An In-Process Neural Network-Based Surface Roughness Prediction (INN-SRP) System Using a Dynamometer in End Milling Operations

Abstract

Surface roughness is influenced by the machining parameters and other uncontrollable factors resulting from the cutting tool in end milling operations. To perform the in-process surface roughness prediction (ISRP) system accurately, the uncontrollable factors must be monitored. In this paper, an empirical approach using a statistical analysis was employed to discover the proper cutting force to represent the uncontrollable factors in end milling operations. Furthermore, an in-process neural network-based surface roughness prediction (INN-SRP) system was developed. A neural network associated with sensing technology was applied as a decision-making system to predict the surface roughness for a wide range of machining parameters. The good accuracy of the results for a wide range of machining parameters indicates that the system is suitable for application in industry.