A tool wear change amount analysis method, device and electronic equipment

Abstract

A tool wear change amount analysis method and device and electronic equipment, relating to the tool wear field. In the method, first control data and first sensors collected by a data acquisition unit are obtained within a preset time period; the first control data and the first sensor data are preprocessed to obtain second control data and second sensor data; the second control data and the second sensor data are input into a preset tool wear amount prediction model; a wear amount corresponding to the second control data and the second sensor data is obtained; and if the wear amount is greater than or equal to a preset first wear threshold, it is determined that the wear degree of the tool is severe wear, and the wear degree includes one of normal, slight wear and severe wear. The technical solution provided in the application can analyze the tool wear amount in a timely manner, and avoid workpiece quality problems caused by excessive wear.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a method, apparatus, and electronic equipment for analyzing tool wear variation. Background Technology

[0002] In the machining process, the characteristics of equipment (such as machine tools and cutting tools), materials, processes, and cutting tools directly affect the quality, efficiency, and stability of the machining. Therefore, determining the optimization needs of the process and the occurrence of abnormal conditions are crucial steps for improving machining results and reducing production risks. Since the cutting tool and workpiece come into contact and are cut, the degree of tool wear directly affects the cutting quality of the workpiece.

[0003] Tool wear causes changes in the size and shape of the workpiece being cut, thus reducing its dimensional and geometric accuracy. This can lead to products exceeding specifications or failing to meet geometric requirements. Accurate assessment of tool wear helps maintain the efficient operation of the production line. During production, severely worn tools can cause workpiece quality problems.

[0004] Therefore, there is an urgent need for a method, device, and electronic equipment for analyzing tool wear variation. Summary of the Invention

[0005] This application provides a method, apparatus, and electronic device for analyzing tool wear variation, thereby avoiding workpiece quality problems caused by excessive wear.

[0006] The first aspect of this application provides a method for analyzing tool wear variation. The method includes: acquiring first control data and first sensor data acquired by a data acquisition unit within a preset time period; the first control data includes multiple spindle feed speeds, multiple cutting speeds, and multiple cutting depths of the machine tool; the first sensor data includes multiple temperature data, multiple sound intensities, and multiple current data; preprocessing the first control data and the first sensor data to obtain second control data and second sensor data; inputting the second control data and the second sensor data into a preset tool wear prediction model to obtain the wear amount corresponding to the second control data and the second sensor data; the preset tool wear prediction model includes the second control data, the second sensor data, and the correspondence between the wear amount; if the wear amount is greater than or equal to a preset first wear threshold, then determining the tool wear degree as severe wear, where the wear degree includes normal, slight wear, and severe wear.

[0007] By adopting the above technical solution, and through comprehensive analysis and processing of control data and collected sensor data, and by using a deep learning model, the amount of tool wear can be predicted and judged. For severely worn tools, they can be repaired or replaced in a timely manner, avoiding quality problems caused by excessive wear.

[0008] Optionally, the first control data and the first sensor data are preprocessed to obtain the second control data. Specifically, the preprocessing of the first control data to obtain the second control data includes: converting the first control data into frictional force, and converting the first spindle speed and the first cutting speed and the first cutting depth into cutting force. The first spindle speed is any one of multiple spindle speeds, the first cutting speed is any one of multiple cutting speeds, and the first cutting depth is any one of multiple cutting depths; assigning weights to the frictional force and the cutting force, and performing a weighted summation of the frictional force and the cutting force to obtain the second control data.

[0009] By adopting the above technical solution, the original complex first control data is directly converted into force information that is more directly related to the cutting process, reducing additional feature engineering processing. The converted data is more in line with the physical nature of the cutting process. At the same time, the converted data is used for weight assignment and weighted summation, which realizes more refined cutting parameter optimization.

[0010] Optionally, the first control data and the first sensor data are preprocessed to obtain the second control data and the second sensor data. Specifically, the preprocessing of the first sensor data to obtain the second sensor data includes: acquiring first temperature data, first sound intensity, and first current data, wherein the first temperature data is a temperature data that is greater than the second temperature data among multiple temperature data, the second temperature data is any temperature data other than the first temperature data among multiple temperature data, the first sound intensity is a sound intensity that is greater than the second sound intensity among multiple sound intensities, the second sound intensity is any sound intensity other than the first sound intensity among multiple sound intensities, and the first current data is the average value of multiple current data; and the first temperature data, the first sound intensity, and the first current data are weighted and summed to obtain the second sensor data.

