Indian Journal of Science and Technology
Year: 2016, Volume: 9, Issue: 13, Pages: 1-21
K. S. Jai Aultrin1 and M. Dev Anand2*
1Noorul Islam Centre for Higher Education, Kumaracoil - 629180, Thuckalay, Tamil Nadu, India; [email protected] 2Department of Mechanical Engineering, Noorul Islam Centre for Higher Education, Kumaracoil - 629180, Thuckalay, Tamil Nadu, India; [email protected]
*Author of Corresponding: M. Dev Anand Department of Mechanical Engineering, Noorul Islam Centre for Higher Education, Kumaracoil - 629 180, Thuckalay, Kanyakumari District, Tamil Nadu, India; [email protected]
Background/Objectives: Last decades have witnessed a rapid growth in the development of harder, difficult and complexity to machine metals and alloys. Abrasive Water Jet Machining (AWJM) is one of the recently developed nontraditional mechanical type hybrid machining processes in processing various kinds of hard-to-cut materials. It is an economical method for heat sensitive materials that cannot be machined by processes that produce heat while machining. Machining parameters play the lead role in determining the machine economics and quality of machining. This paper investigates the prediction of MRR and SR on Aluminum, Copper and Lead alloys using the combination of Artificial Neural Network (ANN) and Fuzzy Logic (FL). Methods/Statistical Analysis: In this study, the consequence of different AWJM process parameters on Material Removal Rate (MRR) and Surface Roughness (SR) of three nonferrous alloys namely Aluminum, Copper and Lead which are machined by AWJM was experimentally performed and analyzed. According to Response Surface Methodology (RSM) design, different experiments were conducted with the combination of input parameters on these alloys. Findings: A combinational method called as Adaptive Neuro Fuzzy Inference System provides effective knowledge based training to process parameters, to make its enhancement of process performance.
Keywords: Abrasive Water Jet Machining, Adaptive Neuro Fuzzy Inference System, Material Removal Rate, Response Surface Methodology, Surface Roughness
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