On-line Tool Wear Monitoring Research Articles

Advances in Research on Tool Wear Online Monitoring Method

Background: With the continued advancement of industrial internet technology, mechanical manufacturing is increasingly developing towards automation and intelligence. As a result, monitoring the manufacturing process has become an essential requirement for intelligent manufacturing. As one of the fundamental components of cutting processes, tools are inevitably subject to wear and damage during use. Therefore, tool wear monitoring plays a crucial role in modern manufacturing. Introduction: With the development of the manufacturing industry, the requirement for automation manufacturing is higher and higher. In the process of automatic processing, unmanned processing and adaptive processing, it is not only required to be able to know the accurate wear state of the tool in the process real-time but also required to change the milling parameters according to the wear state of the tool, in order to optimize the productivity and processing quality. The tool monitoring system can effectively reduce the operating cost of workshop production and improve the reliability of intelligent workshop and flexible production lines. Method: This article summarizes commonly used online monitoring methods mentioned by articles and patents, such as cutting force, vibration, acoustic emission, temperature, current, and power signals. Each monitoring method is analyzed in terms of its principles, advantages and disadvantages, signal acquisition equipment, and research status. The article also identifies current issues and future development directions. Results: As modern manufacturing technology continues to develop rapidly, unmanned factories have become a significant feature of the manufacturing industry. Consequently, the need for tool wear condition monitoring technology is becoming increasingly urgent. Although tool condition monitoring technology has made significant progress over the past twenty years and has been applied in actual production, several issues need to be addressed to make tool wear condition monitoring systems mo. Conclusion: This serves as a reference for theoretical research and application of online monitoring of tool wear in intelligent manufacturing systems.

Tool wear monitoring strategy during micro-milling of TC4 alloy based on a fusion model of recursive feature elimination-bayesian optimization-extreme gradient boosting

Online monitoring of tool wear is an indispensable part of industrial production. It allows for real-time assessment of tool condition, enabling tool replacement at the optimal time. This not only reduces the time costs associated with downtime for inspections but also effectively prevents the deterioration of workpiece quality due to excessive wear. Therefore, online monitoring of tool wear is essential for ensuring machining quality and efficiency. This research presents a monitoring technique for tool wear while performing micro-milling by extracting features from machining signals. Initially, the wear mechanism, which includes initial, regular, and severe wear, is analyzed. Tool wear labels are then constructed. Subsequently, the recursive feature elimination (RFE) algorithm optimizes the original feature set from the processed signal, resulting in a subset of relevant features that accurately describe the tool wear. The optimal subset is utilized as an input vector for extreme gradient boosting (XGBoost) training. Next, the best hyperparameter combination for the monitoring model is determined using Bayesian optimization (BO), ensuring the generalization performance of the monitoring model. Finally, the two-edge wear monitoring model is established based on the RFE-BO-XGBoost. To validate the superiority of the RFE-BO-XGBoost model, the performance of the XGBoost model with default parameters in tool wear monitoring is also investigated. The results demonstrate that the constructed monitoring model can effectively identify the tool wear stage. More notably, it precisely predicts the wear status of two cutting edges simultaneously, enabling qualitative and quantitative monitoring of tool wear under normal manufacturing settings.

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.

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.

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.

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%.

Online tool wear monitoring by super-resolution based machine vision

The tool condition has been a major concern in modern computer numerical control (CNC) machining due to its direct effects on the quality of final product both in the surface and dimensional integrity. The conventional machine vision-based tool condition monitoring (TCM) approaches cannot meet the high precision requirement in micro machining, as the cutting parameters are in micro scale and the spindle works in high rotation speed which makes online tool wear measurement quite difficult. To meet these challenges, this study develops a single image super-resolution (SISR) approach for direct tool wear estimation in micro-milling. Motivated by the self-similarity of tool wear image morphology, this study proposes a sparse decomposition framework by learning dictionaries from the tool wear image pyramid. Based on their multi-scale invariant properties, the similar image patches of coarse scales can be retrieved from fine scales to reconstruct the high-resolution image fastly and in high quality. The reconstructed high-resolution image then can be conveniently applied to wear monitoring, and overcomes the image acquisition deficiencies of the conventional machine vision-based monitoring approaches. Experimental results validate this approach for the tool wear area estimation as well as its generalization of the wear width with regarding to the conventional manual measurements.

