processing production. Summary of the Invention

[0005] Embodiments of the present invention provide a tool wear monitoring method, device, equipment and readable storage medium to solve the problem in the prior art that it is difficult to monitor the wear status of a tool in real time during an actual machining process.

[0006] In order to solve the above technical problems, the embodiments of the present invention provide the following technical solutions:

[0007] An embodiment of the present invention provides a tool wear monitoring method, comprising:

[0008] Obtaining cutting process parameters when a target tool processes a target workpiece and the surface roughness of the target workpiece;

[0009] Inputting the cutting process parameters and the surface roughness into a trained tool wear model to obtain a predicted tool wear output by the trained tool wear model;

[0010] Inputting the cutting process parameters and the predicted tool wear amount into a trained tool spindle power model to obtain a predicted spindle power value output by the trained tool spindle power model;

[0011] According to the comparison relationship between the predicted spindle power value and the real-time spindle power value, a tool wear monitoring result of the target tool is obtained.

[0012] Optionally, the method further includes:

[0013] The historical cutting process parameters when the historical tools were used to process the historical workpieces and the historical tool wear of the historical tools were used as the input of the preset BP neural network model, and the historical surface roughness of the historical workpieces was used as the output of the preset BP neural network model. The preset BP neural network model was trained to obtain the trained tool wear model.

[0014] Optionally, the method further includes:

[0015] The historical cutting process parameters and the historical tool wear of the historical tools when processing the historical workpieces are used as the input of the preset BP neural network model, and the historical spindle power values ​​when the historical tools when processing the historical workpieces are used as the output of the preset BP neural network model. The preset BP neural network model is trained to obtain the trained tool spindle power model.

[0016] Optionally, historical cutting process parameters when a historical tool was used to process a historical workpiece and historical tool wear of the historical tool are used as inputs of a preset BP neural network model, and historical surface roughness of the historical workpiece is used as output of the preset BP neural network model. The preset BP neural network model is trained to obtain the trained tool wear model, including:

[0017] determining a first influence matrix according to the degree of influence of the historical cutting process parameters and the historical tool wear of the historical tool on the historical surface roughness of the historical workpiece;

[0018] The historical cutting process parameters, the historical tool wear and the first influence matrix are used as inputs of a preset BP neural network model, and the historical surface roughness is used as output of the preset BP neural network model. The preset BP neural network model is trained to obtain the trained tool wear model.

[0019] Optionally, historical cutting process parameters and historical tool wear values ​​of historical tools when processing historical workpieces are used as inputs of a preset BP neural network model, and historical spindle power values ​​of historical tools when processing historical workpieces are used as outputs of the preset BP neural network model are used. The preset BP neural network model is trained to obtain the trained tool spindle power model, including:

[0020] determining a second influence matrix according to the degree of influence of the historical cutting process parameters and the historical tool wear of the historical tool on the historical spindle power value;

[0021] The historical cutting process parameters, the historical tool wear and the second influence matrix are used as inputs of a preset BP neural network model, and the historical spindle power values ​​are used as outputs of the preset BP neural network model. The preset BP neural network model is trained to obtain the trained tool spindle power model.

[0022] Optionally, obtaining a tool wear monitoring result of the target tool according to a comparison relationship between the predicted spindle power value and the real-time spindle power value includes:

[0023] When the real-time spindle power value minus the predicted spindle power value is greater than a first preset threshold, determining that the target tool is in a tool damage state;

[0024] When the real-time spindle power value minus the predicted spindle power value is greater than a second preset threshold value, and the real-time spindle power value minus the predicted spindle power value is less than or equal to the first preset threshold value, determining that the target tool is in a tool wear state;

[0025] When the real-time spindle power value minus the predicted spindle power value is less than or equal to the second preset threshold, determining that the target tool is in a normal machining state;

[0026] The second preset threshold is smaller than the first preset threshold.

[0027] Optionally, the method further includes:

[0028] Establishing a mapping relationship between the historical surface roughness of the historical workpiece and the historical tool wear of the historical tool when the historical tool was used to process the historical workpiece;

[0029] According to the mapping relationship, the trained tool wear model is verified to obtain a verification result;

[0030] When the verification result indicates that when the historical surface roughness is the same, the absolute value of the difference between the historical tool wear and the predicted tool wear output by the trained tool wear model is less than a third preset threshold, it is determined that the trained tool wear model is available.

[0031] Optionally, before obtaining the tool wear monitoring result based on the comparison relationship between the predicted spindle power value and the real-time spindle power value, the method further includes:

[0032] determining a correlation coefficient between each characteristic quantity of the historical current when the historical tool processed the historical workpiece and the historical spindle power value when the historical tool processed the historical workpiece;

[0033] determining, according to the correlation coefficient, a target feature quantity that is most correlated with the historical spindle power value among the feature quantities;

[0034] The real-time spindle power value is obtained according to the target characteristic quantity of the real-time current when the target tool processes the target workpiece.

[0035] Optionally, before obtaining the cutting process parameters of the target tool and the surface roughness of the target workpiece when the target tool processes the target workpiece, the method further includes:

[0036] Obtain the teaching tool wear and teaching spindle power values ​​obtained by teaching machining;

[0037] Obtaining a tool wear teaching monitoring result based on a comparison relationship between the teaching spindle power value and the predicted spindle power value;

[0038] When the tool wear teaching monitoring result indicates that the taught spindle power value is less than the predicted spindle power value, it is determined to obtain the cutting process parameters of the target tool and the surface roughness of the target workpiece when the target tool processes the target workpiece.

[0039] An embodiment of the present invention further provides a tool wear monitoring device, comprising:

[0040] A first acquisition module is used to acquire cutting process parameters when a target tool processes a target workpiece and the surface roughness of the target workpiece;

[0041] A first processing module is configured to input the cutting process parameters and the surface roughness into a trained tool wear model to obtain a predicted tool wear output by the trained tool wear model;

[0042] a second processing module, configured to input the cutting process parameters and the predicted tool wear amount into a trained tool spindle power model to obtain a predicted spindle power value output by the trained tool spindle power model;

[0043] The third processing module is used to obtain the tool wear monitoring result of the target tool according to the comparison relationship between the predicted spindle power value and the real-time spindle power value.

