On-Line Surface Roughness Prediction by Using a Probabilistic Approach for End-Milling

Abstract

An effective and efficient methodology is proposed to predict surface roughness by online monitoring of surface quality using accelerometer signals. A probabilistic approach, Monte Carlo Simulation, was researched and developed to create an automated tool for on-line prediction of surface quality. Data from 3-axis vibration (Vx, Vy, Vz) signals were used to predict on-line surface roughness. According to an experimental design with four cutting parameters (Cutting speed (Vc), Feed per teeth (Sz), Dept of cut (Dc), Width of cut (Wc)), three-axis vibration signals were used to combine data into a probabilistic model for development of an on-line surface roughness prediction system. Once the probability model was established by using a data set consisting of 71 experiments, the model was tested for 10 different cutting conditions. The probability model shows that the results have convergence values that are close to each other, by as high as 96.37%.