Title: Review of empirical modelling techniques for modelling of turning process

Authors: Akhil Garg; Yogesh Bhalerao; Kang Tai

Addresses: School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore ' Department of Mechanical Engineering, MIT Academy of Engineering (MAE), Pune MH 412105, India ' School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore

Abstract: The most widely and well known machining process used is turning. The turning process possesses higher complexity and uncertainty and therefore several empirical modelling techniques such as artificial neural networks, regression analysis, fuzzy logic and support vector machines have been used for predicting the performance of the process. This paper reviews the applications of empirical modelling techniques in modelling of turning process and unearths the vital issues related to it.

Keywords: empirical modelling, turning; artificial neural networks; ANNs; review; regression analysis; genetic programming; fuzzy logic; support vector machines; SVM.

DOI: 10.1504/IJMIC.2013.056184

International Journal of Modelling, Identification and Control, 2013 Vol.20 No.2, pp.121 - 129

Available online: 16 Aug 2013 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article