In-process surface roughness estimation model for compliant abrasive belt machining process

• Main Authors: Pandiyan, Vigneashwara, Tjahjowidodo, Tegoeh, Samy, Meena Periya
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2018
• Subjects: In-process Measurement; Roughness; DRNTU::Engineering::Mechanical engineering
• Online Access: https://hdl.handle.net/10356/89656; http://hdl.handle.net/10220/47107
• Institution: Nanyang Technological University

Surface roughness inspection in robotic abrasive belt machining process is an off-line operation which is time-consuming. An in-process multi-sensor integration technique comprising of force, accelerometer and acoustic emission sensor was developed to predict state of the surface roughness during machining. Time and frequency-domain features extracted from sensor signals were correlated with the corresponding surface roughness to train the Support vector machines (SVM's) in Matlab toolbox and a classification model was developed. Prediction accuracy of the classification model shows proposed in-process surface roughness recognition system can be integrated with abrasive belt machining process for capping lead-time and is reliable.