Tool Wear Monitoring Research Articles

Image super resolution by double dictionary learning and its application to tool wear monitoring in micro milling

It is an effective means to improve the machining quality of product by monitoring the tool wear conditions in micro milling. However, due to the small size and high speed of the micro milling cutter, it is difficult to capture the clear image of tool wear by CCD camera. Therefore, a double dictionary sparse coding super-resolution (SR) method is proposed to improve the resolution of micro milling tool wear images in this work. Based on the traditional single dictionary, this work proposes the residual dictionary to reconstruct the residual image of the tool, achieving the double dictionary coefficient encoding reconstruction. In order to extract local features of low resolution (LR) tool wear images, the Gobor filter banks are designed, and then the PCA method is used to reduce dimension of extracted features. The dictionary learning based on a single high resolution (HR) image is realized, which reduces the dependence of super-resolution algorithm on large datasets. The effectiveness of Gabor filter and double dictionary for restoration tool wear image details is verified by experiments. Compared with proposed method, traditional interpolation method, sparse coding super resolution model [32] and super resolution convolutional neural network model [26], the PSNR mean value of those methods are 43.62, 42.19, 43.20, 43.21, respectively, which shows that the proposed method has excellent performance in super-resolution reconstruction of micro milling tool images for tool wear monitoring.

Research on tool condition monitoring (TCM) using a novel unsupervised deep neural network (DNN)

In order to improve the recognition precision and accuracy of tool wear monitoring, an unsupervised deep neural network (DNN) based on stack denoising autoencoder (SDA) is proposed. After feature extraction and selection, the stack denoising automatic coding network reduces the dimensionality of the feature vector. On this basis, principal component analysis (PCA) and T-distributed random neighbor embedding (t-SNE) are used to reduce the dimensionality of the features twice, and finally a simple two-dimensional feature matrix is obtained. Finally, the deep neural network model of SDA is established by adding SoftMax regression layer, and the tool wear monitoring results are taken as new labeled data, and the deep neural network parameters are fine-tuned by secondary backpropagation. The experimental results show that the proposed method can learn adaptively and obtain effective feature expression, and the tool wear state recognition results are highly accurate. The proposed method can effectively identify the tool wear state.

Tool Wear State Recognition Based on One-Dimensional Convolutional Channel Attention.

Tool wear state recognition is an important part of tool condition monitoring (TCM). Online tool wear monitoring can avoid wasteful early tool changes and degraded workpiece quality due to later tool changes. This study incorporated an attention mechanism implemented by one-dimensional convolution in a convolutional neural network for improving the performance of the tool wear recognition model (1DCCA-CNN). The raw multichannel cutting signals were first preprocessed and three time-domain features were extracted to form a new time-domain sequence. CNN was used for deep feature extraction of temporal sequences. A novel 1DCNN-based channel attention mechanism was proposed to weigh the channel dimensions of deep features to enhance important feature channels and capture key features. Compared with the traditional squeeze excitation attention mechanism, 1DCNN can enhance the information interaction between channels. The performance of the model was validated on the PHM2010 public cutting dataset. The excellent performance of the proposed 1DCCA-CNN was verified by the improvement of 4% and 5% compared to the highest level of existing research results on T1 and T3 datasets, respectively.

Generative and self‐supervised ensemble modeling for multivariate tool wear monitoring

AbstractDevelopment of an effective tool wear monitor requires maximum utilization of information from associated data, especially in machine learning based modeling. However, vastly varied annotated training data is required, which is not only expensive but impractical to obtain. In the present work, a contiguous approach of artificial data generation followed up by self‐supervised pre‐training before supervised model fine tuning and final stacked generalized ensembling, has been adopted to develop an effective tool wear monitor in a low data regime. Cross‐validated results of proposed methodology adoption in tool wear prediction on an experimental data set of few labeled samples attained an averaged MAE of 0.035, RMSE of 0.045 and MAPE of on the best case ensemble, which was comparatively superior to a purely supervised‐only trained deep model on the same data set, with an overall accuracy enhancement of over . The proposed approach provides an effective experimental data augmentation technique while simultaneously minimizing aleatoric uncertainty and allowing for utilization of information from often ignored static cutting parameters.