[0011] By adopting the above technical solution, the maximum value of temperature data, the maximum value of sound intensity, and the average value of current data constitute a comprehensive feature. The comprehensive feature can reduce the data dimensions and simplify the subsequent data analysis process.

[0012] Optionally, before acquiring the first control data and the first sensor data acquired by the data acquisition unit, the method further includes: acquiring a training sample library, which includes multiple samples, including historical control data, historical sensor data, and wear amount corresponding to the historical time period; and inputting the training sample library into a preset tool wear amount prediction model for training.

[0013] By employing the above technical solution, historical control data, historical sensor data, and corresponding wear values ​​are acquired as training samples, and the tool wear prediction model is trained based on these training samples. This allows the model to learn patterns and trends from historical samples, thereby improving the accuracy of wear prediction.

[0014] Optionally, after obtaining the wear amount corresponding to the second control data and the second sensor data, the method further includes: if the wear amount is greater than or equal to a preset second wear amount threshold and the wear amount is less than a preset first wear amount threshold, then the wear degree of the tool is determined to be slight wear, and the first wear amount threshold is greater than the second wear amount threshold; obtaining the condition information of the cut finished product, and performing a similarity judgment between the condition information of the cut finished product and preset condition information, the condition information including the length, straightness and geometry of the cut finished product; if the similarity is greater than a preset similarity threshold, then adjusting the spindle speed of the machine tool to a preset minimum spindle feed speed and adjusting the cutting speed of the tool to a preset minimum tool cutting speed.

[0015] By adopting the above technical solution, a first and a second preset wear threshold are set, and the degree of tool wear is determined based on the actual wear amount. If the tool is in a state of slight wear, the condition information of the cut product is obtained and compared with the preset condition information. If the similarity is greater than the preset similarity threshold, it indicates that the quality of the cut product meets expectations. In this case, the spindle speed of the machine tool is adjusted to the preset minimum spindle feed speed, and the cutting speed of the tool is adjusted to the preset minimum tool cutting speed, thereby reducing tool wear and preventing greater wear.

[0016] Optionally, after obtaining the wear amount corresponding to the second control data and the second sensor data, the method further includes: if the wear amount is less than a preset second wear amount threshold, then the wear degree of the tool is determined to be in a normal state; acquiring the first control data and the first sensor data corresponding to the machine tool in the normal state; and saving the first control data and the first sensor data to the training sample library.

[0017] By adopting the above technical solution, when the first control data and first sensor data of the machine tool under normal conditions are obtained, these data can be saved to the training sample library for subsequent analysis and model training.

[0018] Optionally, if the wear amount is greater than or equal to a preset first wear threshold, and the tool is determined to be severely worn, the method further includes: displaying a prompt message to prompt the user to repair or replace the tool.

[0019] By adopting the above technical solution and setting a first wear threshold, it is possible to provide early warning and alert the user to the severe wear condition of the tool.

[0020] A second aspect of this application provides a tool wear variation analysis device, which includes: an acquisition module, a processing module, an input module, and a judgment module;

[0021] The acquisition module is used to acquire first control data and first sensor data collected by the data acquisition unit within a preset time period. The first control data includes the spindle transfer speed, cutting speed and cutting depth of the machine tool. The first sensor data includes temperature, sound intensity and current data.

[0022] The processing module is used to process the first control data and the first sensor data to obtain the second control data and the second sensor data.

[0023] The input module is used to input the second control data and the second sensor data into the preset tool wear prediction model to obtain the wear amount corresponding to the second control data and the second sensor data. The preset tool wear prediction model includes the second control data, the second sensor data and the correspondence between the wear amounts.

[0024] The judgment module is used to determine the wear level of the tool as severe wear if the wear amount is greater than or equal to a preset first wear threshold. The wear level includes one of normal wear, slight wear, and severe wear.