[0044] An embodiment of the present invention also provides a tool wear monitoring device, comprising: a transceiver, a processor, a memory, and a program or instruction stored in the memory and executable on the processor; when the processor executes the program or instruction, the tool wear monitoring method described above is implemented.

[0045] An embodiment of the present invention further provides a readable storage medium having a program or instruction stored thereon, which implements the steps of any one of the above-described tool wear monitoring methods when the program or instruction is executed by a processor.

[0046] The beneficial effects of the present invention are:

[0047] The tool wear monitoring method provided by the solution of the present invention obtains the cutting process parameters when the target tool processes the target workpiece and the surface roughness of the target workpiece; inputs the cutting process parameters and the surface roughness into a trained tool wear model to obtain the predicted tool wear output by the trained tool wear model; inputs the cutting process parameters and the predicted tool wear into a trained tool spindle power model to obtain the predicted spindle power value output by the trained tool spindle power model; obtains the tool wear monitoring result based on the comparative relationship between the predicted spindle power value and the real-time spindle power value and the comparative relationship between the predicted tool wear and the tool wear threshold, which can realize real-time monitoring of the tool wear status during the actual processing process without interfering with the actual processing process, with low cost and high efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A flow chart showing a tool wear monitoring method provided by an embodiment of the present invention;

[0049] Figure 2 A specific flow chart showing a tool wear monitoring method provided by an embodiment of the present invention;

[0050] Figure 3 A schematic diagram showing the connection of a tool wear monitoring system provided by an embodiment of the present invention;

[0051] Figure 4A schematic diagram showing an interface of a tool wear monitoring system in a teaching machining state and a comparison relationship between a predicted spindle power value and a real-time spindle power value provided by an embodiment of the present invention;

[0052] Figure 5 A schematic diagram showing an interface of a tool wear monitoring system under normal machining conditions and a comparison relationship between a predicted spindle power value and a real-time spindle power value provided by an embodiment of the present invention;

[0053] Figure 6 A schematic diagram showing an interface of a tool wear monitoring system under a tool wear state and a comparison relationship between a predicted spindle power value and a real-time spindle power value provided by an embodiment of the present invention;

[0054] Figure 7 A schematic diagram showing an interface of a tool wear monitoring system under a tool breakage state and a comparison relationship between a predicted spindle power value and a real-time spindle power value provided by an embodiment of the present invention;

[0055] Figure 8 A schematic diagram showing the structure of a tool wear monitoring device provided by an embodiment of the present invention;

[0056] Figure 9 A schematic structural diagram of a tool wear monitoring device provided in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0057] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0058] The present invention addresses the problem in the prior art that it is difficult to monitor the wear status of a tool in real time during an actual machining process, and provides a tool wear monitoring method, device, equipment and a readable storage medium.

[0059] like Figure 1 As shown, an embodiment of the present invention provides a tool wear monitoring method, comprising:

[0060] Step 101: Obtain cutting process parameters when a target tool is used to process a target workpiece and the surface roughness of the target workpiece.

[0061] In an embodiment of the present invention, the target tool is a tool to be monitored, the target workpiece is a workpiece to be processed, the surface roughness of the target workpiece is known, and the cutting process parameters include at least one of the following: rotation speed, feed, cutting depth and cutting width.

[0062] Step 102: Input the cutting process parameters and the surface roughness into a trained tool wear model to obtain a predicted tool wear of a target tool output by the trained tool wear model.

[0063] In this step, the input of the trained tool wear model is five: speed, feed, cutting depth, cutting width and surface roughness of the target workpiece, and the output is the predicted tool wear.

[0064] Step 103: Input the cutting process parameters and the predicted tool wear amount into a trained tool spindle power model to obtain a predicted spindle power value output by the trained tool spindle power model.

[0065] In this step, the input of the trained tool spindle power model is five: speed, feed, cutting depth, cutting width and tool wear of the target tool, and the output is the predicted spindle power value.

[0066] Step 104: Obtaining a tool wear monitoring result based on a comparison relationship between the predicted spindle power value and the real-time spindle power value.

[0067] The real-time spindle power value is the real-time spindle power value of the machining center, which is obtained by collecting the current from a current sensor provided at the master control of the machining center, and does not affect actual machining production.

[0068] In this step, the comparison relationship between the predicted spindle power value and the real-time spindle power value is monitored. Based on the above two comparison relationships, the wear condition of the target tool can be determined. Specifically, when the real-time spindle power value minus the predicted spindle power value is greater than the first preset threshold value, it is determined that the target tool is damaged (the tool is in a damaged state), and it is necessary to stop the work to check the tool status and determine whether to replace it. When the real-time spindle power value minus the predicted spindle power value is greater than the second preset threshold value and less than or equal to the first preset threshold value, it is determined that the target tool is worn (the tool is in a worn state), and a tool wear prompt message is generated. When the real-time spindle power value minus the predicted spindle power value is less than or equal to the second preset threshold value, it is determined that the target tool is in normal use (the tool is in a normal processing state), wherein the second preset threshold value is less than the first preset threshold value.

[0069] Through the above steps, the tool wear during the processing can be monitored in real time without hindering processing production. The cost is low and the calculation speed is fast. Moreover, tool damage can be discovered in time, which improves processing efficiency and ensures processing accuracy.

[0070] In an optional embodiment of the present invention, the method further includes:

[0071] The historical cutting process parameters when the historical tools were used to process the historical workpieces and the historical tool wear of the historical tools were used as the input of the preset BP neural network model, and the historical surface roughness of the historical workpieces was used as the output of the preset BP neural network model. The preset BP neural network model was trained to obtain the trained tool wear model.

