Tool Wear Estimation Research Articles

A technique for flank wear monitoring in milling machines based on the novel tool wear indicator

ABSTRACT This research investigates an innovative method for monitoring flank wear by analyzing these signal variations. By dissecting the signal bands into multiple frequency bands using Wavelet Packet Transform, a precise examination of the signal contents is enabled. Using the signal power spectrum to Shannon entropy (SPSSE) node power spectrum analysis metric, the specific frequency band that exhibits a stable rising trend in the proposed metric correlated with flank wear is identified. This frequency band serves as a basis for constructing a tool wear indicator that utilizes Mean Square Error (MSE) comparisons of a healthy baseline signal sample with the sample under test. The calculated indicator closely correlates with the actual measured levels of flank wear, allowing for effective evaluation of the tool condition and differentiation between the healthy tool and the tool exhibiting any levels of flank wear. The performance of the proposed method is evaluated using the publicly available benchmark NASA Ames Milling tool dataset. This innovative approach holds promise for real-time assessment of machining processes, facilitating timely intervention based on the tool wear estimation.

DeepTool: A deep learning framework for tool wear onset detection and remaining useful life prediction

Milling tool availability and its useful life estimation is essential for optimisation, reliability and cost reduction in milling operations. This work presents DeepTool, a deep learning-based system that predicts the service life of the tool and detects the onset of its wear. DeepTool showcases a comprehensive feature extraction process, and a self-collected dataset of sensor data from milling tests carried out under different cutting settings to extract relevant information from the sensor signals. The main contributions of this study are:•Self-Collected Dataset: Makes use of an extensive, self-collected dataset to record precise sensor signals during milling.•Advanced Predictive Modeling: Employs hybrid autoencoder-LSTM and encoder-decoder LSTM models to estimate tool wear onset and predict its remaining useful life with over 95 % R2 accuracy score.•Comprehensive Feature Extraction: Employs an efficient feature extraction technique from the gathered sensor data, emphasising both time-domain and frequency-domain aspects associated with tool wear.

A spin on active learning analysis for health monitoring.

Decisions on the operation and maintenance of high-valued structures are critical, collecting and analysing data that can inform these decisions motivates the development of structural health monitoring (SHM) and tool monitoring (TM) systems. Descriptive health-state information labels for measured data points are crucial for successful monitoring systems. However, the labels can be expensive to collect and are often unavailable. Due to this problem, fully-supervised machine learning is limited in it’s application to monitoring systems which gives rise to unsupervised and semi-supervised approaches, one of these approaches is active learning. Active learning aims to highlight which unlabelled data points have the highest value of information and would be most informative to the model if they were labelled. For risk-based active learning in SHM, the model is used to advise some maintenance decision process where querying a data point means an expert inspecting the structure to determine its health-state. The current paper applies an active learning regression technology to a machining tool health case study. The algorithm queries data points that are most important in helping determine the health-state of the tool. Querying the most informative datapoints can drastically reduce the costs of a monitoring system. The regression analysis results are used to estimate tool wear and inform a risk-based maintenance decision process. This addresses the problem of when to replace a tool based on its health status, which has applications for many monitoring system problems.

Industrial federated learning algorithm (P-PFedSGD) for tool wear estimation

In recent years, machine learning has been challenged by the growing concerns regarding data privacy. This has led to the emergence of federated learning, which aims to train a model across distributed clients without sharing their data, thereby resolving the data privacy issues. However, this scheme may not generalize well to the heterogeneous data of distributed clients, particularly in industrial applications. This has motivated the development of personalized approaches for preserving privacy. Therefore, in this study, we introduced an index called gradient divergence to verify heterogeneity based on federated learning, which was adopted to adjust the aggregation weight. A personalized federated learning with privacy preserving algorithm called personalized federated stochastic gradient descent (P-PFedSGD) was developed to improve the performance of federated learning on local datasets while maintaining the performance on other client datasets. P-PFedSGD allows clients to transmit the local gradient instead of the local model. This approach utilizes the advantages of both personalized and federated learning, while preserving data privacy. The developed P-PFedSGD algorithm was applied to tool-wear estimation to demonstrate its performance and effectiveness. The results showed that the developed approach has more functionality than other algorithms to overcome the challenges of federated learning, such as communication cost reduction and computation reduction for clients.