[0025] Optionally, the processing module preprocesses the first control data and the first sensor data to obtain the second control data. Specifically, the preprocessing of the first control data to obtain the second control data includes: the processing module performing data conversion on the first control data, which includes converting the machine tool's first spindle movement speed into frictional force and the machine tool's first cutting speed and first cutting depth into cutting force. The first spindle movement speed is any one of multiple spindle movement speeds, the first cutting speed is any one of multiple cutting speeds, and the first cutting depth is any one of multiple cutting depths. The processing module assigns weights to the frictional force and the cutting force and performs a weighted summation of the frictional force and the cutting force to obtain the second control data.

[0026] Optionally, the processing module preprocesses the first control data and the first sensor data to obtain the second control data and the second sensor data. Specifically, the preprocessing of the first sensor data to obtain the second sensor data includes: the acquisition module acquiring first temperature data, first sound intensity, and first current data, wherein the first temperature data is a temperature data that is greater than the second temperature data among multiple temperature data, the second temperature data is any temperature data other than the first temperature data among multiple temperature data, the first sound intensity is a sound intensity that is greater than the second sound intensity among multiple sound intensities, the second sound intensity is any sound intensity other than the first sound intensity among multiple sound intensities, and the first current data is the average value of multiple current data; the processing module uses a weighted summation method to obtain the second sensor data from the first temperature data, the first sound intensity, and the first current data.

[0027] Optionally, before the acquisition module acquires the first control data and the first sensor data acquired by the data acquisition unit, the method further includes: the acquisition module acquiring a training sample library, which includes multiple samples, including historical control data, historical sensor data and wear amount corresponding to the historical time period; and the input module inputting the training sample library into a preset tool wear amount prediction model for training.

[0028] Optionally, after obtaining the wear amount corresponding to the second control data and the second sensor data, the method further includes: if the wear amount is greater than or equal to a preset second wear amount threshold and the wear amount is less than a preset first wear amount threshold, the processing module determines that the wear degree of the tool is slight wear, and the first wear amount threshold is greater than the second wear amount threshold; the acquisition module acquires the condition information of the cut finished product, and performs a similarity judgment between the condition information of the cut finished product and preset condition information, the condition information including the length, straightness and geometry of the cut finished product; if the similarity is greater than a preset similarity threshold, the processing module adjusts the spindle speed of the machine tool to a preset minimum spindle feed speed and adjusts the cutting speed of the tool to a preset minimum tool cutting speed.

[0029] Optionally, after the processing module obtains the wear amount corresponding to the second control data and the second sensor data, the method further includes: if the wear amount is less than a preset second wear amount threshold, the processing module determines that the wear degree of the tool is in a normal state; the acquisition module acquires the first control data and the first sensor data corresponding to the machine tool when it is in a normal state; and the processing module saves the first control data and the first sensor data to the training sample library.

[0030] Optionally, if the wear amount is greater than or equal to a preset first wear threshold, after the processing module determines that the wear degree of the tool is severe wear, the method further includes: the processing module displays a prompt message, which is used to prompt the user to repair or replace the tool.

[0031] A third aspect of this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform any of the methods described above.

[0032] A fourth aspect of this application provides a computer-readable storage medium storing computer instructions. When the instructions are executed, the method steps described above are performed.

[0033] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0034] 1. By analyzing and processing comprehensive control data and collected sensor data, and using a deep learning model, the wear of cutting tools can be predicted and judged. For severely worn tools, timely repair or replacement can be carried out to avoid quality problems caused by excessive wear.

[0035] 2. The original complex first control data is directly converted into force information that is more directly related to the cutting process, reducing additional feature engineering processing. The converted data is more in line with the physical nature of the cutting process. At the same time, the converted data is used for weight assignment and weighted summation, which realizes more refined cutting parameter optimization.

[0036] 3. By setting a preset first wear threshold and a second wear threshold, and based on the actual wear amount, the degree of tool wear is determined. If the tool is in a slightly worn state, the condition information of the cut product is obtained and compared with the preset condition information. If the similarity is greater than the preset similarity threshold, it indicates that the quality of the cut product meets expectations. In this case, the machine tool spindle speed is adjusted to the preset minimum spindle feed speed, and the tool cutting speed is adjusted to the preset minimum tool cutting speed, thereby reducing tool wear and preventing greater wear. Attached Figure Description

[0037] Figure 1 This is a schematic flowchart of a tool wear change analysis method disclosed in an embodiment of this application.