[0072] Specifically, in this embodiment, historical data from the process of machining historical workpieces with historical tools is collected and a database is established. From the database, historical cutting process parameters and historical tool wear are selected as inputs of a preset BP neural network model (BP neural network model 1). The historical surface roughness of the historical workpiece is used as the output of the preset BP neural network model. The preset BP neural network model is trained, and when the target error of the training is achieved, the trained tool wear model is obtained. Optionally, the selected historical data (including historical cutting process parameters, historical tool wear, and historical surface roughness) is split in a ratio of 4:1, with four portions of the historical data used as training data sets for training and one portion of the historical data used as a test data set for testing.

[0073] Among them, during training, the BP neural network model uses historical speed, historical feed, historical cutting depth, historical cutting width, and historical tool wear as input, and historical surface roughness as output. The BP neural network model consists of one input layer, one hidden layer, and one output layer, with a structure of 5-256-1.

[0074] In an optional embodiment of the present invention, the method further includes:

[0075] The historical cutting process parameters and the historical tool wear of the historical tools when processing the historical workpieces are used as the input of the preset BP neural network model, and the historical spindle power values ​​when the historical tools when processing the historical workpieces are used as the output of the preset BP neural network model. The preset BP neural network model is trained to obtain the trained tool spindle power model.

[0076] Specifically, in this embodiment, historical data from the process of machining historical workpieces using historical tools is collected and a database is established. Historical cutting process parameters and historical tool wear values ​​are selected from the database as inputs to a preset BP neural network model (BP neural network model 2), and historical spindle power values ​​are used as outputs of the preset BP neural network model. The preset BP neural network model is trained, and when the target error of the training is achieved, the trained tool spindle power model is obtained. Optionally, the selected historical data (including historical cutting process parameters, historical tool wear values, and historical spindle power values) is split in a ratio of 4:1, with four portions of the historical data used as training data sets for training and one portion of the historical data used as a test data set for testing.

[0077] Among them, during training, the BP neural network model uses historical speed, historical feed, historical cutting depth, historical cutting width, and historical tool wear as input, and historical spindle power value as output. The BP neural network model consists of one input layer, one hidden layer, and one output layer. The structure is 5-128-1, which has a simple structure and fast calculation speed.

[0078] Furthermore, in an embodiment of the present invention, in order to increase the training speed, a gain matrix is ​​calculated based on the degree of influence of the input on the output, which can enable the model to affect the distribution of weights during training, thereby achieving a state of rapid convergence and reducing the time for model training and learning.

[0079] During the model training process, an influence analysis and calculation is performed on the model input values ​​based on the output function. That is, in an optional embodiment of the present invention, historical cutting process parameters and historical tool wear of historical tools when processing historical workpieces are used as inputs of a preset BP neural network model, and historical surface roughness of historical workpieces is used as output of the preset BP neural network model. The preset BP neural network model is trained to obtain the trained tool wear model, including:

[0080] According to the degree of influence of the historical cutting process parameters and the historical tool wear of the historical tool on the historical surface roughness of the historical workpiece, a first influence matrix is ​​determined, the historical cutting process parameters, the historical tool wear and the first influence matrix are used as inputs of a preset BP neural network model, and the historical surface roughness is used as output of the preset BP neural network model. The preset BP neural network model is trained to obtain the trained tool wear model.

[0081] The specific operation is to design and conduct an analysis experiment on the influence of historical cutting process parameters and historical tool wear on the historical surface roughness of historical workpieces, and perform range analysis on the experimental data to obtain the primary and secondary influence of each historical cutting process parameter and historical tool wear on the historical surface roughness of the historical workpiece, and calculate the enhanced diagonal matrix A (i.e., the first influence matrix) based on the primary and secondary influences, A=diag{a 11 ,a 22 ,……,a nn}, where a nnIt is calculated based on the degree of influence of historical cutting process parameters and historical tool wear on the historical surface roughness of historical workpieces. Then, the historical cutting process parameters, the historical tool wear and the first influence matrix are used as inputs of a preset BP neural network model, and the historical surface roughness is used as the output of the preset BP neural network model. The preset BP neural network model is trained to obtain the trained tool wear model. The trained tool wear model formula is as follows:

[0082]

[0083] Among them, y j is the output of the jth neuron in the BP neural network model, σ is the activation function, A is the first influence matrix, w ij is the weight coefficient, specifically the connection weight from the i-th neuron to the j-th neuron in the BP neural network model, a i is the input of the jth neuron, b j is the bias, specifically the bias of the j-th neuron.

[0084] It should also be noted that different historical cutting process parameters and historical tool wear of historical tools have different degrees of influence on the historical surface roughness of historical workpieces. The greater the degree of influence, the greater the corresponding element a in the first influence matrix A. nn The larger the value, the greater the influence of the feed parameter in the historical cutting process parameters on the historical surface roughness of the historical workpiece. Therefore, the element a corresponding to the feed parameter in the historical cutting process parameters in the first influence matrix A is nn The value of is the largest.

[0085] In another optional embodiment of the present invention, historical cutting process parameters and historical tool wear values ​​of historical tools when processing historical workpieces are used as inputs of a preset BP neural network model, and historical spindle power values ​​of historical tools when processing historical workpieces are used as outputs of the preset BP neural network model are used. The preset BP neural network model is trained to obtain the trained tool spindle power model, including:

[0086] According to the degree of influence of the historical cutting process parameters and the historical tool wear of the historical tool on the historical spindle power value, a second influence matrix is ​​determined, the historical cutting process parameters, the historical tool wear and the second influence matrix are used as inputs of a preset BP neural network model, and the historical spindle power value is used as output of the preset BP neural network model. The preset BP neural network model is trained to obtain the trained tool spindle power model.

