Cutter wear monitoring and repairing method based on artificial intelligence

Abstract

The invention discloses a tool wear monitoring and repairing method based on artificial intelligence. The method comprises the following steps that a terminal layer collects original machining data and uploads the original machining data to a cloud computing layer; the cloud computing layer trains a power increment diagnosis model; the terminal layer collects processing data in the processing process in real time as monitoring data; the cloud computing layer selects a deep learning algorithm or a transfer learning algorithm to train a final processing precision fault diagnosis model; constructing an edge calculation distribution optimization algorithm and performing task scheduling; and based on the machining precision fault diagnosis result, a laser surface strengthening path is constructed, and the abraded tool is repaired through laser surface strengthening treatment. The numerical control machining process can be diagnosed in real time based on the transfer learning mechanism, the laser surface strengthening path is planned based on the diagnosis result, the laser surface strengthening operation is guided in real time, the abnormal state of the tool is repaired in time through the laser surface strengthening technology, and the machining precision is ensured.

Description

Technical Field

[0001] The invention relates to the technical field of CNC machine tool processing, and in particular to a tool wear monitoring and repair method based on artificial intelligence. Background Art

[0002] As the core equipment of intelligent manufacturing, CNC machine tools play a vital role in modern manufacturing. According to statistics, there are more than 10 million CNC machine tools in my country. Realizing high-end green CNC machining driven by digital twins is an important goal for my country to achieve modernization and intelligence in manufacturing. How to maximize the energy efficiency of high-end machining is an important research direction. To achieve this goal, the Ministry of Industry and Information Technology, the National Development and Reform Commission and other eight departments jointly issued the "14th Five-Year Plan for Intelligent Manufacturing Development" in December 2021 to promote the application of intelligent manufacturing technologies related to digital twins, such as big data, artificial intelligence and edge computing in manufacturing to achieve high-quality and efficient planning and scheduling. In August 2022, the Ministry of Industry and Information Technology proposed to further promote the development of advanced manufacturing to achieve the strategic goal of revitalizing industry. Promoting the reconstruction of industrial foundations and enhancing the scientific and technological innovation capabilities of the industry are important development goals. In the context of interdisciplinary studies, the use of emerging technologies such as intelligent sensing measurement systems, big data analysis, deep learning algorithms, data visualization, edge computing, etc. based on digital twins, combined with traditional mechanical processing mechanism models, can effectively optimize processing energy consumption, efficiency and equipment utilization.

[0003] my country produces more than 450 million tons of waste every year, including a large number of spare parts and short-life cutting tools. The scrapping of these resources is a huge economic loss. Reducing the waste of these resources can significantly reduce the use of new materials and reduce carbon emissions.

[0004] In the field of CNC machine tools, current big data collection methods and deep learning algorithms have achieved a series of research results in fault diagnosis, processing parameter optimization and processing scheduling optimization. However, the main problems are: CNC machining precision faults cannot fully utilize the fault mechanism to achieve accurate diagnosis, the processing data acquisition rate and training efficiency are low, and the computing architecture efficiency is low; there is a lack of in-depth research on the relationship between tool wear and machining precision faults, and only the tool status (running-in, steady state, failure) can be generally determined. It is difficult to effectively diagnose the impact of the tool on machining precision faults during actual use, and it is impossible to promptly and effectively process and respond based on the machining precision fault diagnosis results.

[0005] In view of this, how to provide strong support for data analysis based on fault mechanisms, how to effectively monitor and diagnose tool wear during CNC machining, how to effectively process based on diagnostic results, and how to improve the analysis efficiency of CNC machining diagnosis have important application value and strategic significance. Summary of the invention

[0006] The purpose of the present invention is to provide a tool wear monitoring and repair method based on artificial intelligence to solve the above problems.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] A tool wear monitoring and repair method based on artificial intelligence, the method is implemented based on an edge computing system, the edge computing system includes a cloud computing layer, an edge computing layer, a terminal layer, a task allocation computing unit and a task allocation optimization unit, the cloud computing layer is used to train a power increment diagnosis model, the edge computing layer is used to retrain the power increment diagnosis model for new processing conditions, the terminal layer includes a CNC machine tool, a working condition parameter acquisition module, a power data acquisition module and an image acquisition module, the working condition parameter acquisition module is used to collect cutting processing parameters and workpiece types, the power data acquisition module is used to collect cutting processing power in real time, and the image acquisition module is used to collect cutting tool images and workpiece surface quality images;

[0009] The method comprises the following steps:

[0010] S1. Collecting original processing data under the same processing condition through the terminal layer and uploading it to the cloud computing layer, wherein the original processing data at least includes processing power, processing parameters, cutting tool image and workpiece surface quality image, and the processing condition at least includes cutting processing parameters and workpiece type;

[0011] S2. Train a power increment diagnosis model through the cloud computing layer, wherein the input of the power increment diagnosis model is the processing power and the cutting tool wear, and the output is the cutting processing accuracy fault diagnosis result, and use the original processing data and the cutting processing accuracy fault diagnosis result as source domain data, and send the source domain data and the power increment diagnosis model to the edge computing layer;

[0012] S3, the terminal layer collects the processing data in real time as monitoring data during the processing, and transmits it to the edge computing layer through the MQTT protocol, and then transmits the processing data to the cloud computing layer through the MQTT protocol;

[0013] S4. The cloud computing layer determines whether the processing conditions have changed based on the monitoring data. If not, the power increment diagnosis model is trained by the edge computing layer based on the deep learning algorithm to obtain the final processing accuracy fault diagnosis model. If so, the monitoring data is used as the target domain data, and a deep learning algorithm model is constructed based on the transfer learning algorithm. The deep learning algorithm model is trained by the edge computing layer to obtain the final processing accuracy fault diagnosis model. The processing accuracy fault diagnosis model is implemented by combining a long short-term memory neural network with a convolutional neural network.