[0038] Figure 2 This is a schematic diagram of the structure of a tool wear change analysis device disclosed in an embodiment of this application.

[0039] Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application.

[0040] Explanation of reference numerals in the attached drawings: 201, acquisition module; 202, processing module; 203, input module; 204, judgment module; 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed Implementation

[0041] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0042] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0043] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0044] This application provides a method, apparatus, and electronic device for estimating tool wear variation, with reference to... Figure 1 , Figure 1 This is a flowchart illustrating a method for estimating tool wear variation according to an embodiment of this application. The method is applied to a server and includes steps S101 to S104, as follows:

[0045] Step S101: Within a preset time period, acquire first control data and first sensor data acquired by the data acquisition unit. The first control data includes multiple spindle transfer speeds, multiple cutting speeds, and multiple cutting depths of the machine tool. The first sensor data includes multiple temperature data, multiple sound intensities, and multiple current data.

[0046] Before step S101, the method further includes obtaining a training sample library, which includes multiple samples, including historical control data, historical sensor data, and wear amount corresponding to the historical time period; and inputting the training sample library into a preset tool wear amount prediction model for training.

[0047] Specifically, a training sample library is obtained from historical time periods. This library can include multiple samples, each corresponding to data from a specific historical time period. Each sample includes historical control data, historical sensor data, and the corresponding wear amount within that historical time period. A deep learning framework is used to compile the model. Control data, sensor data, and tool wear amounts from historical time periods are collected. The dataset is preprocessed, including data cleaning, missing value handling, and data normalization. To ensure the data format is suitable for the input of the deep learning model, the dataset is divided into training, validation, and test sets. 70-80% of the data is used as the training set, 10-15% as the validation set, and the remaining 10-15% as the test set. Relevant features related to the control and sensor data are extracted. Correlation analysis is used to determine which sensor data have a strong correlation with tool wear. For example, the Pearson correlation coefficient between each sensor feature and tool wear can be calculated, and sensor data with a correlation higher than a certain threshold with tool wear can be selected as features. If the temperature data, sound intensity, and current magnitude in the sensor data contain periodic fluctuations, moving averages are used to smooth these data to reduce the impact of noise. For time-series characteristics, various time-related features are extracted to capture temperature change trends in sensor data. During training, cross-validation is used to improve the model's robustness and accuracy. The goal of training is to build a model capable of accurately predicting tool wear through learning from and analyzing historical data. The mean squared error function is chosen as the loss function, and the predicted results are compared with the actual data until the loss function converges to a predetermined stopping criterion.

[0048] In step S101, first control data is directly obtained from the machine tool's control unit within a preset time period. The first control data includes multiple spindle transfer speeds, multiple cutting speeds, and multiple cutting depths of the machine tool. First sensor data is obtained from the machine tool's sampling data acquisition unit. The first sensor data includes multiple temperature data, multiple sound intensities, and multiple current data within the preset time period.

[0049] Step S102: Preprocess the first control data and the first sensor data to obtain the second control data and the second sensor data.

[0050] In the above steps, the first control data is preprocessed to obtain the second control data, specifically including: converting the first control data into frictional force, and converting the first spindle speed and first depth of cut into cutting force. The first spindle speed is any one of multiple spindle speeds, the first cutting speed is any one of multiple cutting speeds, and the first depth of cut is any one of multiple cutting depths; assigning weights to the frictional force and the cutting force, and performing a weighted summation of the frictional force and the cutting force to obtain the second control data.

[0051] Specifically, the first spindle speed is converted into friction force according to the friction model, with the formula F = γ * v, where F is the friction force, v is the first spindle speed, and γ is the viscosity coefficient. The specific value of γ can be set according to actual needs. During the cutting process, friction is one of the main components of the cutting force. Cutting force is an important factor affecting wear. Converting the spindle speed into friction force allows for a more accurate simulation of the influence of friction during the cutting process, and this can be used as part of the model input. This improves the accuracy of wear prediction, thereby optimizing the workpiece machining process. Simultaneously, the first cutting speed and first cutting depth are converted into cutting force, with the formula Fc = Kc * Vc * d, where Fc represents the cutting force, Kc represents the cutting force coefficient, Vc is the cutting speed, and d represents the cutting depth. The converted data better reflects the physical nature of the cutting process, making modeling simpler and more intuitive. The relative contributions of friction and cutting force are considered, and second control data for tool wear estimation is generated. For example, when performing weighted summation using the transformed data, the weighting operation can be performed directly without considering the weighting methods of different dimensions of data, thus improving the feasibility of the model.

