Tool Wear Condition Research Articles

Tool Wear Monitoring Based on the Gray Wolf Optimized Variational Mode Decomposition Algorithm and Hilbert–Huang Transformation in Machining Stainless Steel

The online monitoring and prediction of tool wear are important to maintain the stability of machining processes. In most cases, the tool wear condition can be evaluated by signals such as force, sound, vibration, and temperature, which are often processed via Fourier-transform based methods, typically, the short-time Fourier transform (STFT). However, the fixed-width window function in STFT has many limitations. In this paper, a novel tool wear monitoring method based on variational mode decomposition (VMD) and Hilbert–Huang transformation (HHT) were developed to monitor the wear of carbide tools in machining stainless steel. In this method, the intrinsic mode function (IMF) was used as the fitness function, and the (K alpha) parameter sets for VMD were optimized by the gray wolf optimization (GWO). The results show that the characteristic frequency in the GWO-VMD-HHT method is more significant with no aliasing compared with the EMD-HHT method, and an obvious characteristic frequency shift phenomenon is present. By utilizing the energy value of IMF3 as the feature to classify the wear state of the cutting tool, the increase of energy reached 85.48% when 260–315 milling passes were in severe wear state. GWO, which can accurately find the best parameters for VMD, not only solves the problem that the Entropy Function is not suitable for force signals, but also provides reference for the selection of parameters of VMD.

Study on tool wear state monitoring based on EEMD information entropy and PSO-SVM

Tool wear state is closely related to the workpiece surface, so tool wear monitoring has an important engineering significance. This paper proposes a tool for wear monitoring means based on EEMD information entropy and PSO-SVM. Firstly, the force signal of the cutting process collected force by the sensor is decomposed by EEMD, and information entropy of the decomposed signal was used as the tool wear feature quantity. Then the PSO-SVM model was used to classify and identify the extracted tool wear feature quantity, and the recognition accuracy of the method was 97.75%. Compared with BP neural network and SVM model without algorithm optimization, it can quickly and accurately identify three types of tool wear states meanwhile realizing monitoring of tool wear state.

Wear monitoring solution for end mills using deep learning and mobile application

Mobile computing can effectively monitor tool life by establishing a relationship between periodically captured cutting tool images and the usefulness level of an end mill. This work presents an image-based deep-learning model to estimate end mill wear parameters and provide tool state feedback to the machine operator. The algorithm estimates the Remaining Useful Life (RUL) and tool wear state from the mobile camera images, viz. initial, intermediate, and worn. The operator captures cutting tool images at predefined intervals on the machine using a mobile camera, macro lens, and tripod arrangement. A pre-trained network, GoogLeNet, is employed for feature extraction, linear regression, and recognizing the tool wear status as RUL. A mobile application is developed to display the wear state and RUL for assisting machine operators in replacing/regrinding decisions. The accuracy and robustness of the proposed model are demonstrated using RMSE (Root Mean Square Error) and Correlation Coefficient (R2) metrics. A set of machining experiments are performed, and it has been shown that the developed module can capture wear states and RUL for end mills. The proposed solutions can be deployed effectively by manufacturing industries for obtaining tool wear information without significant investment in machine vision hardware and software.

A hybrid-driven probabilistic state space model for tool wear monitoring

Tool wear monitoring (TWM) is essential to improve product quality and maintain machining safety. Within recent years, numerous techniques have been developed for TWM, including data-driven methods and physics-based models. However, traditional data-driven methods are highly dependent on measured data. The physics-based models are difficult to model complex machining processes. To solve these problems, a hybrid-driven probabilistic state space model is proposed to improve the accuracy and robustness of TWM. In this work, a sensitive feature extraction scheme is first constructed to eliminate interferences and redundancies for improving monitoring efficiency. Subsequently, the Gaussian process is innovatively developed to integrate the data mining and physical model from a probabilistic perspective. On this basis, the particle filter is then utilized to estimate the model parameters and estimate tool wear conditions. Finally, an adaptive wear state recognition method is further established for the predictive maintenance of cutting tools. The practical data obtained from milling experiments are used to validate the effectiveness of the proposed methodology. The results show that the proposed hybrid-driven method effectively improves tool wear prediction accuracy to over 97.7%, while the constructed probabilistic model successfully evaluates the prediction results with over 95% confidence. Therefore, the developed methodology may provide a promising way for tool health management to improve economic efficiency and maintain machining safety.