[0087] The specific operation is to design and conduct an analysis experiment on the influence of historical cutting process parameters and historical tool wear on historical spindle power values, and perform range analysis on the experimental data to obtain the primary and secondary influence of each historical cutting process parameter and historical tool wear on historical spindle power values, and calculate the enhanced diagonal matrix A (i.e., the second influence matrix) based on the primary and secondary influences, A=diag{a 11 ,a 22 ,……,a nn}, where a nn It is calculated based on the degree of influence of historical cutting process parameters and historical tool wear of historical tools on historical spindle power values. Then, the historical cutting process parameters, the historical tool wear and the second influence matrix are used as inputs of a preset BP neural network model, and the historical spindle power values ​​are used as outputs of the preset BP neural network model. The preset BP neural network model is trained to obtain the trained tool spindle power model, wherein the trained tool spindle power model is similar to the above-mentioned trained tool wear model formula and will not be repeated here.

[0088] It should also be noted that different historical cutting process parameters and historical tool wear of historical tools have different degrees of influence on the historical spindle power value. The greater the influence, the greater the corresponding element a in the second influence matrix A. nn The larger the value, the greater the historical spindle power value. Among them, the depth of cut in the historical cutting process parameters has the greatest influence on the historical spindle power value. Therefore, the element a corresponding to the depth of cut in the historical cutting process parameters in the second influence matrix A is nn The value of is the largest.

[0089] Furthermore, the process of training the tool wear model using the influence matrix in this optional embodiment is similar to the process of training the tool spindle power model using the influence matrix in the above optional embodiment, and will not be repeated here.

[0090] Using historical data as samples to train the improved model, the addition of matrix A allows for faster model convergence and increased computational speed. The trained tool spindle power model and tool wear model are saved for future use. Real-time machining data is added to the historical database, which is updated and used to further train the model, improving training accuracy.

[0091] Optionally, the method further includes:

[0092] A mapping relationship is established between the historical surface roughness of the historical workpiece and the historical tool wear of the historical tool when the historical tool processes the historical workpiece. According to the mapping relationship, the trained tool wear model is verified to obtain a verification result. Specifically, the established mapping relationship is based on an empirical formula to fit the historical surface roughness and the historical tool wear. Thereafter, the tool wear corresponding to the historical surface roughness is calculated according to the fitting equation and the tool wear corresponding to the historical surface roughness is calculated according to the trained tool wear model. The two tool wears are compared to obtain a verification result, that is, the verification result includes inputting the historical surface roughness and the corresponding historical cutting process parameters into the trained tool wear model under the same historical surface roughness, and the predicted tool wear output by the model. The predicted tool wear is compared with the historical tool wear to obtain a comparison result. If the verification result indicates that under the same historical surface roughness, the absolute value of the difference between the historical tool wear and the predicted tool wear output by the trained tool wear model is less than a third preset threshold value, then it is determined that the trained tool wear model is available.

[0093] After confirming that the trained tool wear model is available, actual processing is carried out to ensure the accuracy of tool wear prediction using the trained tool wear model.

[0094] In an optional embodiment of the present invention, before obtaining the tool wear monitoring result based on the comparison relationship between the predicted spindle power value and the real-time spindle power value, the method further includes:

[0095] Determine the correlation coefficient between each characteristic quantity of the historical current when the historical tool processed the historical workpiece and the historical spindle power value when the historical tool processed the historical workpiece. That is, in this optional example, collect historical data, establish a database, and calculate nine common characteristic vectors of the historical current based on the historical current in the database: mean value (MV), root mean square error (RMSE), square mean root (SMR), root mean square (RMS), maximum value (MA), skewness (SF), crest factor (KF), crest factor (CF), and margin factor (MF). The calculation formula for each characteristic vector is as follows:

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102]

[0103] CF: y8=y 5 / y4

[0104] MF: y9=y 5 / y3

[0105] Among them, y1 represents the mean value MV, y2 represents the mean square error RMSE, y3 represents the mean square amplitude SMR, y4 represents the root mean square RMS, y5 represents the maximum value MA, y6 represents the skewness SF, y7 represents the peak factor KF, y8 represents the kurtosis factor CF, y9 represents the margin factor MF, represents the function variable, and N represents the number of data.

[0106] Perform correlation analysis on the solved characteristic quantity and the predicted target (historical spindle power value) and calculate the Pearson correlation coefficient. The formula is as follows:

[0107]

[0108] Among them, r xy is the correlation coefficient, x i is the i-th data in sample X (feature quantity), is the mean value of sample X, y i is the i-th data in sample Y (prediction target), is the mean of the sample Y.

[0109] A target feature value that is most correlated with the historical spindle power value among the feature values ​​is determined based on the correlation coefficient; and the real-time spindle power value is obtained based on a target feature value of real-time current when a target tool is machining a target workpiece. The target feature value is one of the above-mentioned feature values.

[0110] It should also be noted that, during the model training process, the historical spindle power value can also be obtained based on the target characteristic quantity of the historical current through the above process, thereby improving the calculation efficiency and model accuracy.

[0111] The trained tool wear model and the trained tool spindle power model are integrated into a monitoring system for actual machining monitoring. Before the actual machining monitoring is performed, a teaching process is first performed, that is, before obtaining the cutting process parameters of the target tool and the surface roughness of the target workpiece when the target tool is machining the target workpiece, the method further includes:

[0112] The teaching tool wear amount and the teaching spindle power value obtained by the teaching processing are obtained, and the tool wear teaching monitoring result is obtained based on the comparative relationship between the teaching spindle power value and the predicted spindle power value. When the tool wear teaching monitoring result indicates that the teaching spindle power value is less than the predicted spindle power value, it is checked whether the tool is working normally. If there is no error in the teaching processing, normal processing can be carried out thereafter to determine the cutting process parameters of the target tool and the surface roughness of the target workpiece when the target tool is used to process the target workpiece.

[0113] The teaching processing may be processing performed by a target tool at a position other than a target processing position on the target workpiece.

[0114] The process of obtaining the teaching tool wear amount and the teaching spindle power value obtained by teaching machining is basically the same as the process of obtaining the predicted tool wear amount and the predicted spindle power value in steps 102 and 103, and will not be repeated here.