[0014] S5. Utilize the task allocation calculation unit to count the data transmission volume, edge computing processing time, and edge layer computing utilization rate, wherein the data transmission volume is the total amount of data transmitted from the edge computing layer to the cloud computing layer, the edge computing processing time is the time required for data transmission and algorithm processing under a specific network environment, and the edge layer computing utilization rate is the utilization efficiency of the edge computing layer; utilize the task allocation optimization unit to construct an edge computing allocation optimization algorithm, and utilize the fruit fly algorithm to solve the optimal task allocation method and perform task scheduling;

[0015] S6. Use the machining accuracy fault diagnosis model to perform real-time diagnosis of the CNC machining process. For machining accuracy faults, obtain the corresponding tool image, obtain the tool wear area based on the tool image, and perform laser surface strengthening treatment on the tool wear area.

[0016] Preferably, the fruit fly algorithm is implemented by the following method:

[0017] The data transmission volume, edge computing processing time, and edge layer computing utilization are normalized and used as optimization targets.

[0018] Initialize the number of fruit fly swarms and randomly initialize the positions of the fruit fly swarms;

[0019] Define the fitness function based on the optimization objective:

[0020] F = argmin(e·Q tmn +f·T op +h·D op )

[0021] Among them, F is the fitness function, Q tmn is the normalized value of data transmission volume, T op is the normalized value of edge computing processing time, D op is the normalized value of edge layer computing utilization;

[0022] Based on the smell search method, the crossover and mutation process is introduced to calculate the fitness function value F;

[0023] Construct an iterative mechanism for visual search. If the fitness function value of an individual fruit fly is better than the fitness function value of the center of the fruit fly group, the position of the individual fruit fly is used as the new search center.

[0024] Through iterative optimization, the individual fruit flies with the best fitness function value are determined as the optimal task allocation method.

[0025] Preferably, the step S4 of training the deep learning algorithm model using the edge computing layer is specifically implemented by the following method: analyzing the difference in feature distribution between source domain data and target domain data, introducing alignment weights for data alignment to achieve cross-domain data transfer; using the Tensorflow deep learning architecture to build a deep learning algorithm for forward computing training; using parameter transfer learning and retaining most of the training parameters, performing back propagation calculations to optimize feature distribution, using the maximum average difference to measure the difference in probability distribution between the source domain and the target domain, combining deep learning training cross entropy to characterize the training error results, and using regularized maximum average difference and cross entropy to construct the optimization target of the algorithm; after training, the final machining accuracy fault diagnosis model is obtained for real-time diagnosis during the machining process.

[0026] Preferably, the machining accuracy fault diagnosis model structure in step S4 includes a convolution layer, a pooling layer, an LSTM layer and a fully connected layer, the convolution layer and the pooling layer are used for feature extraction, the LSTM layer is used to process time series data, and the fully connected layer is used for final diagnosis result classification.

[0027] Preferably, the forward computation training is implemented by the following method:

[0028] Randomly initialize the model's weight matrix and bias parameters;

[0029] Input source domain data and target domain data to each layer of the neural network, and output the diagnosis result. The diagnosis result is calculated by the following formula:

[0030]

[0031] Among them, y is the output diagnosis result, x is the input source domain data and target domain data, b is the bias parameter, is the activation function, which uses one of the ReLU function, PReLU function, Softmax function, and Swish function;

[0032] The loss function is used to calculate the error between the output diagnosis result and the true result.

[0033] Preferably, the back propagation calculation is implemented by the following method:

[0034] Calculate the gradient of the loss function with respect to each model parameter. Based on the obtained gradient of each model parameter, use the gradient descent algorithm to update the model parameters of the neural network. Specifically:

[0035] Select an initial point θ0, define θ as the model parameter, and θ0 as the initialization parameter;

[0036] Choose a loss function L(θ) that measures the difference between the predictions given the model parameters θ and the actual observations;

[0037] Determine an appropriate learning rate η, which controls the step size of updating the parameters at each iteration;

[0038] Iterative update, for each training sample i, calculate the gradient of the loss function with respect to θ:

[0039]

[0040] Among them, g i is the gradient, x i is the input processing data, y i It is the corresponding machining accuracy fault diagnosis result.

[0041] Preferably, step S2 is specifically implemented by the following method:

[0042] S21. The cutting tool processing life cycle is divided into three intervals: the running-in period, the steady-state period, and the failure period through the wear curve of the cutting tool. For the running-in period and the steady-state period, a tool wear and tool cutting force correlation model is established. The tool wear and tool cutting force correlation model is:

[0043]

[0044] Where i is the i-th tool wear state in the cutting tool processing life cycle, A i is the wear amount of the i-th tool under wear state, which is obtained through image analysis, F i is the cutting force corresponding to the i-th tool wear data, F0 is the initial cutting force of the cutting tool, a and b are constants determined based on the wear curve of the cutting tool;

