PSO Research on Cutting Parameters in AWJM Process for Aluminum 6061 Alloy
K. S. Jai Aultrin1, M. Dev Anand2, R. Rajesh3, S. Muthu Sherin4
1K. S. Jai Aultrin, Associate Professor, Department of Mechanical Engineering, Noorul Islam Centre for Higher Education, Kumaracoil (Tamil Nadu), India.
2M. Dev Anand, Professor, Department of Mechanical Engineering, Noorul Islam Centre for Higher Education, Kumaracoil (Tamil Nadu), India.
3R. Rajesh, Associate Professor, Department of Mechanical Engineering, Noorul Islam Centre for Higher Education, Kumaracoil (Tamil Nadu), India.
4S. Muthu Sherin, Assistant Professor, Department of Aerospace Engineering, Noorul Islam Centre for Higher Education, Kumaracoil (Tamil Nadu), India.
Manuscript received on 16 July 2019 | Revised Manuscript received on 01 August 2019 | Manuscript Published on 10 August 2019 | PP: 64-71 | Volume-8 Issue-2S3 July 2019 | Retrieval Number: B10110782S319/2019©BEIESP | DOI: 10.35940/ijrte.B1011.0782S319
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In recent years there is a rapid growth in the improvement of complexity, difficult and harder to machine metals and alloys. AWJM is one of the hybrid, nontraditional machining process in machining several hard-to-cut materials these days. Machining parameters play the lead role in determining the machine economics and quality of machining. In this study Particle Swarm Optimization soft computing technique is executed to estimate the optimal process parameters which leads to a least value of machining performance and compared with the machining performance value of experimental data. The approaches suggested in this study involve three components, viz., experimental observation, multi regression modeling and single objective Particle Swarm Optimization. The consequence of Pressure, Abrasive flow rate, Orifice diameter, Focusing nozzle diameter and Stand off distance AWJM process parameters on MRR and SR of Aluminium 6061 alloy which is machined by AWJM was experimentally performed and analyzed. According to Response Surface Methodology design, different experiments were conducted with the combination of input parameters on this alloy. The outcome of this study revealed that the PSO soft computing technique obtains the optimal solution of AWJM process parameters for Aluminium 6061 alloy.
Keywords: Response Surface Methodology, Particle Swarm Optimization, Material Removal Rate, Surface Roughness.
Scope of the Article: Manufacturing Processes