Analysis of machining parameters effects on surface roughness: a review

This paper aims at presenting the effective parameters on surface roughness in machining process. Many investigations have been carried out on the offline or online machining parameters estimation. Both the optical and artificial intelligence (AI) techniques have been investigated to achieve the objective. AI-based methods which are divided into three branches, including the genetic algorithms (GA), knowledge-based expert systems, and artificial neural networks (ANN), have been discussed. Online or real time AI based systems for monitoring the machine surface roughness have also been described. The effective machining parameters have been categorised into six main branches: such as machine tool, workpiece, tool properties, cutting, thermal and dynamic parameters.