[0045] S22. For the workpiece surface quality image, a grayscale image is obtained by grayscale processing, and the fractal dimension of the grayscale surface of the grayscale image is calculated. The grayscale plane of the image is covered by boxes of different sizes, and the number of boxes required to cover the entire grayscale surface is calculated. Then, the fractal dimension is obtained by least square fitting, and the obtained fractal dimension is normalized to obtain the surface roughness of the workpiece, and the corresponding processing data with a surface roughness greater than a preset threshold value σ is eliminated;

[0046] S23. Use Matlab software to estimate the cutting force during the machining life cycle, analyze the nonlinear relationship between cutting tool wear and cutting force increment, and define the tool aging coefficient K. cutter , K cutter The solution model is:

[0047]

[0048] Among them, K cutter is the tool aging coefficient, c and d are model parameters;

[0049] S24. Based on the original processing data, establish and train the tool wear power increment model:

[0050] ΔP ik =P i ·i(K cutter )

[0051]

[0052] Among them, ΔP ik is the machining power increment between the kth tool wear state and the ith tool wear state, P i is the machining power under the i-th tool wear state, f(K cutter ) is a function of the tool aging coefficient, g, h, and n are model parameters;

[0053] Obtaining machining data of other different working conditions, verifying the tool wear power increment model, and optimizing model parameters;

[0054] A power increment diagnosis model is constructed based on the tool wear power increment model. The input of the power increment diagnosis model is the machining power and the cutting tool wear amount, and the output is the cutting machining accuracy fault diagnosis result.

[0055] Preferably, the wear amount of the tool under the wear state is obtained by the following method:

[0056] Acquire a cutting tool image, wherein the cutting tool image is obtained by photographing a CCD camera installed at a corresponding position of a CNC machine tool; extract tool feature information using an image feature extraction method, wherein the tool feature information includes tool contour features, tool surface texture features, tool wear area texture features, and tool wear area edge features; based on the tool feature information, obtain tool geometry information and position and geometry information of the wear area; based on the tool geometry information and position and geometry information of the wear area, calculate the area of ​​the wear area and the depth of the wear area, and then calculate the volume of the wear area as the wear amount.

[0057] Preferably, the method further comprises: after establishing the tool wear power increment model, optimizing the tool wear power increment model using an optimization function, wherein the optimized tool wear power increment model is expressed as:

[0058] ΔP final =P i ·f(K cutter )·δ(K cutter ,R i )

[0059]

[0060] Where ΔP final The power increment result after optimization, δ(K cutter , R i ) is the optimization function, R i is the surface roughness of the workpiece processed under the i-th tool wear state, β is a constant, 0<β<0.5.

[0061] Preferably, the step S6 comprises the following sub-steps:

[0062] S61, using the machining accuracy fault diagnosis model to perform real-time diagnosis on the CNC machining process, and judging whether a machining accuracy fault occurs based on the power increment change, if so, executing step S62;

[0063] S62, acquiring a corresponding tool image, and obtaining tool contour geometry information and tool wear area geometry information by image feature extraction;

[0064] S63, constructing a three-dimensional model of the tool based on the tool profile geometry information and the tool wear area geometry information;

[0065] S64, based on the geometric information of the tool wear area, dividing the tool wear area into a group of equivalent planes, and setting a sawtooth laser scanning path on each equivalent plane to form a plurality of trajectory points;

[0066] S64, constructing a laser surface strengthening path based on all trajectory points on each equivalent plane;

[0067] S66, establishing tool space coordinates based on tool contour geometry information, determining wear area space coordinates based on tool space coordinates and tool wear area geometry information, performing coordinate conversion on the wear area space coordinates to a robot working coordinate system, converting the laser surface strengthening path constructed in step S64 to a robot working coordinate system, wherein the robot working coordinate system is a working coordinate system jointly determined by the robot's manipulator and the laser;

[0068] S67. Based on the laser surface strengthening path, the robot controls the laser to perform laser surface strengthening on the tool wear area to achieve tool repair.

[0069] After adopting the above technical solution, the present invention has the following beneficial effects compared with the background technology:

[0070] 1. Based on the precision failure mechanism of CNC machining process, the present invention constructs and trains a power increment diagnosis model, and then uses the transfer learning algorithm to realize the efficient transfer of knowledge between different machining conditions, reuses the labeled data of the source domain, constructs an algorithm to migrate old data from the source domain to the target domain, and reuses the training parameters of the deep learning algorithm in the original machining conditions, which can effectively improve its training accuracy and efficiency.

[0071] 2. The present invention obtains machining accuracy fault diagnosis results by performing real-time CNC machining process diagnosis, and plans laser surface strengthening paths to promptly and effectively repair worn tools. On the one hand, it ensures the machining accuracy of CNC machining, and on the other hand, it also improves the service life and life of the tools.

[0072] 3. The present invention adopts laser surface strengthening technology to repair abnormal status of the tool in time. Based on the laser scanning path of the tool wear image and converting it into the working coordinate system of the laser operation, it can guide the laser surface strengthening operation in real time.

[0073] 4. The present invention improves the computational efficiency in the diagnosis process of CNC machining processes by constructing an edge computing allocation optimization algorithm and using the fruit fly algorithm to solve the optimal task allocation method and perform task scheduling.

[0074] 5. The present invention constructs a tool wear power increment model, fully considers the impact of tool wear on machining power, identifies abnormal energy patterns in which tool states undergo significant changes during machining based on changes in machining power increments, performs a rational diagnosis of the CNC machining process, and improves the accuracy and robustness of data diagnosis.