[0115] Perform teaching processing first and then normal processing. The current tool status is judged by different power responses, which can ensure the processing accuracy of parts while monitoring the tool.

[0116] It should also be noted that after obtaining the trained tool wear model, a mapping relationship between historical tool wear and historical surface roughness is established, and the mapping relationship is used to compare the mapping relationship between the predicted tool wear and the predicted surface roughness output by the trained tool wear model. The trained tool wear model is verified based on the comparison relationship.

[0117] In an embodiment of the present invention, a set of historical data is used to train two models. After the training is completed, the predicted tool wear and the predicted surface roughness can be predicted at one time, which is convenient for studying the relationship between tool wear and part surface quality. At the same time, the calculation of fitting equations (mapping relationships) is added to the establishment of the two models to perform a second verification of the predicted values ​​of the two models; as the number of processing productions increases, the more processing conditions are collected, and the amount of data accumulated increases, the prediction and monitoring capabilities of the model will become more and more powerful; finally, the data in the processing process is monitored by the monitoring system, which not only avoids tool damage but also ensures the processing accuracy of parts.

[0118] The following combination Figure 2 , using a specific embodiment to illustrate the specific process of the tool wear monitoring method:

[0119] The MIK-HRI-50A Hall effect current sensor is used to measure the spindle current data of the machine tool. A spindle current database is established to store all collected data. The collected current data is filtered and denoised to obtain spindle current data. Nine eigenvalues ​​are extracted and analyzed using the Pearson correlation coefficient analysis method to find the eigenvalue with the strongest correlation as the input feature. The power calculation formula is used to calculate the spindle power storage database. The calculated data is compared with the spindle power data changes inside the machine tool to verify the accuracy of the current. The spindle power data is determined based on the spindle current data according to the following formula:

[0120]

[0121] The calculated spindle power data is stored in a database, and the spindle power values ​​in the database are used to input the tool spindle power model for training.

[0122] An orthogonal tool wear experiment was designed to collect tool wear. Each set of orthogonal parameter combinations was divided into 50 cutting processes, with each process milling length of 1000mm. The machine tool used was a VMC1008B CNC machine tool, and the tool used was a φ8 carbide four-flute milling cutter. After each cutting experiment, the tool was removed and the width of the tool flank wear band was photographed and recorded using a HY-H2100 portable electron microscope. The data was then saved in a database. Simultaneously, the roughness of the machined surface was measured using a Mitutoyo surface roughness tester. Measurements were taken at three equidistant locations, and the average value was calculated as the surface roughness Ra value of the current tool in its current state, which was saved for future use.

[0123] The two models are trained and learned using the historical processed data in the prepared database. The processed data set is split in a 4:1 ratio, with 4 parts used as training data sets for training and 1 part used as test data sets for testing.

[0124] The tool wear data and surface roughness data are used to fit the equation to solve the mapping relationship, which makes it convenient to solve the threshold of tool wear VB according to the surface accuracy requirements. At the same time, the predicted values ​​of the two trained models can also be verified twice.

[0125] The two trained models are embedded in the tool wear prediction and monitoring system. Since the spindle current is collected in real time during the machine tool processing process (the internal spindle power value of the machine tool is collected through a data acquisition box, and the collected internal spindle power value of the machine tool is also stored in the database), the spindle power converted from the spindle current can be monitored as a real-time monitoring value. By inputting the calculated tool wear threshold, the spindle power threshold is automatically calculated and compared with the real-time collected power curve to achieve the purpose of automatically monitoring the spindle power.

[0126] The data collected during real-time monitoring is still saved in the established database, and the training operation is repeated to optimize and upgrade the trained tool wear prediction model and surface roughness prediction model to enhance their prediction and monitoring capabilities.

[0127] like Figure 3 As shown, an embodiment of the present invention also provides a tool wear monitoring system, which integrates the tool wear amount model (prediction model 1) and the trained tool spindle power model (prediction model 2) trained according to process parameters and spindle current into the tool wear prediction module and the processing accuracy prediction module respectively, determines the accuracy requirements and preset wear of the target workpiece, performs real-time power monitoring, and integrates it into the monitoring module for real-time data acquisition, collects it into the database, and integrates it into the data acquisition module through the data processor. The tool wear prediction module, the processing accuracy prediction module, the monitoring module and the data acquisition module are integrated into the tool wear monitoring system. The tool wear monitoring system is used to monitor the tool during the processing. The monitored states include four, including the teaching processing state, the normal processing state, the tool wear state and the tool damage state. Optionally, it is ensured that the processing can be stopped when the tool wear state is reached or the processing is stopped in the tool wear state.

[0128] The four states are judged by converting the data collected during real-time processing into spindle power data and comparing it with the power threshold output by the tool wear prediction module. Processing is performed under the currently determined processing parameters, and the power threshold is automatically calculated to ensure the accuracy requirements of the processing accuracy calculation. When the power curve is initially generated, teaching processing is required to stabilize the system. The interface of the tool wear monitoring system in the teaching processing state and the comparative relationship between the predicted spindle power value and the real-time spindle power value are shown in the figure below. Figure 4 As shown; below this threshold is the normal machining state, the interface of the tool wear monitoring system in the normal machining state and the comparative relationship between the predicted spindle power value and the real-time spindle power value are shown in the figure below. Figure 5 As shown; the real-time power curve approaches the threshold power curve when the tool enters the wear state. The interface of the tool wear monitoring system in the tool wear state and the comparative relationship between the predicted spindle power value and the real-time spindle power value are shown in the figure. Figure 6As shown; when the power mutation exceeds the threshold curve, it proves that the tool is damaged and it is in the tool damage state. It is necessary to stop processing immediately to check the tool status. The interface of the tool wear monitoring system in the tool damage state and the comparative relationship between the predicted spindle power value and the real-time spindle power value are shown in the figure. Figure 7 shown.

[0129] It should be noted that Figures 4 to 7 In the example, actual power refers to the real-time spindle power value, and power threshold refers to the predicted spindle power value.

