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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%.

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.

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.

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.

The process of high-speed and high-precision machining is highly dependent on the wear process of the tool, so it is necessary to obtain the accurate identification of the wear state of the tool and the timely prediction of the degradation trend through effective detection methods. However, the influencing factors of the real cutting process are complex and variable. The milling process is a complex spatial deformation process, accompanied by very high cutting accuracy requirements, and it is hard to use VB value, the currently popular singular linear indicator of wear state evaluation, to describe the current wear state of end mills precisely and difficult to make a valid decision proof for subsequent dynamic degradation trends. For these issues, a three-dimensional wear region reconstruction and calculation method for end milling cutters based on bi-sensor monitoring information is proposed in this paper, which can be accurately used to estimate the volume of current wear areas and give more reasonable predictions of future wear trends. Firstly, an in situ wear detection device with a bi-sensor based on an industrial camera and line laser scanner is designed, which can obtain the wear shape and depth information synchronously with high precision. Secondly, aiming at the problem of missing wear information due to uneven furring wear on the rear tool face of the end milling tool, this paper proposes a combined threshold segmentation method to extract the complete tool wear region, which improves the calculation precision of the wear region. Thirdly, the SFS algorithm, which integrates high-precision scale information from line laser data, is utilized to reconstruct the rear tool surface topography. This reconstruction allows for accurate estimation of the wear volume. Finally, the experiment results have shown that the wear volume can reflect the wear state of the tool more quickly and comprehensively compared with the traditional tool wear width standard, and it can provide early warning before the tool enters severe wear condition.

Tool wear has become an extremely serious issue in drilling of particle reinforced metal matrix composite (MMCp) due to the existence of the hard abrasive particles, which drastically decreases tool life and deteriorates the mechanical performance of the machined component. However, there is no available research on studying the tool wear modeling in drilling of MMCp. The existing tool wear models mainly focus on the machining of homogeneous materials and almost ignore the chisel edge structure of the drill tool, which cannot be applied to MMCp. In this study, a new tool wear rate model, considering abrasive particle characteristics, tool wear mechanisms, and tool’s geometrical structure, is proposed to describe the complete tool wear topography in drilling of MMCp for the first time. Drilling experiments of SiCp/Al composites were performed to validate the proposed model, and two existing models were introduced to make a comparison. The results show that the proposed model can effectively predict the tool wear rate of major cutting edge and chisel edge, and present a more accurate prediction than the existing models. Moreover, an accurate depiction of the tool wear topography is achieved by combining the proposed tool wear rate model and tool geometry. The research provides important guidance in improving tool life and machining quality in machining of MMCp.