Analytical modeling of milling residual stress under different tool wear conditions

Analytical modeling of milling residual stress under different tool wear conditions

Cutting tool wear state recognition based on a channel-space attention mechanism

The identification of the cutting tool wear state is important to the cutting process. If the cutting tool is not replaced in time, it may have a negative impact on the processing quality of the workpiece. However, the identification of the cutting tool wear state is mainly achieved by establishing a mapping relationship between cutting tool information and cutting tool wear state. The feature extraction of signal during cutting tool wear directly affects the accuracy of wear state identification model. Poor selection of cutting tool wear signal features will lead to misjudgment of cutting tool wear state. This will lead to more fuzzy areas in the process of cutting tool state recognition. In this paper, the cutting force signal is transformed into time-frequency images through continuous wavelet transform to realize the feature extraction of cutting tool wear. In this paper, a channel-space attention mechanism is established to evaluate the effectiveness of different cutting tool wear signal features. The cutting tool wear states recognition model is capable of selective learning of different wear feature. Finally, a mapping relationship between cutting tool wear states and wear feature is established by means of a fully connected layer. The identification of the cutting tool's wear state is thus completed. The model proposed in this paper achieved 0.965, 0.965 and 0.966 in the three-classification metrics of Accuracy, Recall and F1score respectively. The model reduces misclassification due to difficult to classify areas (Fuzzy areas) between different wear stages. It has high recognition accuracy and anti-interference capability. The proposed model can accurately identify the cutting tool wear state based on the processing monitoring information. This will help in making more flexible cutting tool replacement decisions on smart manufacturing.

A Robust Tool Condition Monitoring System Based on Cluster Density under Variable Machining Processes

Real-time tool condition monitoring (TCM) is becoming more and more important to meet the increased requirement of reducing downtime and ensuring the machining quality of manufacturing systems. However, it is difficult to satisfy both robustness and effectiveness of pattern recognition for a TCM system without using an unsupervised strategy. In this paper, a clustering-based TCM system is proposed that can be used for different machining conditions such as variable cutting parameters, variable cutters, and even variable cutting methods. The solution is based on a significant statistical correlation between tool wear and the distribution of cutting force features, which is revealed through the clustering results obtained from a novel clustering method based on adjacent grids searching (CAGS). This statistical correlation is converted into tool wear status by using an empirical factor that is robust for variable cutting processes. The proposed TCM system is completely unsupervised as a training-free procedure is used in the monitoring process. To verify the effectiveness of the system, a series of experiments are conducted, such as whole life-cycle wear experiment under same milling condition, tool wear experiment under variable milling conditions and tool wear experiment under same turning condition. The prediction accuracy of our system for tool wear experiment under variable milling conditions is 100%, 75% and 75%, respectively. In contrast, BP neural network, Bayesian network and SVM are used for tool wear prediction under the same conditions. Experimental results show the superiority and effectiveness of our TCM system based on cluster density of CAGS over several state-of-the-art supervised methods.