[0130] The embodiment of the present invention saves all data during the entire technology use and establishes a database. For the data processing of the database, nine methods are used to extract characteristic values. Correlation analysis is performed based on the different inputs and outputs of different models, and the most highly correlated features are used as training inputs. The database data also receives and saves the real-time processing data, and uses the newly added data to further optimize and update the two models to make their predictions more accurate. The self-optimization and learning of the training model expand the model's functions and can predict more processing conditions.

[0131] like Figure 8 As shown, an embodiment of the present invention also provides a tool wear monitoring device, comprising:

[0132] The first acquisition module 801 is used to acquire cutting process parameters when a target tool processes a target workpiece and the surface roughness of the target workpiece;

[0133] A first processing module 802 is configured to input the cutting process parameters and the surface roughness into a trained tool wear model to obtain a predicted tool wear output by the trained tool wear model;

[0134] The second processing module 803 is configured to input the cutting process parameters and the predicted tool wear into a trained tool spindle power model to obtain a predicted spindle power value output by the trained tool spindle power model;

[0135] The third processing module 804 is configured to obtain a tool wear monitoring result of the target tool according to a comparison relationship between the predicted spindle power value and the real-time spindle power value.

[0136] Optionally, the device further comprises:

[0137] The first training module is used to use the historical cutting process parameters of the historical tools when processing the historical workpieces and the historical tool wear of the historical tools as the input of the preset BP neural network model, and use the historical surface roughness of the historical workpieces as the output of the preset BP neural network model, train the preset BP neural network model, and obtain the trained tool wear model.

[0138] Optionally, the device further comprises:

[0139] The second training module is used to use the historical cutting process parameters and the historical tool wear of the historical tools when processing the historical workpieces as the input of the preset BP neural network model, and use the historical spindle power values ​​when the historical tools processed the historical workpieces as the output of the preset BP neural network model, train the preset BP neural network model, and obtain the trained tool spindle power model.

[0140] Optionally, the first training module includes:

[0141] A first determining unit is configured to determine an influence matrix according to the influence of the historical cutting process parameters on the historical spindle power values;

[0142] The first training unit is used to take the historical cutting process parameters, the historical tool wear and the influence matrix as the input of the preset BP neural network model, take the historical surface roughness as the output of the preset BP neural network model, train the preset BP neural network model, and obtain the trained tool wear model.

[0143] Optionally, the second training module includes:

[0144] A second determining unit is configured to determine an influence matrix according to the influence of the historical cutting process parameters on the historical spindle power values;

[0145] The second training unit is used to take the historical cutting process parameters, the historical tool wear and the influence matrix as the input of the preset BP neural network model, and take the historical spindle power value as the output of the preset BP neural network model, train the preset BP neural network model, and obtain the trained tool spindle power model.

[0146] Optionally, the third processing module 804 includes:

[0147] a third determining unit, configured to determine that the target tool is in a tool damage state when the real-time spindle power value minus the predicted spindle power value is greater than a first preset threshold;

[0148] a fourth determining unit, configured to determine that the target tool is in a tool wear state if the real-time spindle power value minus the predicted spindle power value is greater than a second preset threshold value, and the real-time spindle power value minus the predicted spindle power value is less than or equal to the first preset threshold value;

[0149] a fifth determining unit, configured to determine that the target tool is in a normal machining state if the real-time spindle power value minus the predicted spindle power value is less than or equal to the second preset threshold;

[0150] The second preset threshold is smaller than the first preset threshold.

[0151] Optionally, the device further comprises:

[0152] a fourth processing module, configured to establish a mapping relationship between a historical surface roughness of a historical workpiece and a historical tool wear amount of the historical tool when the historical tool processed the historical workpiece;

[0153] A verification module, configured to verify the trained tool wear model according to the mapping relationship to obtain a verification result;

[0154] The first determination module is used to determine that the trained tool wear model is available when the verification result indicates that the historical surface roughness is the same and the absolute value of the difference between the historical tool wear and the predicted tool wear output by the trained tool wear model is less than a third preset threshold.

[0155] Optionally, the device further comprises:

[0156] The second determining module is used to determine the correlation coefficient between each characteristic quantity of the historical current when the historical tool processed the historical workpiece and the historical spindle power value when the historical tool processed the historical workpiece;

[0157] a third determining module, configured to determine, based on the correlation coefficient, a target feature quantity that is most correlated with the historical spindle power value among the feature quantities;

[0158] The fifth processing module is configured to obtain the real-time spindle power value according to the target characteristic quantity of the real-time current when the target tool processes the target workpiece.

[0159] Optionally, the device further comprises:

[0160] The second acquisition module is used to obtain the teaching tool wear value and the teaching spindle power value obtained by the teaching processing;

[0161] a sixth processing module, configured to obtain a tool wear teaching monitoring result based on a comparison relationship between the teaching spindle power value and the predicted spindle power value;

[0162] The fourth determination module is used to determine the cutting process parameters of the target tool and the surface roughness of the target workpiece when the target tool is used to process the target workpiece when the tool wear teaching monitoring result indicates that the teaching spindle power value is less than the predicted spindle power value.

[0163] It should be noted that the tool wear monitoring device provided in the embodiment of the present invention is a device capable of executing the above-mentioned tool wear monitoring method. All embodiments of the above-mentioned tool wear monitoring method are applicable to the device and can achieve the same or similar technical effects.

[0164] The embodiment of the present invention also provides a tool wear monitoring device, such as Figure 9 As shown, it includes: a processor 901; and a memory 902 connected to the processor 901 through a bus interface, the memory 902 is used to store programs and data used by the processor 901 when performing operations, and the processor 901 calls and executes the programs and data stored in the memory 902.

[0165] The transceiver 903 is connected to the bus interface and is used to receive and send data under the control of the processor 901.

