Surface Roughness Prediction using Empirical Modelling Techniques: - A Review

Abstract

Optimization of process parameters for minimum surface, along with its prediction and monitoring has long been studied, for it is one of the important indices of machining quality. This study continues to attract several researchers as development of newer work materials, tool materials, machining process, and quest for improved product quality because of increased market competition never cease to end. All the different approaches have a common aim of determining the relationships between the input- machining parameters and output-surface roughness.The empirical- AI based methods have been increasingly used for machining performance prediction due to their ability to acknowledge and address imprecision and uncertainty in the machining process, while learning from the experimental data. In this paper the different empirical AI based techniques are reviewed that employ surface roughness as a response variable for more conventional machining operations like turning, milling. The main purpose of this work is to review and re-evaluate machining process modelling literature related to surface roughness as modelling metal cutting process is highly dynamic in nature and highly interconnected to the technological developments.