Intelligent milling tool wear estimation based on machine learning algorithms

Intelligent milling tool wear estimation based on machine learning algorithms

Data-model linkage prediction of tool remaining useful life based on deep feature fusion and Wiener process

Accurately predicting the tool remaining useful life (RUL) is critical for maximizing tool utilization and saving machining costs. Various physical model-based or data-driven prediction methods have been developed and successfully applied in different machining operations. However, many uncertain factors affect tool RUL during the cutting process, making it challenging to create a precise physical model to characterize the degradation of tool performance. The success of the purely data-driven technique depends on the amount and quality of the training samples, it does not consider the physical law of tool wear, and the interpretability of the prediction results is poor. This paper presents a data-model linkage approach for tool RUL prediction based on deep feature fusion and Wiener process to address the above limitations. A convolutional stacked bidirectional long short-term memory network with time-space attention mechanism (CSBLSTM-TSAM) is developed in the data-driven module to fuse the multi-sensor signals collected during the cutting process and then obtain the mapping relationship between signal features and tool wear values. In the physical modeling module, a three-stage tool RUL prediction model based on the nonlinear Wiener process is established by considering the evolution law of different wear stages and multi-layer uncertainty, and the corresponding probability density function is derived. The real-time estimated tool wear of the data-driven module is used as the observed value of the physical model, and the model parameters are dynamically updated by the weight-optimized particle filter (WOPF) algorithm under a Bayesian framework, thereby realizing the data-model linkage tool RUL prediction. Milling experiments demonstrate that the proposed method not only improves RUL prediction accuracy, but also has good generalization ability and robustness for prediction tasks under different working conditions.

Machine learning coupled with acoustic emission signal features for tool wear estimation during ultrasonic machining of Inconel 718

The material removal in the ultrasonic machining process is due to the fracture of the workpiece material. The fracture is due to energy transfer by vibrating abrasive particles. The vibration energy is induced by a tool oscillating at a frequency of more than 20 kHz. The vibrating particles not only impact the workpiece surface but also impact the oscillating tool, and it leads to the fracture of the tool, which is of interest to understand to guarantee efficient machining with accuracy and precision. In the current investigation, the tool wear during the ultrasonic machining of Inconel 718 super alloy is attempted. An acoustic emission sensor is integrated with the machining setup, and signal information is extracted in the time and time-frequency domains. The features and process parameters are input to a support vector regression model to estimate tool wear. The model developed for tool wear prediction yields an accuracy of 96.13% compared to the model developed with only process parameters, which delivers an accuracy of 84.89%. The developed model can be beneficial for the real-time monitoring of tool wear during the ultrasonic machining process for industrial and remote applications.

Deep Learning Approach for Enhanced Transferability and Learning Capacity in Tool Wear Estimation

As an integral part of contemporary manufacturing, monitoring systems obtain valuable information during machining to oversee the condition of both the process and the machine. Recently, diverse algorithms have been employed to detect tool wear using single or multiple sources of measurements. In this study, a deep learning approach is proposed for estimating tool wear, considering cutting parameters. The model's accuracy and transferability in tool wear estimation were assessed with milling experiments conducted under varying cutting parameters. The results indicate that the proposed method outperforms conventional methods in terms of both transferability and rapid learning capabilities.

The analysis of the effect of varying feed values in each pass on tool wear and surface roughness in turning of AISI 4140

Machining is a complex process between the cutting tool and the workpiece, and in this process, the cutting tool constantly wears out with various wear mechanisms. Tool-wear estimation in turning is a complicated process; thus, there is a need for a study to reveal the time-dependent behavior of tool wear. In this study, tool wear and surface roughness change during the turning process of AISI 4140 steel at low cutting speeds were examined in detail with variable feed values for the first time. For this purpose, a half-minute cutting time was applied for each pass to analyze the tool wear as gradually as possible. Tool wear (flank, notch, and nose), surface roughness (Ra, Rz, and Rt), and chip morphologies were examined after each turning operation. The flank wear reached 0.3 mm, which is one of the tool life criteria according to ISO 3685, and the experiments were terminated at the 149th minute. Initially, a significant surface improvement (approx. 300%) was observed due to the decrease in feed values. In subsequent experiments, the effect of feed was significantly reduced due to increased tool wear, and limited surface roughness improvement (approx. 30%) was observed. At the beginning of the experiment, the chip breaker function of the cutting insert was successful at almost all feeds. However, in subsequent experiments, it was found that even at higher feeds, the chip breaker geometry of the insert deteriorated due to tool wear, causing changes in chip morphology, and resulting in long and undesirable chips.

