A support vector machine-based online tool condition monitoring for milling using sensor fusion and a genetic algorithm

Abstract

In machining systems, the quality of the manufactured part is directly related to the condition of the tool used. Sharp tools are mostly used on the final machining pass to obtain enhanced dimensional accuracy and surface smoothness. Worn tools on the other hand are typically used for coarse machining. The operator usually makes tool assignments based on his experience, the wear levels of the tools and the type of machining task. However, this kind of operator judgment is bound to errors and may not be reliable in processes requiring high precision. Therefore, a tool condition monitoring system is highly desirable to achieve the best results in machining quality. In this study, three-axis cutting forces, torque, three-axis accelerometer and acoustic emission signals were analyzed and used for the development of an online tool condition monitoring system. Various time domain and statistical features extracted from these signals were used to train support vector machine models in a binary decision tree, which was used to predict the condition of the cutting tool. The genetic algorithm was employed for reducing the dimensionality of the feature set by selecting the features that correlates best with the tool condition. Nine experiments were carried out at different cutting conditions. Experimental results demonstrated the efficacy of the proposed scheme. The classification rates for the tool condition monitoring system before and after inclusion of the genetic algorithm step were determined as 89% and 100%, respectively.

Development of Insert Condition Classification System for CNC Lathes Using Power Spectral Density Distribution of Accelerometer Vibration Signals

“…Kaya et al used an SVM to develop a tool condition monitoring system that could acquire cutting force, cutting torque, vibration, and acoustic emission signals. This system uses the sensor fusion method to capture time-domain statistical features from the sensing signals and subsequently employs a genetic algorithm to determine the main features of the cutting tool conditions [ 22 ]. Wang et al used a v-SVM to design a tool condition monitoring system that could acquire vibration signals during the cutting process.…”

An Automatic Intelligent Diagnostic Mechanism for the Milling Cutter Wear

“…The first involves a Finite Impulse Response Filter (FIR) [13], [14] for signal decomposition after which feature extraction is done using Approximate Entropy (ApEn) [15]- [17].The second approach uses a fractional-order Chen-Lee Chaotic system [18], [19] to conduct nonlinear feature mapping and the Chaotic Dynamic Error Centroid and Chaotic Dynamic Error Maps are selected as features of status identification. Finally, the feature extraction data obtained by these two different methods are used for identification by (1) a Back Propagation Neural Network (BPNN) [20], [21], (2) a Support Vector Machine (SVM) [22], [23] and (3) a Convolutional Neural Network (CNN) [24], [25] respectively, to find the most suitable classification model and feature extraction method for signal testing.…”

Application of C-SVM Classification Algorithm to the Lighting Visual Comfort of University Classrooms

“…However, most research results are controversial or difficult to apply to practical applications. As an important algorithms in the fields of pattern recognition and machine learning, support vector machine (SVM) which is based on the theory of VC (Vapnik-Chervonenkis) dimension theory and structural risk minimization principle performs well on the nonlinear and small sample classification or regression problems, which causes scholars' continuous exploration on the optimization of this model and the practical application in recent years (Ben et al, 2012;Kaya et al, 2012;Huo and Duan, 2011). This research tries to develop a classification model using the algorithm of C-support vector machine (C-SVM) with features of illuminant parameters and labels of subjects' visual comfort level.…”