[0075] Instruction Manual

[0076] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0077] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0078] Example

[0079] The technical idea of ​​the present invention is based on digital twin technology. The application of digital twin technology in the field of CNC machine tools can simulate the operating status and processing of machine tools in a virtual environment by creating an accurate digital copy of the physical machine tool, so that the performance, efficiency and potential problems of the machine tool can be comprehensively predicted and analyzed. Emerging technologies such as intelligent sensor measurement systems, big data analysis, deep learning algorithms, data preprocessing, data visualization, edge computing, and processing compensation technology based on digital twins, combined with traditional mechanical processing mechanism models, can effectively realize effective diagnosis, analysis and intervention of CNC machining.

[0080] Deep learning is a subset of machine learning that is based on artificial neural networks. The learning process is deep because of the structure of artificial neural networks, which consists of multiple input, output, and hidden layers. Each layer contains units that transform input data into information that the next layer uses for a specific prediction task. Thanks to this structure, the machine can learn through its own data processing.

[0081] Transfer learning is an important concept in the field of machine learning. Its core idea is to use the knowledge learned in one task or field to accelerate or improve the learning process of another related task or field. This method is particularly effective in the case of data scarcity or domain migration, because it can significantly reduce the need for a large amount of labeled data and improve the learning efficiency and generalization ability of the model.

[0082] An embodiment of the present invention discloses a tool wear monitoring and repair method based on artificial intelligence. The method is implemented based on an edge computing system. The edge computing system includes a cloud computing layer, an edge computing layer, a terminal layer, a task allocation computing unit and a task allocation optimization unit. The cloud computing layer is used to train a power increment diagnosis model. The edge computing layer is used to retrain the power increment diagnosis model for new processing conditions. The terminal layer includes a CNC machine tool, a working condition parameter acquisition module, a power data acquisition module and an image acquisition module. The working condition parameter acquisition module is used to collect cutting processing parameters and workpiece types. The power data acquisition module is used to collect cutting processing power in real time. The image acquisition module is used to collect cutting tool images and workpiece surface quality images.

[0083] See also Figure 1 The present invention discloses a tool wear monitoring and repair method based on artificial intelligence, which is implemented by the following steps:

[0084] S1. Collect the original processing data under the same processing condition through the terminal layer and upload it to the cloud computing layer. The original processing data at least includes processing power, processing parameters, cutting tool image and workpiece surface quality image. The processing condition at least includes cutting processing parameters and workpiece type. For the cutting tool image and the workpiece surface quality image, the cutting tool and the workpiece are segmented from the image respectively through the threshold segmentation algorithm. Image threshold segmentation is based on the grayscale of the image to classify pixels. The core lies in how to select an appropriate grayscale threshold. The grayscale threshold is determined according to the grayscale histogram of the image. In this embodiment, the threshold segmentation algorithm adopts one of global threshold segmentation, adaptive threshold segmentation, Otsu algorithm, and block variable threshold segmentation. The selection of the segmentation algorithm is based on the segmentation quality and efficiency.

[0085] S2. Train the power increment diagnosis model through the cloud computing layer. The input of the power increment diagnosis model is the processing power and the cutting tool wear. The output is the cutting processing accuracy fault diagnosis result. The original processing data and the cutting processing accuracy fault diagnosis result are used as the source domain data. The source domain data and the power increment diagnosis model are sent to the edge computing layer. Step S2 is specifically implemented by the following method:

[0086] S21. Through the wear curve of the cutting tool, the cutting tool processing life cycle is divided into three intervals: running-in period, steady-state period, and failure period. For the running-in period and steady-state period, a correlation model between tool wear and tool cutting force is established. The correlation model between tool wear and tool cutting force is:

[0087]

[0088] Where i is the i-th tool wear state in the cutting tool processing life cycle, A i is the wear amount of the i-th tool under wear state, which is obtained through image analysis, F i is the cutting force corresponding to the i-th tool wear data, F0 is the initial cutting force of the cutting tool, and a and b are constants determined based on the wear curve of the cutting tool. In this embodiment, the wear amount of the tool under the wear state is obtained by the following method:

[0089] The cutting tool image is obtained by photographing a CCD camera installed at the corresponding position of the CNC machine tool; the tool feature information is extracted using an image feature extraction method, and the tool feature information includes tool contour features, tool surface texture features, tool wear area texture features, and tool wear area edge features; based on the tool feature information, the tool geometry information and the position and geometry information of the wear area are obtained; based on the tool geometry information and the position and geometry information of the wear area, the area and depth of the wear area are calculated, and then the volume of the wear area is calculated as the wear amount.

[0090] S22. For the workpiece surface quality image, a grayscale image is obtained through grayscale processing, and the fractal dimension of the grayscale surface of the grayscale image is calculated. The grayscale plane of the image is covered by boxes of different sizes, and the number of boxes required to cover the entire grayscale surface is calculated. Then, the fractal dimension is obtained by least squares fitting. The obtained fractal dimension is normalized to obtain the surface roughness of the workpiece, and the corresponding processing data with surface roughness greater than the preset threshold σ is eliminated.

[0091] S23. Use Matlab software to estimate the cutting force during the machining life cycle, analyze the nonlinear relationship between cutting tool wear and cutting force increment, and define the tool aging coefficient K. cutter , K cutter The solution model is:

[0092]

[0093] Among them, K cutter is the tool aging coefficient, c and d are model parameters.