[0166] Among them, Figure 9 In the embodiment, the bus architecture may include any number of interconnected buses and bridges, specifically linking together various circuits of one or more processors represented by processor 901 and memory represented by memory 902. The bus architecture may also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides a user interface 904. The transceiver 903 may be a plurality of components, i.e., a transmitter and a receiver, providing a unit for communicating with various other devices on a transmission medium. For different user devices, the user interface 904 may also be an interface capable of connecting external or internal devices as required, and the connected devices include but are not limited to a keypad, a display, a speaker, a microphone, a joystick, etc.

[0167] The processor 901 is responsible for managing the bus architecture and general processing, and the memory 902 can store data used by the processor 901 when performing operations.

[0168] The processor 901 performs the following processes:

[0169] Obtaining cutting process parameters when a target tool processes a target workpiece and the surface roughness of the target workpiece;

[0170] Inputting the cutting process parameters and the surface roughness into a trained tool wear model to obtain a predicted tool wear output by the trained tool wear model;

[0171] Inputting the cutting process parameters and the predicted tool wear amount into a trained tool spindle power model to obtain a predicted spindle power value output by the trained tool spindle power model;

[0172] According to the comparison relationship between the predicted spindle power value and the real-time spindle power value, a tool wear monitoring result of the target tool is obtained.

[0173] Optionally, the processor 901 is further configured to:

[0174] The historical cutting process parameters when the historical tools were used to process the historical workpieces and the historical tool wear of the historical tools were used as the input of the preset BP neural network model, and the historical surface roughness of the historical workpieces was used as the output of the preset BP neural network model. The preset BP neural network model was trained to obtain the trained tool wear model.

[0175] Optionally, the processor 901 is further configured to:

[0176] The historical cutting process parameters and the historical tool wear of the historical tools when processing the historical workpieces are used as the input of the preset BP neural network model, and the historical spindle power values ​​when the historical tools when processing the historical workpieces are used as the output of the preset BP neural network model. The preset BP neural network model is trained to obtain the trained tool spindle power model.

[0177] Optionally, the processor 901 is specifically configured to:

[0178] Determining an influence matrix according to the influence degree of the historical cutting process parameters on the historical spindle power value;

[0179] The historical cutting process parameters, the historical tool wear and the influence matrix are used as inputs of a preset BP neural network model, and the historical surface roughness is used as output of the preset BP neural network model. The preset BP neural network model is trained to obtain the trained tool wear model.

[0180] Optionally, the processor 901 is specifically configured to:

[0181] Determining an influence matrix according to the influence degree of the historical cutting process parameters on the historical spindle power value;

[0182] The historical cutting process parameters, the historical tool wear and the influence matrix are used as inputs of a preset BP neural network model, and the historical spindle power values ​​are used as outputs of the preset BP neural network model. The preset BP neural network model is trained to obtain the trained tool spindle power model.

[0183] Optionally, the processor 901 is specifically configured to:

[0184] When the real-time spindle power value minus the predicted spindle power value is greater than a first preset threshold, determining that the target tool is in a tool damage state;

[0185] When the real-time spindle power value minus the predicted spindle power value is greater than a second preset threshold value, and the real-time spindle power value minus the predicted spindle power value is less than or equal to the first preset threshold value, determining that the target tool is in a tool wear state;

[0186] When the real-time spindle power value minus the predicted spindle power value is less than or equal to the second preset threshold, determining that the target tool is in a normal machining state;

[0187] The second preset threshold is smaller than the first preset threshold.

[0188] Optionally, the processor 901 is further configured to:

[0189] Establishing a mapping relationship between the historical surface roughness of the historical workpiece and the historical tool wear of the historical tool when the historical tool was used to process the historical workpiece;

[0190] According to the mapping relationship, the trained tool wear model is verified to obtain a verification result;

[0191] When the verification result indicates that when the historical surface roughness is the same, the absolute value of the difference between the historical tool wear and the predicted tool wear output by the trained tool wear model is less than a third preset threshold, it is determined that the trained tool wear model is available.

[0192] Optionally, the processor 901 is specifically configured to:

[0193] determining a correlation coefficient between each characteristic quantity of the historical current when the historical tool processed the historical workpiece and the historical spindle power value when the historical tool processed the historical workpiece;

[0194] determining, according to the correlation coefficient, a target feature quantity that is most correlated with the historical spindle power value among the feature quantities;

[0195] The real-time spindle power value is obtained according to the target characteristic quantity of the real-time current when the target tool processes the target workpiece.

[0196] Optionally, the processor 901 is further configured to:

[0197] Obtain the teaching tool wear and teaching spindle power values ​​obtained by teaching machining;

[0198] Obtaining a tool wear teaching monitoring result based on a comparison relationship between the teaching spindle power value and the predicted spindle power value;

[0199] When the tool wear teaching monitoring result indicates that the taught spindle power value is less than the predicted spindle power value, it is determined to obtain the cutting process parameters of the target tool and the surface roughness of the target workpiece when the target tool processes the target workpiece.

[0200] Those skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware, or may be accomplished by instructing relevant hardware through a program, wherein the program includes instructions for executing part or all of the steps of the above method; and the program may be stored in a readable storage medium, which may be any form of storage medium.

[0201] An embodiment of the present invention further provides a readable storage medium, wherein a program is stored on the readable storage medium, and when the program is executed by a processor, the tool wear monitoring method as described in any one of the above items is implemented.

[0202] In the several embodiments provided in this application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection of some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0203] In addition, the functional units in various embodiments of the present invention may be integrated into a single processing unit, each unit may be physically included separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional units.

[0204] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to perform some of the steps of the sending and receiving methods described in various embodiments of the present invention. The aforementioned storage medium includes: a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, an optical disk, and other media that can store program code.

[0205] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary personnel in this technical field, several improvements and modifications can be made without departing from the principles described in the present invention. These improvements and modifications are also within the scope of protection of the present invention.