Bayesian-based uncertainty-aware tool-wear prediction model in end-milling process of titanium alloy

Tool wear negatively affects machined surfaces and causes surface cracking, therefore increasing manufacturing costs and degrading product quality. Titanium alloys, which are widely used because of their desirable mechanical properties, have problems associated with tool wear due to poor thermal properties, such as specific heat capacity and thermal conductivity. Therefore, the accurate prediction of tool wear is necessary during the titanium alloy end-milling process to improve product quality and ensure reliability for corrective decisions like tool replacement. To this end, uncertainty-aware tool-wear prediction should be performed. In this study, a deep learning-based tool-wear prediction model based on a Bayesian approach was proposed. First, a convolutional neural network (CNN)-based architecture that integrates multiscale information extracted from raw sensor measurement data, termed deep multiscale CNN (DMSCNN), was proposed. It used different-sized convolutional kernels in parallel to enable various receptive field sizes suitable for machining processes. Second, based on a Bayesian learning approach, DMSCNN was transformed into a probabilistic model that produced a predictive distribution for estimated tool wear. In particular, a variational inference was applied to DMSCNN parameters to provide uncertainty awareness. Experiments were conducted with data collected from an actual end-milling process under three different conditions. The results proved the effectiveness of the proposed DMSCNN for tool-wear prediction. Bayesian DMSCNN showed promising results, as it outperformed existing comparative deterministic methods as well as probabilistic methods for tool-wear prediction. The proposed method is expected to be effectively applied in smart manufacturing as well as other machining processes that require data-driven digital decisions.

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.

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.

Online monitoring model of micro-milling force incorporating tool wear prediction process

In modern manufacturing, micro-milling technology plays an essential role in manufacturing high-precision and complex micro-size parts. Exploring the changing rule of time-varying cutting is of great significance for understanding the micro-milling mechanism and improving the machining efficiency. In addition, tool wear identification and updating in advance can enhance the accuracy and sustainability of micromachining. This paper presents a tool wear prediction framework for micro-milling by a temporal convolution network, bi-directional long short-term memory, and the multi-objective arithmetic optimization algorithm. Then, a new integrated model for real-time micro-milling cutting force monitoring is constructed, considering tool deformation, tool runout, time-varying cutting coefficient, chip separation state, and tool wear estimation results. Based on the micro-milling experiment with workpiece material Al6061, the accuracy of the proposed tool wear prediction and cutting force model is verified. The developed model can provide theoretical guidance for statics and dynamics analysis in the micro-milling.

Smart Tool Wear Monitoring of CFRP/CFRP Stack Drilling Using Autoencoders and Memory-Based Neural Networks

The drilling of carbon fiber-reinforced plastic (CFRP) materials is a key process in the aerospace industry, where ensuring high product quality is a critical issue. Low-quality of final products may be caused by the occurrence of drilling-induced defects such as delamination, which can be highly affected by the tool conditions. The abrasive carbon fibers generally produce very fast tool wear with negative effects on the hole quality. This suggests the need to develop a method able to accurately monitor the tool wear development during the drilling process in order to set up optimal tool management strategies. Nowadays, different types of sensors can be employed to acquire relevant signals associated with process variables which are useful to monitor tool wear during drilling. Moreover, the increasing computational capacity of modern computers allows the successful development of procedures based on Artificial Intelligence (AI) techniques for signal processing and decision making aimed at online tool condition monitoring. In this work, an advanced tool condition monitoring method based on the employment of autoencoders and gated recurrent unit (GRU) recurrent neural networks (RNN) is developed and implemented to estimate tool wear in the drilling of CFRP/CFRP stacks. This method exploits the automatic feature extraction capability of autoencoders to obtain relevant features from the sensor signals acquired by a multiple sensor system during the drilling process and the memory abilities of GRU to estimate tool wear based on the extracted sensor signal features. The results obtained with the proposed method are compared with other neural network approaches, such as traditional feedforward neural networks, and considerations are made on the influence that memory-based hyperparameters have on tool wear estimation performance.

Tool-Wear-Estimation System in Milling Using Multi-View CNN Based on Reflected Infrared Images.