[0094] S24. Based on the original processing data, establish and train the tool wear power increment model:

[0095] ΔP ik =P i ·f(K cutter )

[0096]

[0097] Where ΔP ik is the machining power increment between the kth tool wear state and the ith tool wear state, P i is the machining power under the i-th tool wear state, f(K cutter ) is a function of the tool aging coefficient, g, h, and n are model parameters;

[0098] After establishing the tool wear power increment model, the optimization function is used to optimize the tool wear power increment model. The optimized tool wear power increment model is expressed as:

[0099] ΔP final =P i ·f(K cutter )·δ(K cutter ,R i )

[0100]

[0101] Where ΔP final The power increment result after optimization, δ(K cutter , R i) is the optimization function, R i is the surface roughness of the workpiece processed under the i-th tool wear state, β is a constant, 0<β<0.5. The tool wear power increment model is optimized using the surface roughness index, which further improves the accuracy and robustness of data diagnosis;

[0102] Obtain machining data of other different working conditions, verify the tool wear power increment model, and optimize the model parameters.

[0103] S25. Construct a power increment diagnosis model based on the tool wear power increment model, wherein the input of the power increment diagnosis model is the machining power and the amount of cutting tool wear, and the output is the cutting machining accuracy fault diagnosis result. In this way, the power increment diagnosis model obtained, under the premise of fully considering the influence of tool wear on machining power, identifies the abnormal energy mode in which the tool state undergoes a significant change during the machining process based on the machining power increment change, performs a reasonable diagnosis of the CNC machining process, and improves the accuracy and robustness of data diagnosis.

[0104] S3, the terminal layer collects the processing data in the processing process in real time as monitoring data, and transmits it to the edge computing layer through the MQTT protocol, and then transmits the processing data to the cloud computing layer through the MQTT protocol. It should be understood by those skilled in the art that the cloud computing layer, edge computing layer, and terminal layer involved in the present invention are all deployed with network communication equipment, and the network communication equipment is used to transmit data based on the MQTT protocol. The MQTT protocol is a message protocol based on the publish / subscribe paradigm under the ISO standard (ISO / IEC PRF 20922).

[0105] S4. The cloud computing layer determines whether the processing conditions have changed based on the monitoring data. If not, the power increment diagnosis model is trained using the edge computing layer based on the deep learning algorithm to obtain the final processing accuracy fault diagnosis model. If so, the monitoring data is used as the target domain data, and a deep learning algorithm model is constructed based on the transfer learning algorithm. The deep learning algorithm model is trained using the edge computing layer to obtain the final processing accuracy fault diagnosis model. The processing accuracy fault diagnosis model is implemented using a long short-term memory neural network combined with a convolutional neural network, which includes a convolutional layer, a pooling layer, an LSTM layer, and a fully connected layer. The convolutional layer and the pooling layer are used for feature extraction, the LSTM layer is used to process time series data, and the fully connected layer is used for the final diagnosis result classification. Among them, the convolution layer is the core of the convolutional neural network (CNN), which is used to extract local features in the image; the pooling layer is used to reduce the spatial size of the feature map, thereby reducing the number of parameters and the amount of calculation. It achieves downsampling by applying the maximum operation in a certain area, which helps to improve the invariance of feature detection; LSTM controls the flow of information by introducing a gating mechanism (input gate, forget gate, and output gate), enabling it to learn long-term dependencies; each node of the fully connected layer is connected to all the activation values ​​of the previous layer. The fully connected layer is located at the end of the network and is used to integrate the features transferred from the convolution layer and the pooling layer for final classification or regression.

[0106] In this step, the edge computing layer is used to train the deep learning algorithm model, which is specifically achieved through the following methods: analyzing the difference in feature distribution between source domain data and target domain data, introducing alignment weights for data alignment to achieve cross-domain data transfer; using the Tensorflow deep learning architecture to build a deep learning algorithm for forward computing training; using parameter transfer learning and retaining most of the training parameters, performing back propagation calculations to optimize feature distribution, using the maximum average difference to measure the difference in probability distribution between the source domain and the target domain, combining deep learning training cross entropy to characterize the training error results, and using regularized maximum average difference and cross entropy to construct the optimization target of the algorithm; after training, the final machining accuracy fault diagnosis model is obtained for real-time diagnosis during the machining process.

[0107] The above forward calculation training is achieved by the following method:

[0108] Randomly initialize the model's weight matrix and bias parameters;

[0109] Input source domain data and target domain data to each layer of the neural network, and output the diagnosis result. The diagnosis result is calculated by the following formula:

[0110]

[0111] Among them, y is the output diagnosis result, x is the input source domain data and target domain data, b is the bias parameter, is the activation function, which uses one of the ReLU function, PReLU function, Softmax function, and Swish function;

[0112] The loss function is used to calculate the error between the output diagnosis result and the true result.

[0113] The above back propagation calculation is implemented by the following method:

[0114] Calculate the gradient of the loss function with respect to each model parameter. Based on the obtained gradient of each model parameter, use the gradient descent algorithm to update the model parameters of the neural network. Specifically:

[0115] Select an initial point θ0, define θ as the model parameter, and θ0 as the initialization parameter;

[0116] Choose a loss function L(θ) that measures the difference between the predictions given the model parameters θ and the actual observations;

[0117] Determine an appropriate learning rate η, which controls the step size of updating the parameters at each iteration;

[0118] Iterative update, for each training sample i, calculate the gradient of the loss function with respect to θ:

[0119]

[0120] Among them, g i is the gradient, x i is the input processing data, y i It is the corresponding machining accuracy fault diagnosis result.