Discrete wavelet transforms analysis of vibration signals for correlating tool wear in diamond turning of additive manufactured Ti-6Al-4V alloy

Ultra-precision machining (UPM) of Ti-6Al-4V alloy is widely regarded as a challenging material processing due to excessive tool wear and chemical reactivity of the tool and workpiece. Tool wear has a significant influence on the surface quality and also causes damage to the substrate. Therefore, it is critical to consider the tool condition during diamond turning, especially as precision machining moves toward intelligent systems. Consequently, there is a need for effective ways for in-process tool wear monitoring in UPM. This study aims to monitor the diamond tool wear using time-frequency-based wavelet analysis on vibrational signals acquired during the machining of Additively Manufactured (AM) Ti6Al4V alloy. The analysis employed Daubechies wavelet (db4, level 8) to establish a correlation between the Standard Deviation (SD) of the magnitude in the decomposed vibrational signal obtained from both the fresh and used tools. The analysis revealed that at a feed rate of 1 mm/min, the change in SD is 32.3% whereas at a feed rate of 5 mm/min, the change in SD is 8.4%. Furthermore, the flank wear and microfractures are observed using a scanning electron microscope on the respective flank and rake face of the diamond tool.

On-line milling tool wear monitoring under practical machining conditions

Accurate diagnosis of milling tool wear monitoring during high-speed cutting processes is essential for ensuring machining surface precision, enhancing tool efficiency, and extending the service life of machine tools. However, challenges in data screening during each stage of tool movement and signal migration caused by changes in machining parameters have not been sufficiently addressed. To tackle these issues, this paper presents a novel MB-DAAN model, which considers signals from each stage of tool movement and diagnoses tool wear under dynamic machining parameters. Firstly, the multi-branch classification module is introduced, incorporating two independent classifiers to form a multi-branch classification module. This module adaptively extracts features of tool wear states during the cutting stage. Subsequently, the dynamic adversarial factor adjusts the global and local losses between features, aligning conditional and edge distributions across different machining parameters. The models were evaluated in the milling tool wear data set. MB-DAAN achieving a diagnostic accuracy of 97.3%-a significant improvement compared to CNN, DAN, and DAAN models.

Using machine learning for cutting tool condition monitoring and prediction during machining of tungsten

ABSTRACT Machining of single-phase tungsten, used as a plasma facing material in fusion energy reactors, is commonly associated with rapid tool wear and short tool life. Conventional methods of monitoring tool wear or changing cutting tools after a predetermined period are inefficient and can lead to unnecessary tool change or risk damaging the workpiece. Tool wear can adversely affect the surface finish and dimensional tolerances of machined parts. Predicting its onset can avoid this critical damage whilst ensuring maximum tool life is utilised. In this paper, firstly the tool life results in end milling single-phase tungsten using different cutting tool geometries and cutting speeds are provided for the first time. A novel method is proposed by combining sensor signal prediction and classification machine learning models. It works by forecasting the cutting tool bending moment signal which is then used for predicting future cutting tool condition in end milling of pure dense tungsten. A series of machining experiments, covering the whole life of a cutting tool, were performed to collect the sensor signals. The current time series signal from the sensory tool holder is employed to forecast the future signal by training a 1D convolutional neural network (1D CNN) and an artificial neural network (ANN). The forecasted signal is then used to predict the state of the cutting tool in the future. Machine learning classifiers namely, random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGBoost) supervised learning models were trained and validated on actual sensor signals to correlate the tool conditions with specific sensor signal features. The investigations revealed that the 1D CNN performed best in forecasting the time series sensor signal whilst achieving a mean absolute error of 3.37. In addition, the RF, when trained on Wavelet Scattering features, resulted in the most accurate classification of sensor signals for tool condition detection. The analysis showed that the combination of 1D CNN signal forecasting, feature extraction through statistical analyses and RF classifier performs best in predicting the state of a cutting tool in near future. Using this method allows for decision making for changing the tool whilst ensuring that the maximum useful life of a cutting tool is utilised. It also enables preventing undesired damage to the machined surface due to late detection of tool wear or delays in taking appropriate actions. The application of this method can reliably reduce the manufacturing costs and resource consumption associated with cutting tools for machining tungsten and minimise tool wear induced damage to the workpiece.