[0052] For example, friction and cutting forces are assigned specific weights. Cutting forces are considered to have a greater impact on tool wear in actual measurements, so a higher weight (wc) is assigned to cutting forces, while friction forces have a lower weight (wf). Based on these weights, they are weighted and summed to obtain the second control data.

[0053] The first sensor data is preprocessed to obtain the second sensor data, specifically including: acquiring first temperature data, first sound intensity, and first current data, wherein the first temperature data is a temperature data that is greater than the second temperature data among multiple temperature data, the second temperature data is any temperature data other than the first temperature data among multiple temperature data, the first sound intensity is a sound intensity that is greater than the second sound intensity among multiple sound intensities, the second sound intensity is any sound intensity other than the first sound intensity among multiple sound intensities, and the first current data is the average value of multiple current data; the first temperature data, the first sound intensity, and the first current data are weighted and summed to obtain the second sensor data.

[0054] Specifically, within a preset time period, multiple temperature data points, multiple sound intensities, and multiple current data points are acquired through sensors. The first temperature data point is the maximum value among the multiple temperature data points, the first sound intensity data point is the maximum value among the multiple sound intensities, and the first current intensity data point is the maximum value among the multiple current data points. Temperature and sound intensities are typically real-time, continuously sampled data. When the tool experiences external interference or other factors, these data points can fluctuate suddenly. Selecting the maximum value captures the largest fluctuation peak in the data. The maximum temperature fluctuation peak may be related to a rapid increase in tool wear. For sound intensities, the sound characteristics during the cutting process change with tool wear. Selecting the maximum sound intensity captures the largest sound peak during the cutting process. The maximum sound peak may be related to a sudden change in tool wear. Current data represents the current consumption of a continuous process and typically does not fluctuate as rapidly as temperature or sound intensities. Current data is usually an indicator of power consumption during the cutting process. Selecting the average current value reflects the average power consumption level during the cutting process and is related to the overall trend of tool wear. Using weighted summation, a weight can be assigned to each type of sensor data, and these weights are summed to obtain the second sensor data. The general form of the linear combination is as follows: Second sensor data = (Weight 1 * First temperature data) + (Weight 2 * First sound intensity) + (Weight 3 * First current data), where Weight 1 corresponds to the first temperature data, Weight 2 corresponds to the first sound intensity, and Weight 3 corresponds to the first current data. Summing the weights integrates information from different sensor data into a single feature, which helps reduce data dimensionality.

[0055] Step S103: Input the second control data and the second sensor data into the preset tool wear prediction model to obtain the wear amount corresponding to the second control data and the second sensor data. The preset tool wear prediction model includes the second control data, the second sensor data and the correspondence between the wear amounts.

[0056] In the above steps, the preprocessed second control data and second sensor data are input into the input interface of the preset tool wear model, ensuring that the data format and units match the requirements of the preset tool wear model. The model will calculate and analyze the input second control data and second sensor data to generate an estimate of the tool wear, and simultaneously output the wear amount corresponding to the second control data and second sensor data.

[0057] After step S103, the method further includes: if the wear amount is greater than or equal to a preset second wear amount threshold and the wear amount is less than a preset first wear amount threshold, then the wear degree of the tool is determined to be slight wear, and the first wear amount threshold is greater than the second wear amount threshold; obtaining the condition information of the cut finished product, and performing a similarity judgment between the condition information of the cut finished product and preset condition information, the condition information including the length, straightness and geometry of the cut finished product; if the similarity is greater than a preset similarity threshold, then adjusting the spindle speed of the machine tool to a preset minimum spindle feed speed and adjusting the cutting speed of the tool to a preset minimum tool cutting speed.