Claims

1. A tool wear monitoring method, characterized in that: include: Obtaining cutting process parameters when a target tool processes a target workpiece and the surface roughness of the target workpiece; Inputting the cutting process parameters and the surface roughness into a trained tool wear model to obtain a predicted tool wear output by the trained tool wear model; Inputting the cutting process parameters and the predicted tool wear amount into a trained tool spindle power model to obtain a predicted spindle power value output by the trained tool spindle power model; According to the comparison relationship between the predicted spindle power value and the real-time spindle power value, a tool wear monitoring result of the target tool is obtained.

2. The method according to claim 1, characterized in that The method further comprises: The historical cutting process parameters when the historical tools were used to process the historical workpieces and the historical tool wear of the historical tools were used as the input of the preset BP neural network model, and the historical surface roughness of the historical workpieces was used as the output of the preset BP neural network model. The preset BP neural network model was trained to obtain the trained tool wear model.

3. The method according to claim 1, characterized in that The method further comprises: The historical cutting process parameters and the historical tool wear of the historical tools when processing the historical workpieces are used as the input of the preset BP neural network model, and the historical spindle power values ​​when the historical tools when processing the historical workpieces are used as the output of the preset BP neural network model. The preset BP neural network model is trained to obtain the trained tool spindle power model.

4. The method according to claim 2, characterized in that The method includes: using historical cutting process parameters and historical tool wear of historical tools when processing historical workpieces as inputs of a preset BP neural network model; using historical surface roughness of the historical workpieces as outputs of the preset BP neural network model; and training the preset BP neural network model to obtain the trained tool wear model. determining a first influence matrix according to the degree of influence of the historical cutting process parameters and the historical tool wear of the historical tool on the historical surface roughness of the historical workpiece; The historical cutting process parameters, the historical tool wear and the first influence matrix are used as inputs of a preset BP neural network model, and the historical surface roughness is used as output of the preset BP neural network model. The preset BP neural network model is trained to obtain the trained tool wear model.

5. The method according to claim 3, characterized in that The method includes: using historical cutting process parameters and historical tool wear of historical tools when processing historical workpieces as inputs of a preset BP neural network model; using historical spindle power values ​​when processing historical workpieces by historical tools as outputs of the preset BP neural network model; and training the preset BP neural network model to obtain the trained tool spindle power model. The method includes: determining a second influence matrix according to the degree of influence of the historical cutting process parameters and the historical tool wear of the historical tool on the historical spindle power value; The historical cutting process parameters, the historical tool wear and the second influence matrix are used as inputs of a preset BP neural network model, and the historical spindle power values ​​are used as outputs of the preset BP neural network model. The preset BP neural network model is trained to obtain the trained tool spindle power model.

6. The method according to claim 1, characterized in that According to the comparison relationship between the predicted spindle power value and the real-time spindle power value, a tool wear monitoring result of the target tool is obtained, including: When the real-time spindle power value minus the predicted spindle power value is greater than a first preset threshold, determining that the target tool is in a tool damage state; When the real-time spindle power value minus the predicted spindle power value is greater than a second preset threshold value, and the real-time spindle power value minus the predicted spindle power value is less than or equal to the first preset threshold value, determining that the target tool is in a tool wear state; When the real-time spindle power value minus the predicted spindle power value is less than or equal to the second preset threshold, determining that the target tool is in a normal machining state; The second preset threshold is smaller than the first preset threshold.

7. The method according to claim 1, characterized in that The method further comprises: Establishing a mapping relationship between the historical surface roughness of the historical workpiece and the historical tool wear of the historical tool when the historical tool was used to process the historical workpiece; According to the mapping relationship, the trained tool wear model is verified to obtain a verification result; When the verification result indicates that when the historical surface roughness is the same, the absolute value of the difference between the historical tool wear and the predicted tool wear output by the trained tool wear model is less than a third preset threshold, it is determined that the trained tool wear model is available.

8. The method according to claim 1, characterized in that Before obtaining the tool wear monitoring result based on the comparative relationship between the predicted spindle power value and the real-time spindle power value, the method further includes: determining a correlation coefficient between each characteristic quantity of the historical current when the historical tool processed the historical workpiece and the historical spindle power value when the historical tool processed the historical workpiece; determining, according to the correlation coefficient, a target feature quantity that is most correlated with the historical spindle power value among the feature quantities; The real-time spindle power value is obtained according to the target characteristic quantity of the real-time current when the target tool processes the target workpiece.

9. The method according to claim 1, characterized in that Before obtaining the cutting process parameters of the target tool and the surface roughness of the target workpiece when the target tool processes the target workpiece, the method further includes: Obtain the teaching tool wear and teaching spindle power values ​​obtained by teaching machining; Obtaining a tool wear teaching monitoring result based on a comparison relationship between the teaching spindle power value and the predicted spindle power value; When the tool wear teaching monitoring result indicates that the taught spindle power value is less than the predicted spindle power value, it is determined to obtain the cutting process parameters of the target tool and the surface roughness of the target workpiece when the target tool processes the target workpiece.

10. A tool wear monitoring device, characterized in that: include: A first acquisition module is used to acquire cutting process parameters when a target tool processes a target workpiece and the surface roughness of the target workpiece; A first processing module is configured to input the cutting process parameters and the surface roughness into a trained tool wear model to obtain a predicted tool wear output by the trained tool wear model; a second processing module, configured to input the cutting process parameters and the predicted tool wear amount into a trained tool spindle power model to obtain a predicted spindle power value output by the trained tool spindle power model; The third processing module is used to obtain the tool wear monitoring result of the target tool according to the comparison relationship between the predicted spindle power value and the real-time spindle power value.

11. A tool wear monitoring device comprising: A transceiver, a processor, a memory, and a program or instruction stored in the memory and executable on the processor; characterized in that when the processor executes the program or instruction, the tool wear monitoring method according to any one of claims 1 to 9 is implemented.

12. A readable storage medium having a program or instruction stored thereon, characterized in that: When the program or instruction is executed by a processor, the steps of the tool wear monitoring method according to any one of claims 1 to 9 are implemented.

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