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This review presents a comprehensive examination of recent advancements in the modelling and monitoring of cutting tool life, emphasizing its critical role in enhancing manufacturing efficiency and cost-effectiveness. The paper discusses the primary wear mechanisms, such as abrasive, adhesive, diffusive, and chemical wear. Traditional and modern predictive models, including Taylor’s, Colding’s, and Usui’s tool life models, are evaluated. The review also covers a range of modelling approaches from empirical to numerical and analytical methods, alongside cutting tool monitoring techniques. The paper concludes by identifying future research directions, hybrid models that combine empirical and analytical techniques, and the creation of comprehensive datasets. The goal is to provide practitioners and researchers with insights into the next wave of innovations in tool life optimization, fostering advancements in adaptive self-learning tool performance predictive systems and integrated monitoring technologies.

In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, considering the noise, varying length and irregular sampling behind sensory data, this kind of sequential data cannot be fed into classification and regression models directly. Therefore, previous work focuses on feature extraction/fusion methods requiring expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, which redefine representation learning from raw data, a deep neural network structure named Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM) has been designed here to address raw sensory data. CBLSTM firstly uses CNN to extract local features that are robust and informative from the sequential input. Then, bi-directional LSTM is introduced to encode temporal information. Long Short-Term Memory networks(LSTMs) are able to capture long-term dependencies and model sequential data, and the bi-directional structure enables the capture of past and future contexts. Stacked, fully-connected layers and the linear regression layer are built on top of bi-directional LSTMs to predict the target value. Here, a real-life tool wear test is introduced, and our proposed CBLSTM is able to predict the actual tool wear based on raw sensory data. The experimental results have shown that our model is able to outperform several state-of-the-art baseline methods.

Tool wear monitoring is crucial during machining of difficult-to-cut materials to save cost and improve efficiency. In this paper, a tool wear–monitoring strategy was proposed for milling of titanium alloy Ti–6Al–4 V under inner minimum quantity lubrication (MQL) conditions. Unlike the usual categorization method, tool wear was categorized into four states based on tool wear mechanism, tool wear rate, and tool life. Thus, more detailed information of tool could be predicted for tool wear monitoring. Cutting forces and acoustic emission were measured online as raw datasets. Statistical features were extracted from time and frequency domain, and mutual information (MI) was used for feature selection. Then, linear discriminant analysis (LDA) was adopted for dimensionality reduction and finding the optimal datasets for training. At last, ν-Support vector machine (ν-SVM) was applied for training and prediction. The proposed strategy had a prediction accuracy of 98.9%, which could be considered as valid and useful for tool wear monitoring.

Managing tool-wear is an important issue associated with all material removal processes. This paper deals with the application of two nature-inspired computing techniques, namely, artificial neural network (ANN) and (in silico) DNA-based computing (DBC) for managing the tool-wear. Experimental data (images of worn-zone of cutting tool) has been used to train the ANN and, then, to perform the DBC. It is demonstrated that the ANN can predict the degree of tool-wear from a set of tool-wear images processed under a given procedure whereas the DBC can identify the degree of similarity/dissimilar among the processed images. Further study can be carried out while solving other complex problems integrating ANN and DBC where both prediction and pattern-recognition are two important computational problems that need to be solved simultaneously.

Tool wear monitoring has been increasingly important in intelligent manufacturing to increase machining efficiency. Multi-domain features can effectively characterize tool wear condition, but manual feature fusion lowers monitoring efficiency and hinders the further improvement of predicting accuracy. In order to overcome these deficiencies, a new tool wear predicting method based on multi-domain feature fusion by deep convolutional neural network (DCNN) is proposed in this paper. In this method, multi-domain (including time-domain, frequency domain and time–frequency domain) features are respectively extracted from multisensory signals (e.g. three-dimensional cutting force and vibration) as health indictors of tool wear condition, then the relationship between these features and real-time tool wear is directly established based on the designed DCNN model to combine adaptive feature fusion with automatic continuous prediction. The performance of the proposed tool wear predicting method is experimentally validated by using three tool run-to-failure datasets measured from three-flute ball nose tungsten carbide cutter of high-speed CNC machine under dry milling operations. The experimental results show that the predicting accuracy of the proposed method is significantly higher than other advanced methods.

Tool condition monitoring (TCM) has become essential to achieve high-quality machining as well as cost-effective production. Identification of the cutting tool state during machining before it reaches its failure stage is critical. This paper presents a novel big data approach for tool wear classification based on signal imaging and deep learning. By combining these two techniques, the approach is able to work with the raw data directly, avoiding the use of statistical pre-processing or filter methods. This aspect is fundamental when dealing with large amounts of data that hold complex evolving features. The imaging process serves as an encoding procedure of the sensor data, meaning that the original time series can be re-created from the image without loss of information. By using an off-the-shelf deep learning implementation, the manual selection of features is avoided, thus making this novel approach more general and suitable when dealing with large datasets. The experimental results have revealed that deep learning is able to identify intrinsic features of sensory raw data, achieving in some cases a classification accuracy above 90%.

