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.
International Journal of Modelling, Identification and Control, 2013 Vol.20 No.2, pp.121 - 129
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 16 Aug 2013 *