Early detection of tool wear in electromechanical broaching machines by monitoring main stroke servomotors

This paper aims to provide researchers and engineers with evidence that sensorless machine variable monitoring can achieve tool wear monitoring in broaching in real production environments, reducing production errors, enhancing product quality, and facilitating zero-defect manufacturing. Additionally, broaching plays a crucial role in improving the quality of manufacturing products and processes. These aspects are especially pertinent in aeronautical manufacturing, which serves as the experimental case in this study.The research presents findings that establish a correlation between the variables of a broaching machine’s servomotors and the condition of the broaching tools. The authors propose an effective method for measuring broaching tool wear without external sensors and provide a detailed explanation of the methodology, enabling reproducibility of similar results. The results stem from three trials conducted on an electromechanical vertical broaching machine, utilizing cemented carbide grade broaching tools to broach a superalloy Inconel 718 test piece. The machine data collected facilitated the training of a set of machine learning models, accurately estimating tool wear on the broaches. Each model demonstrates high predictive accuracy, with a coefficient of determination surpassing 0.9.

Tool wear monitoring using a novel parallel BiLSTM model with multi-domain features for robotic milling Al7050-T7451 workpiece

Tool wear monitoring using a novel parallel BiLSTM model with multi-domain features for robotic milling Al7050-T7451 workpiece

Identification of Tool Wear Based on Infographics and a Double-Attention Network

Tool wear is a crucial factor in machining as it directly impacts surface quality and indirectly decreases machining efficiency, which leads to significant economic losses. Hence, monitoring tool wear state is of the utmost importance for achieving high performance and efficient machining. Although monitoring tool wear state using a single sensor has been validated in laboratory settings, it has certain drawbacks such as limited feature information acquisition and inability to learn important features adaptively. These limitations pose challenges to quickly extending the monitoring function of tool wear state of the machine tools. To solve these problems, this paper proposes a double-attention deep learning network based on vibroacoustic signal fusion feature infographics. The first solution is the construction of novel infographics using tool-intrinsic characteristics and multi-domain fusion features of multi-sensor inputs, which includes correlation analysis, principal component analysis, and feature fusion. The second solution is to build a novel deep network with a double-attention module and a spatial pyramid pooling module which can accurately and quickly identify tool wear state by successfully extracting critical spatial data from the infographics at various scales. The validity of the network is examined through an interpretability analysis based on the class activation graph. In terms of the tool wear status recognition task, the F1 score of the double-attention model based on an information graph is 11.61% higher than Resnet18, and peak recognition accuracy reaches 97.98%.

Physics-informed Gaussian process for tool wear prediction

The tool wear monitoring (TWM) system plays an increasingly important role to ensure high quality finishing and system safety in advanced CNC machining process. The pure data-based TWM approaches generally needs to develop complex machine learning models and require massive sensory data to learn the models to reach high monitoring accuracy, while the physics-based tool wear models are simple but hard to adapt to varied working conditions. In order to incorporate the benefits of both methods, a novel physics-informed Gaussian process model is developed to predict the tool wear. Different from the traditional approaches, three tool wear physical models are introduced to develop the physics-informed Gaussian process regression (PB-GPR) model. The wear model is applied to constrain the mean function of the Gaussian process, so that the PB-GPR is more in line with the actual tool wear. At the same time, the model can initiate small data training to meet limited tool wear labels in practice, and then update the model with new measurements. Multi-sensor signals are collected and multi-domain features are extracted for the model learning. The proposed approach is validated from high speed milling experiments. The results show a significant performance improvement including tool wear prediction accuracy and robustness in extrapolation compared to the conventional machine learning methods.

