Authors: Gurusamy Selvakumar; Shanmuga Sundaram Ram Prakash; Nagarajan Lenin
Addresses: Department of Mechanical Engineering, SSN College of Engineering, Old Mahabalipuram Road, Kalavakkam, 603 110, Chennai, Tamilnadu, India ' Department of Mechanical Engineering, SSN College of Engineering, Old Mahabalipuram Road, Kalavakkam, 603 110, Chennai, Tamilnadu, India ' Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, 600 062, Tamilnadu, India
Abstract: The objective of this study is to present the optimal machining parameters for abrasive water jet machining (AWJM) of Aluminium alloy 5083 (AA5083) by employing artificial neural networks (ANN) modelling for various material thicknesses. Al 5083 alloy finds vast applications in ship building, rail cars, and vehicle bodies and exclusively for cryogenic applications. The experimental work was carried out by using Taguchi L18 orthogonal array to study the influence of the process parameters such as jet diameter, stand-off distance and abrasive flow rate for various ranges of thicknesses over the process yields namely material removal rate (mrr), surface roughness and taper error. Technological table for optimal machining of AA5083 alloy in AWJM was reported for ready to use in industry.
Keywords: abrasive water jet machining; AWJM; aluminium alloy; Taguchi method; artificial neural networks; ANN; Pareto optimisation; technology table.
International Journal of Abrasive Technology, 2018 Vol.8 No.3, pp.218 - 231
Received: 18 Sep 2017
Accepted: 08 Apr 2018
Published online: 03 Aug 2018 *