Multi-Objective Process Parameter Optimization for Surface Roughness and Material Removal Rate in Face Milling of Al 6061 T6 Alloy
Atla Sridhar1, K.Prasanna Lakshmi2
1Atla Sridhar, Mechanical Engineering Department, GITAM Deemed to be University Hyderabad, India.
2K.Prasanna Lakshmi, Mechanical Engineering Department, JNTUH CE Manthani, India.
Manuscript received on November 14, 2019. | Revised Manuscript received on 22 November, 2019. | Manuscript published on December 10, 2019. | PP: 137-142 | Volume-9 Issue-2, December 2019. | Retrieval Number: A5251119119/2019©BEIESP | DOI: 10.35940/ijitee.A5251.129219
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© 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: This study uses Taguchi methodology and Gray Relational Analysis approach to explore the optimization of face milling process parameters for Al 6061 T6 alloy. Surface Roughness (Ra), Material Removal Rate (MRR) has been identified as the objective of performance and productivity. The tests were performed by selecting cutting speed (mm / min), feed rate (mm / rev) and cutting depth (mm) at three settings on the basis of Taguchi’s L9 orthogonal series. The grey relational approach was being used to establish a multi objective relationship between both the parameters of machining and the characteristics of results. To find the optimum values of parameters in the milling operation, the response list and plots are used and found to be Vc2-f1-d3. To order to justify the optimum results, the confirmation tests are performed.The machining process parameters for milling were thus optimized in this research to achieve the combined goals such as low surface roughness and high material removal rate on Aluminum 6061 t6.It was concluded that depth of cut is the most influencing parameter followed by feed rate and cutting velocity.
Keywords: Greyrelation Analysis, Milling, Metal Removal Rate, and Surface Roughness..
Scope of the Article: Discrete Optimization