Abstract
This paper presents a neural network approach to estimate the surface roughness by considering the relationship between the polishing operation parameters and the surface roughness. The neural network model predicts the post-machining surface roughness by using several factors such as pre-machining surface roughness, pressure, feed rate, spindle speed, and the number of polishing as inputs. In this paper, the several neural network models are implemented to estimate the surface roughness by using actual experimental data. The experimental results show that the neural network approach is more appropriate to represent the polishing characteristics of mold and die compared with the results obtained by the approach using exponential function.