[0058] Specifically, a first wear threshold and a second wear threshold are preset. The first wear threshold must be greater than the second wear threshold to determine the boundary between slight and severe wear. Based on the collected data, the tool wear is calculated. Then, it is compared with the preset first and second wear thresholds. If the calculated wear is greater than or equal to the preset second wear threshold and less than the preset first wear threshold, the tool wear is determined to be slight. The condition information of the cut product is acquired, including length, straightness, and geometry. This information can be collected by sensors; for example, length can be measured by measuring the actual length of the cut product, straightness can be measured by measuring the curvature of the cut product, and geometry can be obtained through image processing. The condition information of the cut product is compared with preset condition information for similarity assessment. Various similarity measurement methods can be used, such as cosine similarity and Euclidean distance. The quality of the cut product is judged by comparing the similarity between the cut product condition and the preset condition. If the similarity is greater than a preset similarity threshold, an adjustment operation is performed. Specific operations include adjusting the machine tool's spindle travel speed to a preset minimum spindle feed speed and adjusting the tool's cutting speed to a preset minimum tool cutting speed. These adjustments aim to reduce wear by lowering the spindle travel speed and tool cutting speed while ensuring the quality of the cut product.

[0059] After step S103, the method further includes: after obtaining the wear amount corresponding to the second control data and the second sensor data, the method further includes: if the wear amount is less than the preset second wear amount threshold, then the wear degree of the tool is determined to be normal; when the normal state is obtained, the first control data and the first sensor data corresponding to the machine tool are obtained; and the first control data and the first sensor data are saved to the training sample library.

[0060] Specifically, if the calculated wear amount is less than a preset second wear threshold, the tool wear is determined to be in a normal state. When the tool is in a normal state, the corresponding first control data and first sensor data of the machine tool are acquired. The acquired first control data and first sensor data are saved to a training sample library. This training sample library can be used for subsequent machine learning algorithm training to optimize tool cutting parameters and wear prediction, etc.

[0061] Step S104: If the wear amount is greater than or equal to the preset first wear threshold, the wear degree of the tool is determined to be severe wear. The wear degree includes normal wear, slight wear and severe wear.

[0062] In the above steps, the wear level is classified into one of three categories: normal wear, slight wear, and severe wear, based on different degrees of wear. The obtained wear amount is compared with a preset first wear threshold. If the wear amount is greater than or equal to the preset first wear threshold, the tool wear level is determined to be severe wear.

[0063] After step S104, the method includes: displaying a prompt message to prompt the user to repair or replace the tool.

[0064] Specifically, prompts can be displayed to operators or staff via a human-machine interface. These prompts can be text, graphics, or warnings displayed on monitoring screens, terminals, mobile devices, or other display devices. Users can take appropriate actions based on the prompts. If a tool needs replacement, the user can replace it immediately. If a tool requires maintenance or repair, the user can perform the operation according to the recommended maintenance procedures.

[0065] Reference Figure 2This application also provides a tool wear change analysis device, which includes: an acquisition module 201, a processing module 202, an input module 203, and a judgment module 204; the acquisition module 201 is used to acquire first control data and first sensor data collected by a data acquisition unit within a preset time period. The first control data includes the spindle transfer speed, cutting speed, and cutting depth of the machine tool, and the first sensor data includes temperature, sound intensity, and current data; the processing module 202 is used to process the first control data and the first sensor data to obtain second control data and second sensor data; the input module 203 is used to input the second control data and the second sensor data into a preset tool wear prediction model to obtain the wear amount corresponding to the second control data and the second sensor data. The preset tool wear prediction model includes the correspondence between the second control data, the second sensor data, and the wear amount; the judgment module 204 is also used to determine that the tool wear degree is severe if the wear amount is greater than or equal to a preset first wear amount threshold. The wear degree includes one of normal, slight wear, and severe wear.

[0066] In one possible implementation, the processing module 202 preprocesses the first control data and the first sensor data to obtain the second control data. Specifically, the preprocessing of the first control data to obtain the second control data includes: the processing module 202 performing data conversion on the first control data, which includes converting the first spindle speed of the machine tool into frictional force and converting the first cutting speed and the first cutting depth of the machine tool into cutting force. The first spindle speed is any one of a plurality of spindle speeds, the first cutting speed is any one of a plurality of cutting speeds, and the first cutting depth is any one of a plurality of cutting depths; the processing module 202 assigns weights to the frictional force and the cutting force and performs a weighted summation of the frictional force and the cutting force to obtain the second control data.