Intelligent Tool Wear Monitoring Method Using a Convolutional Neural Network and an Informer

Tool wear (TW) is the gradual deterioration and loss of cutting edges due to continuous cutting operations in real production scenarios. This wear can affect the quality of the cut, increase production costs, reduce workpiece accuracy, and lead to sudden tool breakage, affecting productivity and safety. Nevertheless, since conventional tool wear monitoring (TWM) approaches often employ complex physical models and empirical rules, their application to complex and non-linear manufacturing processes is challenging. As a result, this study presents a TWM model using a convolutional neural network (CNN), an Informer encoder, and bidirectional long short-term memory (BiLSTM). First, local feature extraction is performed on the input multi-sensor signals using CNN. Then, the Informer encoder deals with long-term time dependencies and captures global time features. Finally, BiLSTM captures the time dependency in the data and outputs the predicted tool wear state through the fully connected layer. The experimental results show that the proposed TWM model achieves a prediction accuracy of 99%. It is able to meet the TWM accuracy requirements of real production needs. Moreover, this method also has good interpretability, which can help to understand the critical tool wear factors.

Tool wear process monitoring by damping behavior of cutting vibration for milling process

Tool wear process is a complex thermo-mechanical coupling dynamic process. Tool wear will cause the change of the dynamic property of the cutting process, namely the process damping. A new method for monitoring tool wear by on-line recognition of damping behavior of cutting vibration is presented. By modeling the milling force of worn tools, the influence of tool wear on milling force and cutting vibration is analyzed. For accurately identify the time varying process damping with tool wear increases, the vibration component near the dominant mode of cutting system is extracted from cutting vibration signal, and the local mean damping ratio is calculated using the instantaneous frequency (IF) and the instantaneous amplitude (IA). The method performance is demonstrated through experimental data of milling. The results indicate that the local mean damping ratio of milling vibration increases with tool wear, which can clearly indicate the wear progression of tool. The method provides an effective monitoring index for tool wear monitoring in machining.

On-line tool wear monitoring under variable milling conditions based on a condition-adaptive hidden semi-Markov model (CAHSMM)

Since tool wear during machining process imposes a significant limitation on production quality as well as efficiency, on-line tool wear monitoring (TWM) is one of considerably crucial issues for intelligent manufacturing system. However, there is still a lack of universal on-line TWM techniques appropriate for the practical production process with variable machining parameters. Therefore, this study proposes a highly condition-adaptive method for tool wear state monitoring and remaining useful life (RUL) estimation under time-varying operation conditions. Multi-domain features are first extracted from vibration, acoustic emission and sound signals, and subsequently fused by the kernel principal component analysis (KPCA) to highlight discriminative information correlated to tool wear. A condition-adaptive hidden semi-Markov model (CAHSMM) is established to describe the evolution of tool wear, in which the accelerated failure time model (AFTM) embedded with Taylor tool life equation is utilized to determine the state transition probabilities that are strongly dependent on cutting parameters and duration time. Moreover, to extrapolate the Gaussian mixture models (GMMs) used to represent the observation probability beyond the range where they were trained, a Moment matching based on-line unsupervised transfer learning (OUTL) method is developed. After obtaining the state transition and observation probabilities, a modified forward algorithm is eventually implemented so as to assess tool wear online. Both the dataset from milling experiments of Ti6Al4V and PHM 2010 are employed for verifying the effectiveness of the proposed method. The results show that the presented method enables to reliably evaluate wear state and RUL of the cutter even under time-switching operation parameters, which demonstrates its promising potential for industrial applications.

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.