Milling force coefficients-based tool wear monitoring for variable parameter milling

Tool wear is an important factor that affects the quality and machining accuracy of aeronautical structural parts in the milling process. It is essential to monitor the tool wear in titanium alloy machining. The traditional tool wear features such as root mean square (RMS), kurtosis, and wavelet packet energy spectrum are related to not only the tool wear status but also to the milling parameters, thus monitoring the tool wear status only under fixed milling parameters. This paper proposes a new method of online monitoring of tool wear using milling force coefficients. The instantaneous cutting force model is used to extract the milling force coefficients which are independent of milling parameters. The principal component analysis (PCA) algorithm is used to fuse the milling force coefficients. Furthermore, support vector machine (SVM) model is used to monitor tool wear states. Experiments with different machining parameters were conducted to verify the effectiveness of this method used for tool wear monitoring. The results show that compared to traditional features, the milling force coefficients are not dependent on the milling parameters, and using milling force coefficients can effectively monitor the transition point of cutters from normal wear to severe wear (tool failure).

MicroEye : A low-cost online tool wear monitoring system with modular 3D-printed components for micro-milling application

Tool detachment during the machining process is often required by many image-based tool wear monitoring (TWM) systems. Tool detachment prevents the online mode of the wear measurement, extends the machining time, and contributes to measurement inaccuracy. Other alternatives of the image-based TWM systems have been developed with the image-acquisition device located statically near the tool position without the requirement for the tool detachment. However, due to its proximity to the machining site, the image-acquisition device may experience obstruction from the workpiece chips and the splash of coolant fluid during the machining process, resulting in non-optimal TWM. This article presents MicroEye – an online image-based TWM system with modular 3D-printed components to overcome the two problems. MicroEye offers great flexibility in its operation through the use of an active 6-DOF (degree of freedom) robotics arm with a camera at the end-effector. MicroEye does not require tool detachment to perform tool wear monitoring and can be safely placed outside the machining area. MicroEye is the first open-sourced, 3D-printed components and active dynamic-type TWM system for the application of micro-milling. MicroEye can be built at a low-cost (approximately US$ 872, including the camera). MicroEye is suitable for various micro-milling sites, from laboratory scale to middle-low workshop.

The method of self-learning based online tool wear monitoring in semi-finishing or finishing working step

The method of self-learning based online tool wear monitoring in semi-finishing or finishing working step

Tool wear mechanism of turning Inconel 718 and EEMD-based feature analysis

In the cutting process of high temperature alloys, the performance of the tool is closely related to the surface quality of the workpiece. In this paper, a turning test was conducted on nickel-based high-temperature alloy Inconel 718 using PVD-coated carbide tools, and the microscopic morphology of tool wear was observed by scanning electron microscopy (SEM), and the chemical elemental composition of the tool wear surface was analyzed by energy spectrum analyzer (EDS), and the wear mechanism of PVD-coated carbide tools in three stages of tool wear was analyzed in detail. On this basis, the characteristic quantities closely related to tool wear were extracted in the time and frequency domains by the collected cutting force signals, which provide effective characteristic inputs for the subsequent study of online tool wear monitoring.

Estimation of Tool Wear and Surface Roughness Development Using Deep Learning and Sensors Fusion.

This paper proposes an estimation approach for tool wear and surface roughness using deep learning and sensor fusion. The one-dimensional convolutional neural network (1D-CNN) is utilized as the estimation model with X- and Y-coordinate vibration signals and sound signal fusion using sensor influence analysis. First, machining experiments with computer numerical control (CNC) parameters are designed using a uniform experimental design (UED) method to guarantee the variety of collected data. The vibration, sound, and spindle current signals are collected and labeled according to the machining parameters. To speed up the degree of tool wear, an accelerated experiment is designed, and the corresponding tool wear and surface roughness are measured. An influential sensor selection analysis is proposed to preserve the estimation accuracy and to minimize the number of sensors. After sensor selection analysis, the sensor signals with better estimation capability are selected and combined using the sensor fusion method. The proposed estimation system combined with sensor selection analysis performs well in terms of accuracy and computational effort. Finally, the proposed approach is applied for on-line monitoring of tool wear with an alarm, which demonstrates the effectiveness of our approach.