[0067] In one possible implementation, the processing module 202 preprocesses the first control data and the first sensor data to obtain the second control data and the second sensor data. Specifically, the preprocessing of the first sensor data to obtain the second sensor data includes: the acquisition module 201 acquiring first temperature data, first sound intensity, and first current data, wherein the first temperature data is a temperature data that is greater than the second temperature data among multiple temperature data, the second temperature data is any temperature data other than the first temperature data among multiple temperature data, the first sound intensity is a sound intensity that is greater than the second sound intensity among multiple sound intensities, the second sound intensity is any sound intensity other than the first sound intensity among multiple sound intensities, and the first current data is the average value of multiple current data; the processing module 202 uses a weighted summation method to obtain the second sensor data from the first temperature data, the first sound intensity, and the first current data.

[0068] In one possible implementation, before the acquisition module 201 acquires the first control data and the first sensor data acquired by the data acquisition unit, the method further includes: the acquisition module 201 acquiring a training sample library, the training sample library including multiple samples, the multiple samples including historical control data, historical sensor data and wear amount corresponding to the historical time period; and the input module 203 inputting the training sample library into a preset tool wear amount prediction model for training.

[0069] In one possible implementation, after obtaining the wear amount corresponding to the second control data and the second sensor data, the method further includes: if the wear amount is greater than or equal to a preset second wear amount threshold and the wear amount is less than a preset first wear amount threshold, then the processing module 202 determines that the wear degree of the tool is slight wear, and the first wear amount threshold is greater than the second wear amount threshold; the acquisition module 201 acquires the condition information of the cut finished product, and performs a similarity judgment between the condition information of the cut finished product and preset condition information, the condition information including the length, straightness and geometry of the cut finished product; if the similarity is greater than a preset similarity threshold, then the processing module 202 adjusts the spindle speed of the machine tool to a preset minimum spindle feed speed and adjusts the cutting speed of the tool to a preset minimum tool cutting speed.

[0070] In one possible implementation, after the processing module 202 obtains the wear amount corresponding to the second control data and the second sensor data, the method further includes: if the wear amount is less than a preset second wear amount threshold, the processing module 202 determines that the wear degree of the tool is in a normal state; the acquisition module 201 acquires the first control data and the first sensor data corresponding to the machine tool when it is in a normal state; and the processing module 202 saves the first control data and the first sensor data to the training sample library.

[0071] In one possible implementation, if the wear amount is greater than or equal to a preset first wear threshold, after the processing module 202 determines that the wear degree of the tool is severe, the method further includes: the processing module 202 displays a prompt message, which is used to prompt the user to repair or replace the tool.

[0072] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0073] This application also discloses an electronic device. (See reference...) Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0074] The communication bus 302 is used to enable communication between these components.

[0075] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0076] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0077] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 305, and by calling data stored in memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0078] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. (Refer to...) Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for analyzing tool wear variation.

[0079] exist Figure 3 In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and acquire user input data; while the processor 301 can be used to call an application program stored in the memory 305 for analyzing tool wear variation. When executed by one or more processors 301, the electronic device 300 performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0080] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0081] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0082] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0083] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0084] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0085] The above description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and the disclosure of practical truths.

[0086] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for analyzing the variation of tool wear, characterized in that, The method includes: Within a preset time period, first control data and first sensor data collected by the data acquisition unit are acquired. The first control data includes multiple spindle transfer speeds, multiple cutting speeds, and multiple cutting depths of the machine tool. The first sensor data includes multiple temperature data, multiple sound intensities, and multiple current data. The first control data and the first sensor data are preprocessed to obtain the second control data and the second sensor data. The second control data and the second sensor data are input into a preset tool wear prediction model to obtain the wear amount corresponding to the second control data and the second sensor data. The preset tool wear prediction model includes the second control data, the second sensor data, and the correspondence between the wear amount. If the wear amount is greater than or equal to a preset first wear threshold, then the wear degree of the tool is determined to be severe wear, and the wear degree includes one of normal, slight wear and severe wear; The step of preprocessing the first control data and the first sensor data to obtain the second control data and the second sensor data specifically includes: The first control data is converted into data, which includes converting the first spindle speed of the machine tool into frictional force and converting the first cutting speed and the first cutting depth of the machine tool into cutting force. The first spindle speed is any one of a plurality of spindle speeds, the first cutting speed is any one of a plurality of cutting speeds, and the first cutting depth is any one of a plurality of cutting depths. The friction force and the cutting force are weighted and then summed to obtain the second control data.