The need to monitor tool wear is crucial, particularly in advanced manufacturing industries, as it aims to maximise the lifespan of the cutting tool whilst guaranteeing the quality of workpiece to be manufactured. Although there have been many studies conducted on monitoring the health of cutting tools under a specific cutting condition, the monitoring of tool wear across multi-cutting conditions still remains a challenging proposition. In addressing this, this paper presents a framework for monitoring the health of the cutting tool, operating under multi-cutting conditions. A predictive model, using advanced machine learning methods with multi-feature multi-model ensemble and dynamic smoothing scheme, is developed. The applicability of the framework is that it takes into account machining parameters, including depth of cut, cutting speed and feed rate, as inputs into the model, thus generating the key features for the predictions. Real data from the machining experiments were collected, investigated and analysed, with prediction results showing high agreement with the experiments in terms of the trends of the predictions as well as the accuracy of the averaged root mean squared error values.

In the actual mechanical processing of difficult-to-process materials, normal or abnormal tool wear can lead to processing pauses or terminations, which seriously affects the processing accuracy and efficiency of workpieces, leading to workpiece scrapping. Therefore, predicting and monitoring tool wear during the actual machining process plays a crucial role in controlling tool costs and avoiding workpiece losses caused by tool wear. This paper proposed a tool wear prediction model based on power signals, which predicts tool wear by establishing a mapping between power signals and tool wear. Through drilling experiments for model calibration and validation, we verified that the proposed model can effectively predict tool wear under different parameters. In addition, based on the established prediction model, a real-time monitoring method for tool wear using power signals was proposed and implemented. Through experiments, it has been proven that the proposed method is suitable for monitoring normal and abnormal tool wear in actual machining.

PurposeThe aim of this paper is to review the literature on the prediction of cutting tool life. Tool life is typically estimated by predicting the time to reach the threshold flank wear width. The cutting tool is a crucial component in any machining process, and its failure affects the manufacturing process adversely. The prediction of cutting tool life by considering several factors that affect tool life is crucial to managing quality, cost, availability and waste in machining processes.Design/methodology/approachThis study has undertaken the critical analysis and summarisation of various techniques used in the literature for predicting the life or remaining useful life (RUL) of the cutting tool through monitoring the tool wear, primarily flank wear. The experimental setups that comprise diversified machining processes, including turning, milling, drilling, boring and slotting, are covered in this review.FindingsCutting tool life is a stochastic variable. Tool failure depends on various factors, including the type and material of the cutting tool, work material, cutting conditions and machine tool. Thus, the life of the cutting tool for a particular experimental setup must be modelled by considering the cutting parameters.Originality/valueThis submission discusses tool life prediction comprehensively, from monitoring tool wear, primarily flank wear, to modelling tool life, and this type of comprehensive review on cutting tool life prediction has not been reported in the literature till now. The future suggestions provided in this review are expected to provide avenues to solve the unexplored challenges in this field.

An accurate prediction of the machining tool condition during the cutting process is crucial for enhancing the tool life, improving the production quality and productivity, optimizing the labor and maintenance costs, and reducing workplace accidents. Currently, tool condition monitoring is usually based on machine learning algorithms, especially deep learning algorithms, to establish the relationship between sensor signals and tool wear. However, deep mining of feature and fusion information of multi-sensor signals, which are strongly related to the tool wear, is a critical challenge. To address this issue, in this study, an integrated prediction scheme is proposed based on deep learning algorithms. The scheme first extracts the local features of a single sequence and a multi-dimensional sequence from DenseNet incorporating a heterogeneous asymmetric convolution kernel. To obtain more perceptual historical data, a “dilation” scheme is used to extract features from a single sequence, and one-dimensional dilated convolution kernels with different dilation rates are utilized to obtain the differential features. At the same time, asymmetric one-dimensional and two-dimensional convolution kernels are employed to extract the features of the multi-dimensional signal. Ultimately, all the features are fused. Then, the time-series features hidden in the sequence are extracted by establishing a depth-gated recurrent unit. Finally, the extracted in-depth features are fed to the deep fully connected layer to achieve the mapping between features and tool wear values through linear regression. The results indicate that the average errors of the proposed model are less than 8%, and this model outperforms the other tool wear prediction models in terms of both accuracy and generalization.