Multi-scale one-dimensional convolution tool wear monitoring based on multi-model fusion learning skills

Effective tool wear monitoring (TWM) is crucial for accurately assessing the degree of tool wear, guiding tool replacement during actual cutting processes, ensuring stable machine operation, and improving workpiece processing quality. With the arrival of the era of Big data, more and more data-driven monitoring methods are used for TWM problems, but it also exposes the problems of over reliance on artificial feature extraction and selection, low robustness of the actual industrial environment and poor generalization of different machining processes. To solve these problems, this paper proposes a multi-scale one-dimensional convolution (MODC-MMFL) end-to-end TWM integrated network model based on multi-model fusion learning (MMFL) skills. Firstly, multi-scale local features of multi-sensor signals are adaptively extracted by multi-scale one-dimensional convolution (MODC) network, to realize multi-feature fusion. Then, using MMFL skills, the MMFL network is composed of deep attention temporal convolutional network (DATCN) and stacked bidirectional gate recurrent unit network (SBIGRU)， parallel learning time series features related to tool wear characteristics，and use a fusion layer to fuse these learned features, in which residual channel attention mechanism (RCAM) is used to improve network performance in DATCN network. Finally, the predicted tool wear value is output by fully connected regression network (FCR). In addition, this paper uses the PHM tool wear dataset to conduct experimental study on the proposed model, first verifying the effectiveness of the proposed model. Then, ablation experiments were conducted to investigate the impact of hyper-parameters on the predictive performance of the model. The model was enhanced through hyper-parameter tuning, and a generalized enhanced model was established. The experimental results showed that the enhanced model had better predictive performance compared to ordinary models. Finally, Gaussian noise is added to the original signal of the PHM tool wear dataset to simulate the high noise signal of the actual industrial environment. The noise signal is used to carry out experimental study on the enhanced model. The experimental results show that the enhanced model still has good prediction performance in the high noise environment and has high robustness to the actual industrial environment. After the above research, this paper uses the NASA tool wear dataset to conduct experimental study on the proposed model. The experimental results show that the proposed model has good predictive performance for different machining processes, verifying the generalizability of the proposed model for different machining processes. In summary, the model proposed in this paper can accurately predict tool wear values based on processing monitoring information, and has good predictive performance, anti-interference ability, and environmental adaptability, making it very suitable for practical industrial applications.

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.

Tool Wear Monitoring with Artificial Intelligence Methods: A Review

Tool wear is one of the main issues encountered in the manufacturing industry during machining operations. In traditional machining for chip removal, it is necessary to know the wear of the tool since the modification of the geometric characteristics of the cutting edge makes it unable to guarantee the quality required during machining. Knowing and measuring the wear of tools is possible through artificial intelligence (AI), a branch of information technology that, by interpreting the behaviour of the tool, predicts its wear through intelligent systems. AI systems include techniques such as machine learning, deep learning and neural networks, which allow for the study, construction and implementation of algorithms in order to understand, improve and optimize the wear process. The aim of this research work is to provide an overview of the recent years of development of tool wear monitoring through artificial intelligence in the general and essential requirements of offline and online methods. The last few years mainly refer to the last ten years, but with a few exceptions, for a better explanation of the topics covered. Therefore, the review identifies, in addition to the methods, the industrial sector to which the scientific article refers, the type of processing, the material processed, the tool used and the type of wear calculated. Publications are described in accordance with PRISMA-P (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols).

The multi-sensor-based measurement of machining signals and data fusion to develop predictive tool wear models for TiAlN-PVD coated carbide inserts during end milling of Inconel 617

Machining signals from the Force dynamometer, Acoustic Emission (AE), and Accelerometer are acquired and fused to develop Machine Learning (ML) models for tool wear monitoring of TiAlN-PVD coated carbide inserts. Milling experiments were performed on Inconel 617 with varied process parameter combinations until the tool flank wear met the failure criterion. Support Vector Regression, Random Forest Regression, and Long Short-Term Memory models are developed and compared based on a combination of sensor data fusion for tool wear predictions. It is observed that the Random Forest Regression approach can predict the tool wear with 94% accuracy compared to Support Vector Regression (85%) and Long Short-Term Memory (84%) models while using three-sensor data fusion. Further, the two-sensor data combination was used to test the relative efficacy of all the three developed machine learning tool wear models and found that the force dynamometer and the AE sensor fared better for Random Forest Regression and Long Short-Term Memory models in comparison to Support Vector Regression. For Support Vector Regression-based tool wear predictive models, force dynamometer and accelerometer data fusion performed better.