[0121] S5. Use the task allocation computing unit to calculate the data transmission volume, edge computing processing time, and edge layer computing utilization rate. The data transmission volume is the total amount of data transmitted from the edge computing layer to the cloud computing layer. The edge computing processing time is the time required for data transmission and algorithm processing under a specific network environment. The edge layer computing utilization rate is the utilization efficiency of the edge computing layer. The data transmission volume of this embodiment represents the data transmission cost, and the edge computing processing time reflects the algorithm complexity to a certain extent. The edge layer computing utilization rate can be derived by comprehensively deriving the algorithm complexity and the computing power of the device.

[0122] The task allocation optimization unit is used to build an edge computing allocation optimization algorithm, and the fruit fly algorithm is used to solve the best task allocation method and perform task scheduling. The fruit fly algorithm is implemented in the following ways:

[0123] Normalize the data transmission volume, edge computing processing time, and edge layer computing utilization rate and use them as optimization targets;

[0124] Initialize the number of fruit fly swarms and randomly initialize the positions of the fruit fly swarms;

[0125] Define the fitness function based on the optimization objective:

[0126] F = argmin(e·Q tmn +f·T op +h·D op )

[0127] Among them, F is the fitness function, Q tmn is the normalized value of data transmission volume, T op is the normalized value of edge computing processing time, D op is the normalized value of edge layer computing utilization;

[0128] Based on the smell search method, the crossover and mutation process is introduced to calculate the fitness function value F;

[0129] Construct an iterative mechanism for visual search. If the fitness function value of an individual fruit fly is better than the fitness function value of the center of the fruit fly group, the position of the individual fruit fly is used as the new search center.

[0130] Through iterative optimization, the individual fruit flies with the best fitness function value are determined as the optimal task allocation method.

[0131] S6. Use the machining accuracy fault diagnosis model to perform real-time diagnosis on the CNC machining process. For machining accuracy faults, obtain the corresponding tool image, obtain the tool wear area based on the tool image, and perform laser surface strengthening treatment on the tool wear area. This step is specifically as follows:

[0132] S61. Perform real-time diagnosis on the CNC machining process using a machining accuracy fault diagnosis model, and determine whether a machining accuracy fault occurs based on the power increment change. If so, execute step S62.

[0133] S62, acquiring a corresponding tool image, and obtaining tool contour geometry information and tool wear area geometry information by extracting image features.

[0134] S63. Construct a three-dimensional model of the tool based on the tool contour geometry information and the tool wear area geometry information.

[0135] S64. Based on the geometric information of the tool wear area, the tool wear area is divided into a group of equivalent planes, and a serrated laser scanning path is set on each equivalent plane to form multiple trajectory points. Each equivalent plane can be defined as a rectangle, trapezoid, etc. Taking a rectangle as an example, the setting of the trajectory points starts from a vertex of the rectangle, and the trajectory points are set along the long side. Then, according to the area of ​​the rectangle, a group of reciprocating, coherent, and uniform trajectory points are formed in the rectangle. Since the tool wear area is not an ideal plane, the equivalent planes are not on the same plane. Each equivalent plane has a slope feature, which is used to guide the angle of the robot manipulator.

[0136] S64. Based on all trajectory points on each equivalent plane, a laser surface strengthening path is constructed.

[0137] S66. Establish tool space coordinates based on tool contour geometry information. Based on tool space coordinates, determine wear area space coordinates based on tool wear area geometry information. Perform coordinate transformation on wear area space coordinates to robot working coordinate system. Transform the laser surface strengthening path constructed in step S64 to the robot working coordinate system, which is a working coordinate system jointly determined by the robot's manipulator and laser.

[0138] S67. Based on the laser surface strengthening path, the robot controls the laser to perform laser surface strengthening on the tool wear area to achieve tool repair.

[0139] Those skilled in the art should understand that laser surface strengthening technology is a heat treatment technology that uses laser to heat and then cool. It can achieve phase change strengthening (i.e., laser shock strengthening) on ​​the tool surface by irradiating the laser spot, thereby achieving a repair effect. It can also repair the worn parts of the tool by laser directional energy deposition.

[0140] From the above description, it can be seen that the present invention plans the laser surface strengthening path based on the machining accuracy fault diagnosis result, and repairs the worn tool in a timely and effective manner. On the one hand, it ensures the machining accuracy of CNC machining, and on the other hand, it also improves the service life and life of the tool. The laser surface strengthening technology is used to repair the abnormal state of the tool in time. Based on the laser scanning path of the tool wear image and converted to the working coordinate system of the laser operation, the laser surface strengthening operation can be guided in real time. The present invention is based on the precision fault mechanism of the CNC machining process, constructs and trains the power increment diagnosis model, and then uses the transfer learning algorithm to realize the efficient transfer of knowledge between different machining conditions, reuses the labeled data of the source domain, constructs an algorithm to migrate old data from the source domain to the target domain, and reuses the training parameters of the deep learning algorithm in the original machining conditions, which can effectively improve its training accuracy and efficiency. The present invention improves the computational efficiency in the diagnosis process of the CNC machining process by constructing an edge computing allocation optimization algorithm, using the fruit fly algorithm to solve the optimal task allocation method and perform task scheduling.

