Title: Analysis of machining parameters effects on surface roughness: a review
Authors: A. Mahyar Khorasani; M. Reza Soleymani Yazdi; Mir Saeed Safizadeh
Addresses: Mechanical Engineering Department, Iran University of Industries and Mines (IUIM), North Kargar, 15th St., No. 86, Tehran, 1439763163, Iran. ' Imam Hussein University, Babaee Freeway, Tehran, 1659756578, Iran. ' Iran University of Science and Technology, Narmak, Tehran, 1684613114, Iran
Abstract: 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.
Keywords: surface roughness; machining parameters; artificial intelligence; AI; milling; turning; surface quality; genetic algorithms; GAs; expert systems; artificial neural networks; ANNs.
International Journal of Computational Materials Science and Surface Engineering, 2012 Vol.5 No.1, pp.68 - 84
Received: 10 May 2011
Accepted: 23 Nov 2011
Published online: 23 Aug 2014 *