2. The method according to claim 1, wherein the preprocessing of the first control data and the first sensor data to obtain the second control data and the second sensor data, specifically includes preprocessing the first sensor data to obtain the second sensor data, comprising: Acquire first temperature data, first sound intensity, and first current data, wherein the first temperature data is a temperature data that is greater than a second temperature data among a plurality of temperature data, the second temperature data is any temperature data other than the first temperature data among a plurality of temperature data, the first sound intensity is a sound intensity that is greater than a second sound intensity among a plurality of sound intensities, the second sound intensity is any sound intensity other than the first sound intensity among a plurality of sound intensities, and the first current data is the average value of a plurality of current data; The second sensor data is obtained by weighting and summing the first temperature data, the first sound intensity, and the first current data.

3. The method according to claim 1, characterized in that, Before acquiring the first control data and the first sensor data acquired by the data acquisition unit, the method further includes: Obtain a training sample library, which includes multiple samples, including historical control data, historical sensor data, and wear amount corresponding to the historical time period. The training sample library is input into the preset tool wear prediction model for training.

4. The method according to claim 1, characterized in that, After obtaining the wear amount corresponding to the second control data and the second sensor data, the method further includes: If the wear amount is greater than or equal to a preset second wear threshold and the wear amount is less than a preset first wear threshold, then the wear degree of the tool is determined to be slight wear, and the first wear threshold is greater than the second wear threshold. Obtain the condition information of the cut finished product, and perform a similarity judgment between the condition information of the cut finished product and the preset condition information. The condition information includes the length, straightness and geometric shape of the cut finished product. If the similarity is greater than a preset similarity threshold, the spindle speed of the machine tool is adjusted to a preset minimum spindle feed speed and the cutting speed of the tool is adjusted to a preset minimum tool cutting speed.

5. The method according to claim 1, characterized in that, After obtaining the wear amount corresponding to the second control data and the second sensor data, the method further includes: If the wear amount is less than the preset second wear threshold, then the wear degree of the tool is determined to be normal. When the normal state is obtained, the first control data and the first sensor data corresponding to the machine tool; Save the first control data and the first sensor data to the training sample library.

6. The method according to claim 1, characterized in that, If the wear amount is greater than or equal to a preset first wear threshold, and the tool is determined to be severely worn, the method further includes: The system displays a prompt message to remind the user to repair or replace the tool.

7. A tool wear variation analysis device, characterized in that, The apparatus is used to perform the method as described in any one of claims 1-6, the apparatus comprising: an acquisition module (201), a processing module (202), an input module (203), and a judgment module (204); The acquisition module (201) is used to acquire first control data and first sensor data acquired by the data acquisition unit within a preset time period. The first control data includes the spindle transfer speed, cutting speed and cutting depth of the machine tool. The first sensor data includes temperature, sound intensity and current data. The processing module (202) is used to process the first control data and the first sensor data to obtain the second control data and the second sensor data. The input module (203) is used to input the second control data and the second sensor data into a preset tool wear prediction model to obtain the wear amount corresponding to the second control data and the second sensor data; The judgment module (204) is used to determine that the wear degree of the tool is severe wear if the wear amount is greater than or equal to a preset first wear amount threshold. The wear degree includes one of normal wear, slight wear and severe wear.

8. An electronic device, characterized in that, The device includes a processor (301), a memory (305), a user interface (303), and a network interface (304). The memory (305) is used to store instructions. The user interface (303) and the network interface (304) are used to communicate with other devices. The processor (301) is used to execute the instructions stored in the memory (305) to cause the electronic device (300) to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the steps of the method as described in any one of claims 1-6.

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