[0141] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A tool wear monitoring and repair method based on artificial intelligence, characterized in that: The method is implemented based on an edge computing system, which includes a cloud computing layer, an edge computing layer, a terminal layer, a task allocation computing unit, and a task allocation optimization unit. The cloud computing layer is used to train a power increment diagnosis model, and the edge computing layer is used to retrain the power increment diagnosis model for new processing conditions. The terminal layer includes a CNC machine tool, a working condition parameter acquisition module, a power data acquisition module, and an image acquisition module. The working condition parameter acquisition module is used to acquire cutting processing parameters and workpiece types, the power data acquisition module is used to acquire cutting processing power in real time, and the image acquisition module is used to acquire cutting tool images and workpiece surface quality images. The method comprises the following steps: S1. Collecting original processing data under the same processing condition through the terminal layer and uploading it to the cloud computing layer, wherein the original processing data at least includes processing power, processing parameters, cutting tool image and workpiece surface quality image, and the processing condition at least includes cutting processing parameters and workpiece type; S2. Train a power increment diagnosis model through the cloud computing layer, wherein the input of the power increment diagnosis model is the processing power and the cutting tool wear, and the output is the cutting processing accuracy fault diagnosis result, and use the original processing data and the cutting processing accuracy fault diagnosis result as source domain data, and send the source domain data and the power increment diagnosis model to the edge computing layer; S3, the terminal layer collects the processing data in real time as monitoring data during the processing, and transmits it to the edge computing layer through the MQTT protocol, and then transmits the processing data to the cloud computing layer through the MQTT protocol; S4. The cloud computing layer determines whether the processing conditions have changed based on the monitoring data. If not, the power increment diagnosis model is trained by the edge computing layer based on the deep learning algorithm to obtain the final processing accuracy fault diagnosis model. If so, the monitoring data is used as the target domain data, and a deep learning algorithm model is constructed based on the transfer learning algorithm. The deep learning algorithm model is trained by the edge computing layer to obtain the final processing accuracy fault diagnosis model. The processing accuracy fault diagnosis model is implemented by combining a long short-term memory neural network with a convolutional neural network. S5. Utilize the task allocation calculation unit to count the data transmission volume, edge computing processing time, and edge layer computing utilization rate, wherein the data transmission volume is the total amount of data transmitted from the edge computing layer to the cloud computing layer, the edge computing processing time is the time required for data transmission and algorithm processing under a specific network environment, and the edge layer computing utilization rate is the utilization efficiency of the edge computing layer; utilize the task allocation optimization unit to construct an edge computing allocation optimization algorithm, and utilize the fruit fly algorithm to solve the optimal task allocation method and perform task scheduling; S6. Use the machining accuracy fault diagnosis model to perform real-time diagnosis of the CNC machining process. For machining accuracy faults, obtain the corresponding tool image, obtain the tool wear area based on the tool image, and perform laser surface strengthening treatment on the tool wear area.

2. The tool wear monitoring and repair method based on artificial intelligence as claimed in claim 1, characterized in that: The fruit fly algorithm is specifically implemented by the following method: The data transmission volume, edge computing processing time, and edge layer computing utilization are normalized and used as optimization targets. Initialize the number of fruit fly swarms and randomly initialize the positions of the fruit fly swarms; Define the fitness function based on the optimization objective: F=argmin(e·Q tmn +f·T op +h·D op ) Among them, F is the fitness function, Q tmn is the normalized value of data transmission volume, T op is the normalized value of edge computing processing time, D op is the normalized value of edge layer computing utilization; Based on the smell search method, the crossover and mutation process is introduced to calculate the fitness function value F; Construct an iterative mechanism for visual search. If the fitness function value of an individual fruit fly is better than the fitness function value of the center of the fruit fly group, the position of the individual fruit fly is used as the new search center. Through iterative optimization, the individual fruit flies with the best fitness function value are determined as the optimal task allocation method.

3. A tool wear monitoring and repair method based on artificial intelligence as claimed in claim 1 or 2, characterized in that: The step S4 of training the deep learning algorithm model using the edge computing layer is specifically implemented by the following method: analyzing the feature distribution difference between the source domain data and the target domain data, introducing alignment weights to perform data alignment to achieve cross-domain data transfer; The Tensorflow deep learning architecture is used to build a deep learning algorithm for forward computing training; parameter transfer learning is used and most of the training parameters are retained, and back propagation calculations are performed to optimize feature distribution. The maximum mean difference is used to measure the difference in probability distribution between the source domain and the target domain. The cross entropy of deep learning training is combined to characterize the training error results. The regularized maximum mean difference and cross entropy are used to construct the optimization target of the algorithm; After training, the final machining accuracy fault diagnosis model is obtained, which is used for real-time diagnosis during the machining process.

4. The tool wear monitoring and repair method based on artificial intelligence as claimed in claim 3, characterized in that: The processing accuracy fault diagnosis model structure in step S4 includes a convolution layer, a pooling layer, an LSTM layer and a fully connected layer. The convolution layer and the pooling layer are used for feature extraction, the LSTM layer is used to process time series data, and the fully connected layer is used for the final diagnosis result classification.

5. The tool wear monitoring and repair method based on artificial intelligence as claimed in claim 4, characterized in that: The forward computation training is implemented by the following method: Randomly initialize the model's weight matrix and bias parameters; Input source domain data and target domain data to each layer of the neural network, and output the diagnosis result. The diagnosis result is calculated by the following formula: Among them, y is the output diagnosis result, x is the input source domain data and target domain data, b is the bias parameter, is the activation function, which uses one of the ReLU function, PReLU function, Softmax function, and Swish function; The loss function is used to calculate the error between the output diagnosis result and the true result.