Synthetic Minority Oversampling Enhanced FEM for Tool Wear Condition Monitoring

Recent advances in artificial intelligence (AI) technology have led to increasing interest in the development of AI-based tool wear condition monitoring methods, heavily relying on large training samples. However, the high cost of tool wear experiment and the uncertainty of tool wear change in the machining process lead to the problems of sample missing and insufficiency in the model training stage, which seriously affects the identification accuracy of many AI models. In this paper, a novel identification method based on finite-element modeling (FEM) and the synthetic minority oversampling technique (SMOTE) is proposed to overcome the problem of sample missing and sample insufficiency. Firstly, a few tool wear monitoring experiments are carried out to obtain experimental samples with low cost. Then, a FEM model based on the Johnson–Cook constitutive model was established and verified according to the experimental samples. Based on the verified FEM model, the simulated missing sample in the experiments can be supplemented to compose a complete training set. Finally, the SMOTE is employed to expand the sample size to construct a perfect training set to train the SVM classification model. End milling tool wear monitoring experiments demonstrate that the proposed FEM-SMOTE method can obtain 98.7% identification accuracy, which is 30% higher than that based on experimental samples. The proposed method provides an effective approach for tool wear condition monitoring with low experimental cost.

Tool Wear State Recognition Based on Machine Learning

In order to quickly identify the current state of tool wear in real time, the standard of tool wear state division is analyzed first. The machine learning methods commonly used for pattern recognition classification including SVM, ANN, random forest and so on. In this paper, tool wear data sets are used to build a variety of machine learning models using the constructed feature space. LVQ, random forest and SVM are used to monitor the wear state respectively, and the classification accuracy of the classification model on the test set is calculated respectively.

Tool Wear State Recognition Based on 1D-CNN

Machine learning classification models have the problems of complex feature engineering and unsatisfactory state recognition. In this paper, a deep learning network, One-dimensional convolutional neural networks (1D-CNN), is proposed to recognize the state of tool wear. After the original data is cleaned and pre-processed, it is directly put into the 1D-CNN model for feature self-extraction and state recognition, which improves the automation, accuracy and efficiency of the whole recognition process.

A Hybrid Deep Learning Model as the Digital Twin of Ultra-Precision Diamond Cutting for In-Process Prediction of Cutting-Tool Wear

Diamond cutting-tool wear has a direct impact on the processing accuracy of the machined surface in ultra-precision diamond cutting. It is difficult to monitor the tool’s condition because of the slight wear amount. This paper proposed a hybrid deep learning model for tool wear state prediction in ultra-precision diamond cutting. The cutting force was accurately estimated and the wear state of the diamond tool was predicted by using the hybrid deep learning model with the motion displacement, velocity, and other signals in the machining process. By carrying out machining experiments, this method can classify diamond-tool wear condition with an accuracy of more than 85%. Meanwhile, the effectiveness of the proposed method was verified by comparing it with a variety of machine learning models.

A novel online tool condition monitoring method for milling titanium alloy with consideration of tool wear law

Due to issues such as limited variability in monitoring data across different tool wear conditions and interference during the machining process, data-driven monitoring models are susceptible to misclassification. Therefore, this paper proposes a pioneering approach that takes into account the tool wear law and the characteristic distribution of tool wear monitoring data. Specifically, the paper proposes an automatic feature extraction and tool condition monitoring method based on Siamese Long Short-term Memory Networks (SLSTMs) to transform the original data distribution, enhance the differentiation of different tool wear condition monitoring data, and achieve accurate prediction of tool wear condition. Additionally, a hybrid data and mechanism-driven tool wear monitoring method is proposed that takes advantage of the fact that tool wear is continuous and irreversible to enable reliable adjustment of the monitoring results of the data-driven model without human intervention. The study conducted milling experiments on a three-axis vertical machining center, using TA2 titanium alloy as the workpiece material. Vibration signals from the spindle in three directions were collected as input to the network, with tool wear state labeled as “0″ or ”1″ as the output. Classical machine learning algorithms such as Decision Trees, K-Nearest Neighbors (KNN) and Support Vector Machines (SVM), as well as classical deep learning algorithms such as Convolutional Neural Networks (CNN), Sparse Stacked Autoencoders (SSAE) and Long Short-Term Memory Neural Networks (LSTM), were used to construct and compare feature extraction and state monitoring models. The proposed model based on Siamese Long Short-Term Memory Neural Networks (SLSTMs) achieved testing average accuracy of 98.2% (σ2 = 0.44), outperforming typical deep learning algorithms such as CNN, LSTM and SSAE as well as traditional machine learning algorithms such as Decision Trees, KNN and SVM in terms of accuracy and robustness. Moreover, the proposed data and mechanism hybrid-driven tool wear monitoring method can effectively improve the monitoring accuracy and robustness of data-driven models such as CNN and SSAE. The monitoring accuracy of CNN and SSAE increased from 95.9% to 97.6% and 91.0% to 94.3%, respectively.

