A Novel Machine Learning-Based Methodology for Tool Wear Prediction Using Acoustic Emission Signals

Abstract

There is an increasing trend in the industry of knowing in real-time the condition of their assets. In particular, tool wear is a critical aspect, which requires real-time monitoring to reduce costs and scrap in machining processes. Traditionally, for the purpose of predicting tool wear conditions in machining, mathematical models have been developed to extract the information from the signal of sensors attached to the machines. To reduce the complexity of developing physical models, where an in-depth knowledge of the system being modelled is required, the current trend is to use machine-learning (ML) models based on data from the tool wear. The acoustic emission (AE) technique has been widely used to capture data from and understand the real-time condition of industrial assets such as cutting tools. However, AE signal interpretation and processing is rather complex. One of the most common features extracted from AE signals to predict the tool wear is the counts parameter, defined as the number of times that the amplitude of the signal exceeds a predefined threshold. A recurrent problem of this feature is to define the adequate threshold to obtain consistent wear prediction. Additionally, AE signal bandwidth is rather wide, and the selection of the optimum frequencies band for feature extraction has been pointed out as critical and complex by many authors. To overcome these problems, this paper proposes a methodology that applies multi-threshold count feature extraction at multiresolution level using wavelet packet transform, which extracts a redundant and non-optimal feature map from the AE signal. Next, recursive feature elimination is performed to reduce and optimize the vast number of predicting features generated in the previous step, and random forests regression provides the estimated tool wear. The methodology presented was tested using data captured when turning 19NiMoCr6 steel under pre-established cutting conditions. The results obtained were compared with several ML algorithms such as k-nearest neighbors, support vector machines, artificial neural networks and decision trees. Experimental results show that the proposed method can reduce the predicted root mean squared error by 36.53%.

Tool Condition Monitoring for High-Performance Machining Systems—A Review

“…This domain has attracted considerable attention for TCM systems compared to the aforementioned domains [ 2 , 18 , 25 ]. Time–frequency representation of the acquired data is constructed using the continuous wavelet transform (CWT), discrete wavelet transform (DWT), wavelet packet transform (WPT), short-time Fourier transform (STFT), or empirical mode decomposition (EMD) algorithms [ 29 , 34 , 145 , 146 ]. Extracted features include the average energy of wavelet coefficients and their wavelet domain statistics (RMS, mean, and variance, etc.)…”

Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network

“…The deformation, fracture and phase change of solid materials cause the rapid release of strain energy, and acoustic emission is the stress elastic wave. Thus, acoustic emission features with a higher amplitude can be monitored when the tool is broken [64][65][66]. Acoustic emission is not subject to mechanical interference and propagates much higher than the characteristic frequency caused by machining.…”

Utilising Machine Learning for Tool Condition Monitoring of Diamond-Coated Burrs with Acoustic Emission

“…AE is a well-studied Non-Destructive Testing (NDT) method which has been utilised in a large range of applications. Recently it has been used to monitor the wear of tools during turning [10], milling [11], and grinding [12][13][14]. The AE technique utilises the phenomenon in which a material experiences a permanent change from damage, whether it be crack propagation, delamination, plastic deformation or corrosion [15,16], energy is spontaneously released in the form of elastic waves.…”