6. The tool wear monitoring and repair method based on artificial intelligence as claimed in claim 5, characterized in that: The back propagation calculation is implemented by the following method: Calculate the gradient of the loss function with respect to each model parameter. Based on the obtained gradient of each model parameter, use the gradient descent algorithm to update the model parameters of the neural network. Specifically: Select an initial point θ0, define θ as the model parameter, and θ0 as the initialization parameter; Choose a loss function L(θ) that measures the difference between the predictions given the model parameters θ and the actual observations; Determine an appropriate learning rate η, which controls the step size of updating the parameters at each iteration; Iterative update, for each training sample i, calculate the gradient of the loss function with respect to θ: Among them, g i is the gradient, x i is the input processing data, y i It is the corresponding machining accuracy fault diagnosis result.

7. The tool wear monitoring and repair method based on artificial intelligence as claimed in claim 2, characterized in that: The step S2 is specifically implemented by the following method: S21. The cutting tool processing life cycle is divided into three intervals: the running-in period, the steady-state period, and the failure period through the wear curve of the cutting tool. For the running-in period and the steady-state period, a tool wear and tool cutting force correlation model is established. The tool wear and tool cutting force correlation model is: Where i is the i-th tool wear state in the cutting tool processing life cycle, A i is the wear amount of the i-th tool under wear state, which is obtained through image analysis, F i is the cutting force corresponding to the i-th tool wear data, F0 is the initial cutting force of the cutting tool, a and b are constants determined based on the wear curve of the cutting tool; S22. For the workpiece surface quality image, a grayscale image is obtained by grayscale processing, and the fractal dimension of the grayscale surface of the grayscale image is calculated. The grayscale plane of the image is covered by boxes of different sizes, and the number of boxes required to cover the entire grayscale surface is calculated. Then, the fractal dimension is obtained by least square fitting, and the obtained fractal dimension is normalized to obtain the surface roughness of the workpiece, and the corresponding processing data with a surface roughness greater than a preset threshold value σ is eliminated; S23. Use Matlab software to estimate the cutting force during the machining life cycle, analyze the nonlinear relationship between cutting tool wear and cutting force increment, and define the tool aging coefficient K. cutter , K cutter The solution model is: Among them, K cutter is the tool aging coefficient, c and d are model parameters; S24. Based on the original processing data, establish and train the tool wear power increment model: ΔP ik =P i ·f(K cutter ) Among them, ΔP ik is the machining power increment between the kth tool wear state and the ith tool wear state, P i is the machining power under the i-th tool wear state, f(K cutter ) is a function of the tool aging coefficient, g, h, and n are model parameters; Obtain processing data of other different working conditions, verify the tool wear power increment model, and optimize the model parameters. A power increment diagnosis model is constructed based on the tool wear power increment model. The input of the power increment diagnosis model is the machining power and the cutting tool wear amount, and the output is the cutting machining accuracy fault diagnosis result.

8. The tool wear monitoring and repair method based on artificial intelligence as claimed in claim 7, characterized in that: The wear amount of the tool under the wear state is specifically obtained by the following method: Acquire a cutting tool image, wherein the cutting tool image is obtained by photographing a CCD camera installed at a corresponding position of a CNC machine tool; extract tool feature information using an image feature extraction method, wherein the tool feature information includes tool contour features, tool surface texture features, tool wear area texture features, and tool wear area edge features; based on the tool feature information, obtain tool geometry information and position and geometry information of the wear area; based on the tool geometry information and position and geometry information of the wear area, calculate the area of ​​the wear area and the depth of the wear area, and then calculate the volume of the wear area as the wear amount.

9. The tool wear monitoring and repair method based on artificial intelligence as claimed in claim 8, characterized in that: Also includes: After establishing the tool wear power increment model, the optimization function is used to optimize the tool wear power increment model. The optimized tool wear power increment model is expressed as: ΔP final =P i ·f(K cutter )·δ(K cutter ,R i ) Among them, ΔP final The power increment result after optimization, δ(K cutter , R i ) is the optimization function, R i is the surface roughness of the workpiece processed under the i-th tool wear state, β is a constant, 0<β<0.

5.

10. The tool wear monitoring and repair method based on artificial intelligence as claimed in claim 8, characterized in that: The step S6 comprises the following sub-steps: S61, using the machining accuracy fault diagnosis model to perform real-time diagnosis on the CNC machining process, and judging whether a machining accuracy fault occurs based on the power increment change, if so, executing step S62; S62, acquiring a corresponding tool image, and obtaining tool contour geometry information and tool wear area geometry information by image feature extraction; S63, constructing a three-dimensional model of the tool based on the tool profile geometry information and the tool wear area geometry information; S64, based on the geometric information of the tool wear area, dividing the tool wear area into a group of equivalent planes, and setting a sawtooth laser scanning path on each equivalent plane to form a plurality of trajectory points; S64, constructing a laser surface strengthening path based on all trajectory points on each equivalent plane; S66, establishing tool space coordinates based on tool contour geometry information, determining wear area space coordinates based on tool space coordinates and tool wear area geometry information, performing coordinate conversion on the wear area space coordinates to a robot working coordinate system, converting the laser surface strengthening path constructed in step S64 to a robot working coordinate system, wherein the robot working coordinate system is a working coordinate system jointly determined by the robot's manipulator and the laser; S67. Based on the laser surface strengthening path, the robot controls the laser to perform laser surface strengthening on the tool wear area to achieve tool repair.

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