Tool wear prediction method based on the SVM-Clara model

Abstract To reduce the damage of mechanical parts during machining, a tool wear prediction method based on the SVM-Clara model is proposed. By analyzing the support vector machine (SVM) and Clara algorithm, using regular prediction data or unobservable data, the average dissimilarity of all objects is concentrated, and the characteristics of the overall data are accurately represented. Randomly select data samples from the overall data samples according to a certain proportion, and standardize them to improve the clustering quality. Find the best objective function to minimize the damage function and make the predicted value closer to the actual value. Through experiments, it is proved that the method in this paper can accurately predict the tool wear condition, the mean square error value is 0.03, the prediction method is better, and the production efficiency is ensured.

Tool Wear Condition Monitoring Method Based on Deep Learning with Force Signals.

Tool wear condition monitoring is an important component of mechanical processing automation, and accurately identifying the wear status of tools can improve processing quality and production efficiency. This paper studied a new deep learning model, to identify the wear status of tools. The force signal was transformed into a two-dimensional image using continuous wavelet transform (CWT), short-time Fourier transform (STFT), and Gramian angular summation field (GASF) methods. The generated images were then fed into the proposed convolutional neural network (CNN) model for further analysis. The calculation results show that the accuracy of tool wear state recognition proposed in this paper was above 90%, which was higher than the accuracy of AlexNet, ResNet, and other models. The accuracy of the images generated using the CWT method and identified with the CNN model was the highest, which is attributed to the fact that the CWT method can extract local features of an image and is less affected by noise. Comparing the precision and recall values of the model, it was verified that the image obtained by the CWT method had the highest accuracy in identifying tool wear state. These results demonstrate the potential advantages of using a force signal transformed into a two-dimensional image for tool wear state recognition and of applying CNN models in this area. They also indicate the wide application prospects of this method in industrial production.

A frequency-spatial hybrid attention mechanism improved tool wear state recognition method guided by structure and process parameters

Real-time and accurate monitoring of tool wear status is critical for optimizing product quality and production costs. Deep learning has received much attention as an emerging data-driven technique in the field of tool wear monitoring because of its powerful feature extraction and nonlinear mapping capabilities. However, the model structure and features' poor interpretability limits the application of deep learning in practical machining. Herein, a frequency-spatial hybrid attention network is proposed, driven by tool structure and process parameters (FSHAN-SPD). The structure of FSHAN-SPD considers the recognition of critical frequencies and spatial positions simultaneously, and the receptive field of its spatial attention module can effectively distinguish the cutting stage of each tooth. Attention modules-weighted features are used for interpretability analysis. The effectiveness and flexibility of this method are verified in the PHM2010 dataset. A milling experiment illustrates a more detailed procedure. The experimental results are well interpretable in both frequency and spatial.

A hybrid method for cutting tool RUL prediction based on CNN and multistage Wiener process using small sample data

Based on the multistage and nonlinear characteristics of cutting tool wear, a hybrid method for cutting tool remaining useful life (RUL) prediction based on convolutional neural network (CNN) and multistage Wiener process using small sample data is proposed in this paper. First, wavelet transform is used to analyse the vibration data collected for cutting tools in the time–frequency domain to obtain an initial image data set. A CCVAE (conditional variational autoencoder with CNN) network is built to expand the initial data set and solve the problem of unbalanced data in different stages of cutting tool wear. The expanded data is used as the input of the CNN to monitor the tool wear state and amount of wear. Then, a nonlinear multistage Wiener process is established to describe the cutting tool wear degradation process and achieve accurate RUL prediction for the cutting tool. Specifically, a nonlinear Wiener process model corresponding to different degradation stages based on the change points between the cutting tool wear states output by the CNN is established. Maximum likelihood estimation and Bayesian methods are used to estimate and update the parameters, respectively, and the RUL value and corresponding probability density function (PDF) are obtained under different wear conditions. Finally, through experimental research and comparative analysis, it is found that the multistage nonlinear Wiener model accurately simulates cutting tool wear degradation, which verifies the feasibility and performance of the method proposed in this paper.