Optimization of milling process parameters and prediction of abrasive wear rate increment based on cutting force experiment

Tuning the parameters of Computerized Numerical Control (CNC) is essential for practical manufacturers. Well configured parameters ensure the efficiency of production and the accuracy of the products. However, with the abrasive wear on the flank of the milling cutter, the milling processing parameters should re-configure to adapt to the increment of the abrasive wear. This paper aims to propose a method to predict the abrasive wear rate increment on the flank of the milling cutter and optimize the processing parameters of CNC milling. Firstly, we set a cutting data acquisition system to sample the processing time and cutting force among X, Y coordinates based on the five-factor and four-level orthogonal experiments. Then, the sampled cutting force data increment is transformed into the abrasive wear rate increment by applying the incremental model. Next, five processing parameters for CNC milling are optimized by the gray relational method, which takes the limited abrasive wear rate increment of the flank face and the non-increasing processing time as the constrained conditions. We obtain the relationship between five processing parameters and abrasive wear rate increment. We also find the basic principle of selecting process parameters is to reduce the abrasive wear rate without increasing the processing time. The experimental results verify that the optimized process parameters make the gray relational degree increase by 0.02, and the abrasive wear rate increment decreases by 0.42432 × 10−10 mm3/s without affecting the production efficiency. In the prediction section, by applying the Back Propagation (BP) neural network, we obtain an accurate prediction model from measurable five factors to the abrasive wear increment on the flank of the milling cutter. The maximum error between the predicted value and the actual value is 0.0003, and the predicted value curve fits well with the actual value curve. From the perspective of abrasive wear rate increment prediction, it provides a new idea for online tool wear monitoring.

Tool Wear Online Monitoring Method Based on DT and SSAE-PHMM

Abstract The real-time requirements of tool wear states monitoring are getting higher and higher, at the same time, tool wear monitoring lacks a modeling data comprehensive carrier, which hinders its application in the actual machining process. In order to solve this problem, combining the high fidelity real-time behavior simulation characteristics of digital twin (DT) and the powerful data mining capabilities of artificial intelligence, an online tool wear monitoring method based on DT and Stack Sparse Auto-Encoder-parallel hidden Markov model (SSAE-PHMM) was proposed. First, a DT which can reflect the real state of the tool was established, and the tool wear state was predicted by visual display and analysis in the virtual space; Second, a tool wear state recognition model based on SSAE-PHMM was established, which can adaptively complete time domain feature extraction. And for each tool wear state, multiple HMM models were combined into a PHMM model to realize accurate recognition of tool wear state. PHMM overcome the defects of poor convergence and long training time of artificial neural network, and greatly improved the performance of classifier. Through the deep integration of DT and artificial intelligence, real-time data-driven tool wear qualitative and quantitative online monitoring was realized, and the effectiveness of this method was verified by experiments.

A novel approach of combined edge detection and segmentation for tool wear measurement in machining

Product quality in machined parts is governed by many factors, out of which the state of wear of the tool is one of the most critical factors. Knowing the condition of the tool wear makes it possible to optimize the tool life and simultaneously maintain the surface quality. There are methods of online wear measurement proposed in the literature, like correlating some physical parameters to the wear state of the tool. As the processes are indirect, they do not provide exact values of the tool wear, but only aids in classifying the wear into different states from mild to severe. This work is focused on developing direct tool wear measurement by applying image processing techniques, which is more accurate, and precise. It has a very negligible interruption in production, and helps in automation of the task of tool wear monitoring and replacing it. In this paper, a novel online tool wear measuring algorithm is proposed using combined techniques of edge detection and segmentation. A complementary metal–oxide–semiconductor (CMOS) sensor camera is utilized to capture the wear zone images. The tool’s wear value is extracted by establishing wear boundaries through image processing, threshold segmentation, edge detection, and morphological operation. The machining tests are performed on a CNC lathe. The tool wear measured by the proposed technique is compared with the measurements obtained by an optical microscope. The results demonstrated high detection accuracy of the proposed approach enabling online tool wear monitoring during the turning process.

