In-process tool condition monitoring in compliant abrasive belt grinding process using support vector machine and genetic algorithm

• Main Authors: Pandiyan, Vigneashwara, Caesarendra, Wahyu, Tjahjowidodo, Tegoeh, Tan, Hock Hao
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2019
• Subjects: Abrasive Belt Grinding; GA; DRNTU::Engineering::Mechanical engineering
• Online Access: https://hdl.handle.net/10356/105905; http://hdl.handle.net/10220/48056
• Institution: Nanyang Technological University

Industrial interest in tool condition monitoring for compliant coated abrasives has significantly augmented in recent years as unlike other abrasive machining processes the grains are not regenerated. Tool life is a significant criterion in coated abrasive machining since deterioration of abrasive grains increases the surface irregularity and adversely affects the finishing quality. Predicting tool life in real time for coated abrasives not only helps to optimise the utilisation of the tool’s life cycle but also secures the surface quality of finished components. This paper describes the evolution of the abrasive grain degradation in the belt tool with process time and also the development of Support Vector Machine (SVM) and Genetic Algorithm (GA) based predictive classification model for in-process sensing of abrasive belt wear for robotized abrasive belt grinding process. With this tool condition monitoring predicting system, the effectiveness of the belt and the surface integrity of the material is secure. The analysis of sensor signals generated by the accelerometer, Acoustic Emission (AE) sensor and force sensor during machining is proposed as a technique for detecting belt tool life states. Various time and frequency domain features are extracted from sensor signals obtained from the accelerometer, acoustic sensor and force sensor mounted on the belt grinding setup. The time and frequency domain features extracted from the signals are simultaneously optimised to obtain a subset with fewer input features using a GA. The classification accuracy of the k-Nearest Neighbour (kNN) technique is used as the fitness function for the GA. The subset features extracted from the signals are used to train the SVM in MATLAB. An experimental investigation using four different conditions of tool states is introduced to the SVM and GA for the prediction and classification. By the experimental results, this research proves that the proposed SVM based in-process tool condition monitoring model has a high accuracy rate for predicting abrasive belt condition states.