A novel method for tool wear estimation in milling using infrared (IR) laser vision and a deep-learning algorithm is proposed and demonstrated. The measurement device employs an IR line laser to irradiate the tool focal point at angles of -7.5°, 0.0°, and +7.5° to the vertical plane, and three cameras are placed at 45° intervals around the tool to collect the reflected IR light at different locations. For the processing materials and methods, a dry processing method was applied to a 100 mm × 100 mm × 40 mm SDK-11 workpiece through end milling and downward cutting using a TH308 insert. This device uses the diffused light reflected off the surface of a rotating tool roughened by flank wear, and a polarization filter is considered. As the measured tool wear images exhibit a low dynamic range of exposure, high dynamic range (HDR) images are obtained using an exposure fusion method. Finally, tool wear is estimated from the images using a multi-view convolutional neural network. As shown in the results of the estimated tool wear, a mean absolute error (MAE) of prediction error calculated was to be 9.5~35.21 μm. The proposed method can improve machining efficiency by reducing the downtime for tool wear measurement and by increasing tool life utilization.

A novel approach of tool condition monitoring in sustainable machining of Ni alloy with transfer learning models

Cutting tool condition is crucial in metal cutting. In-process tool failures significantly influences the surface roughness, power consumption, and process endurance. Industries are interested in supervisory systems that anticipate the health of the tool. A methodology that utilizes the information to predict problems and to avoid failures must be embraced. In recent years, several machine learning-based predictive modelling strategies for estimating tool wear have been emerged. However, due to intricate tool wear mechanisms, doing so with limited datasets confronts difficulties under varying operating conditions. This article proposes the use of transfer learning technology to detect tool wear, especially flank wear under distinct cutting environments (dry, flood, MQL and cryogenic). In this study, the state of the cutting tool was determined using the pre-trained networks like AlexNet, VGG-16, ResNet, MobileNet, and Inception-V3. The best-performing network was recommended for tool condition monitoring, considering the effects of hyperparameters such as batch size, learning rate, solver, and train-test split ratio. In light of this, the recommended methodology may prove to be highly helpful for classifying and suggesting the suitable cutting conditions, especially under limited data situation. The transfer learning model with Inception-V3 is extremely useful for intelligent machining applications.

Dissociation artificial neural network for tool wear estimation in CNC milling

Dissociation artificial neural network for tool wear estimation in CNC milling

실시간 절삭 계수 식별 기반의 공구 마모 모니터링 시스템

Among the monitoring technologies in the metal-cutting process, tool wear is the most critical monitoring factor in real machining sites. Extensive studies have been conducted to monitor equipment breakdown in real-time. For example, tool wear prediction studies using cutting force signals and deducting force coefficient values from the cutting process. However, due to many limitations, those wearable monitoring technologies have not been directly adopted in the field. This paper proposes a novel tool wear predictor using the cutting force coefficient with various cutting tools, and its validity evaluates through cutting tests. Tool wear prediction from the cutting force coefficient should conduct in real-time for adoption in real machining sites. Therefore, a real-time calculation algorithm of the cutting force coefficient and a tool wear estimation method proposes, and they compare with actual tool wear in cutting experiments for validation. Validation cutting tests are conducted with carbon steel and titanium, the most commonly used materials in real cutting sites. In future work, validation will be conducted with different materials and cutting tools, considering the application in real machining sites.

A transformer‐based end‐to‐end data‐driven model for multisensor time series monitoring of machine tool condition

AbstractOnline determination of a cutter's health status is crucial for the attainment of condition‐based automated tool change in computer numerically controlled (CNC) machining. Due to the impracticalities associated with direct condition measurements, data‐based modeling of monitoring signals provides a viable practical route. However, the highly noisy and redundant nature of the associated data impacts negatively on model's accuracy and typically calls for additional initial preprocessing before modeling. Additionally, the long sequential data entails widely varying condition distributions exhibited by different cutters, even from the same batch on similar machining parameters, posing a challenge to model generalization. An end‐to‐end model has thus been developed to work directly on unprocessed data to establish global sensitive features from varying distributions for online tool wear estimation in CNC machining. The model utilizes three main functional blocks. First, a data denoising and feature selection block automatically processes raw multisensor data directly, dispensing with scaling or preprocessing of inputs as conventionally done. Each sensor channel's independence is preserved at initial processing ensuring complementary information from different sensors is utilized while simultaneously minimizing existing redundancies. The weighted denoised data is then processed through a transformer encoder block for determination of global dependencies in the time‐series sequence, regardless of the time‐step position. The learned features are then fed to an upper supervised learning block for association with the monitored wear condition. The developed model works directly on raw noisy data irrespective of scaling differences, saving on preprocessing computational cost. The global associations extracted on long sequences by the transformer‐encoder allow for model generalization to varying wear distributions. The parallel processing structure of all channels ensures complementary information is utilized minimizing unforeseen model bias. The model's performance as evaluated on experimental milling data and further comparison with other reported models on same dataset shows attainment of comparable state‐of‐art results.

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.