Tool wear identification and prediction method based on stack sparse self-coding network

In the process of metal cutting, the effective monitoring of tool wear is of great significance to ensure the machining quality of parts. Aiming at the problem of tool wear monitoring, a tool wear recognition and prediction method based on stack sparse self-coding network is proposed. This method can simplify the establishment process of monitoring model, monitor the tool wear according to different task requirements, and guide the tool replacement in the actual cutting process. Firstly, unsupervised K-means clustering is used to divide the tool wear stage, and the feature set is marked. Secondly, the parameters of stack sparse self-coding network layer are determined by trial, and the sensitive features that can reflect the tool wear process are obtained. Finally, the tool wear identification model of stack sparse self-encoder and the tool wear prediction model of BP neural network are established respectively, and the smoothing correction method is used to further improve the prediction accuracy. The experimental results show that the established tool wear identification and prediction model can accurately monitor the tool wear state and wear amount, and has a certain reference value for efficient tool change in the actual metal cutting process.

Tool wear condition monitoring across machining processes based on feature transfer by deep adversarial domain confusion network

Tool wear condition monitoring across machining processes based on feature transfer by deep adversarial domain confusion network

Tool wear classification based on convolutional neural network and time series images during high precision turning of copper

Artificial intelligent techniques are critical in predicting tool wear in the machining process. Effective assessment of the rate and state of tool wear are important in the high-speed turning process and makes it possible to replace the tool before catastrophic wear occurs. It is well known that cutting forces are often used as an indicator to monitor tool wear during the machining process. However, how to use deep learning methods to incorporate cutting forces, accurately predicting the tool wear, remains a challenge. In this paper, a novel tool wear classification method based on time series images of cutting forces and convolutional neural networks was proposed. The one-dimensional cutting force signals are converted into two-dimensional time series images using the Gramina Angular Summation Fields method. Then, a modified AlexNet network is used to classify tool wear types based on the processed cutting force time series images. The prediction results show the overall prediction accuracy of the four tool wear categories can reach around 90%. In addition, by balancing the number of cases in the dataset, the prediction accuracy of each category can be improved while the overall prediction accuracy is maintained.

Development of tool wear condition on-line monitoring method for impeller milling based on new data processing approach and DAE-BP-ANN-integrated modeling

Tool wear condition monitoring plays an important role in the maintaining machining accuracy and machining efficiency of complex surface parts. In this study, a new on-line tool wear monitoring method based on a self-developed data processing approach for the impeller milling was proposed. To achieve that, a new tool wear experimental platform was first built to collect both the spindle current signal and thermal deformation data in entire life cycle of cutter. Based on collected data, features of the time domain, frequency domain and time-frequency domain were extracted indiscriminately, and a 38 × 156 feature-sample set was subsequently established. To further reduce the dimensions of this feature-sample set and rise its characterization capability, the feature set was further processed using the sensitivity analysis and deep auto-encoder algorithm. Finally, 12 synthesized features were filtered out and then used to build the mapping model of signal synthesized features to different tool wear conditions by adopting the structural artificial neural network (ANN) integrated with back propagation (BP) algorithm. To verify the reliability of the proposed BP-ANN-integrated tool wear condition monitoring model, another comparison analysis of different data processing approaches was conducted. The comparison results showed that the proposed method for tool wear condition online monitoring had reliable performance and the recognition accuracy was 88.9%.