Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition

Abstract

Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA) is proposed. PCA allowed to identify a smaller number of features (k = 2 features), the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear (VBmax) was achieved, with predicted values very close to the measured tool wear values.

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

“…Stief et al [33] proposed a sensor fusion approach to diagnose both electrical and mechanical faults in induction motors based on the combination of a two-stage Bayesian method and PCA. Caggiano [34] also proposed an advanced feature extraction methodology based on PCA. By introducing artificial neural networks to the PCA features, an accurate diagnosis of tool flank wear was achieved, with predicted values being very close to the measured tool wear values.…”

A study to determine the onset of catastrophic wear of a processing tool by statistical parameters of acoustic emission

“…Processing the set of new data helps classify the status of the cutting tool or the wear state of the cutting tool. The use of the principal component analysis in machining materials to assess the wear of a cutting tool has also been considered in a number of other studies -in the processing of Inconel 718 alloy [16], normalised steel [17], and Ti-6Al-4V alloy [18].…”

Vibration-Based Structural Health Monitoring Using Piezoelectric Transducers and Parametric t-SNE

“…Dimensionality reduction is very important, because it alleviates undesired properties of high-dimensional spaces, such as "the curse of dimensionality" [5]. In the literature, various dimensionality reduction methods have been proposed: (i) linear methods, such as principal component analysis (PCA) [6,7] and linear discriminant analysis (LDA) [8,9], and (ii) nonlinear methods, such as isometric mapping (ISOMAP) [10,11] and the non-parametric version of t-distributed stochastic neighbor embedding (t-SNE) [12].…”