Tool condition monitoring: unscented Kalman filter for tool flank wear estimation in turning of Inconel 718

This article presents flank wear estimation during the turning process of Inconel 718 in dry cutting condition using unscented Kalman filter (UKF). A discrete flank wear model is developed where two components of flank wear due to abrasion and diffusion are considered as state variables and the cutting force is taken as the output variable. The proposed model can be implemented for online tool wear monitoring and the UKF predicts the actual states of tool flank wear in real-time by measuring the cutting force variation. The simulation result of flank wear estimation using UKF is compared with the extended Kalman filter (EKF) under a similar cutting environment. The error of estimation for UKF is obtained less than that of EKF indicating better accuracy of UKF than EKF for tool condition monitoring of the proposed model. Hardware experiments are performed to validate simulated results of both UKF and EKF on the proposed model correlating the cutting force and flank wear components.

Research on On-line Tool Wear Monitoring Technology Based on GPR

At present, many kinds of sensors are used for on-line monitoring of cutting process, tool identification and timely replacement. However, most of the original monitoring signals extracted from the cutting process are time series signals, which contain too much process noise. As the signal noise is relatively low, it is difficult to establish a direct relationship with the tool wear. Therefore, how to obtain the effective information from the online monitoring signal and extract the characteristics that can directly reflect the tool wear from the complex original signal, so as to establish an effective and reliable tool wear monitoring system, is the key and difficult problem in the research of the online monitoring technology of tool wear. Firstly, an experimental platform based on the force sensor for on-line monitoring of tool wear was built, and the signal obtained by the force sensor was used to monitor the tool wear, and the feature information was extracted and fused. The innovation of the project lies in the use of Gaussian process regression (GPR) method to predict the tool wear, the use of feature dimensional rise technology, to reduce the impact of noise, on the premise of ensuring the prediction accuracy, improve the confidence interval of GPR prediction results, improve the stability and reliability of the monitoring process.

On-line tool wear monitoring based on machine learning

Accurate tool condition monitoring is necessary for the development of automatic milling technology. In order to improve the accuracy and real-time of online monitoring of tool wear state in machining process, an online monitoring system of milling cutter state based on LabVIEW software development is proposed. Firstly, the modern monitoring technology is introduced into the online monitoring of tool state in principle. The vibration signal is analyzed by wavelet packet in time-frequency domain, and the online monitoring of tool state is realized by machine learning algorithm model. The system can be used for real-time monitoring of tool status, timely alarm to facilitate tool replacement, and ensure high efficiency and high quality of processing. The effectiveness and feasibility of the online monitoring system for milling cutter wear state are verified by experiments, and the purpose of online monitoring tool wear state is preliminarily realized.

Tool wear monitoring in turning using image processing techniques

Abstract Observing the tool wear is very important in metal cutting industry as it affects the dimensional accuracy and surface quality of work part. Generally, tool wear is measured directly using an optical microscope which is an offline and time-consuming technique. In the indirect technique, the parameter such as force, surface finish, temperature etc. that is affected by tool wear is measured and correlated with tool life. It has been observed that measurement of tool wear using image processing techniques has a lot of scope. Digital image processing techniques automate the task of measurement and monitoring of tool wear. A novel on-line tool wear measuring algorithm is proposed to monitor tool wear using machine vision. This work is focused on implementing digital image processing techniques to automate the task of two dimensional flank wear measurement. The tool wear images are acquired by a camera, and the wear boundaries are recognized by image pre-processing, threshold segmentation and edge detection to measure the wear on tool. The wear values obtained by proposed method are compared with manual measurement. The maximum flank wear with proposed algorithm and manual measurement is found as 730 and 718 µm respectively. It is found that the maximum variation in the results obtained by algorithm and manual measurement is 4.98%. This shows the higher detection accuracy for on-line tool wear monitoring by the proposed algorithm.

Indirect online tool wear monitoring and model-based identification of process-related signal

Tool wear monitoring using vibrations is a complex task, due to various simultaneously occurring vibration sources and due to distortion of the signals acquired. This work investigates the mechanism by which tool wear information is concealed within acquired process-intrinsic vibration signals. Excluding other sources of vibration, such as machine-related, is attempted utilizing process simulations. As a case study, face milling is performed for three different cutting speeds. At first, the resulted simulated wear curves have been compared with experimental ones resulted under the same cutting conditions. Then, a quantification of the effect of tool wear on the acquired signals is presented.