Publication Type : Journal Article
Publisher : Tribology in Industry,
Source : Tribology in Industry, Volume 42, Issue 1 (2020)
Url : http://www.tribology.rs/journals/2020/2020-1/2020-1-03.html
Keywords : ADC12 Aluminium alloy, analysis of variance, Material removal rate, Signal to noise ratio, Surface roughness, Taguchi technique, Tool wear
Campus : Coimbatore
School : School of Engineering
Department : Mechanical Engineering
Year : 2020
Abstract : This paper presents the optimization of cutting parameters for MRR, tool wear and surface roughness characteristics in machining of ADC12 alloy. Milling experiments were performed on CNC Milling machine using uncoated carbide inserts. The objective is to minimize the surface roughness, tool wear and maximize the MRR. Process parameters that included speed (1000, 2000, 3000) rpm, feed (0.05, 0.075, 0.1) m/min and Depth of Cut (DOC) of (0.5, 1.0, 1.5) mm were varied for three levels to determine the test cases with the help of Taguchi's Orthogonal Array (L9), following which, surface roughness, MRR and tool wear was calculated. MRR and tool wear showed similar trends where the responses increase with speed, feed and DOC, whereas the surface roughness tends to increase with feed but lowers on increasing the speed and DOC. Minimum surface roughness and tool wear obtained was 0.25 µm and 0.001 mm respectively, whereas maximum MRR obtained was 0.1144 mm³/min. Speed had major influence on MRR and tool wear whereas, feed had highest impact on surface roughness. ANOVA results also depicted the same rankings. Regression analysis was performed to estimate the surface roughness, MRR and tool wear which was further validated using confirmation tests.
Cite this Research Publication : H. D. Kumar, Ilangovan, S., and Dr. Radhika N, “Optimization of Cutting Parameters for MRR, Tool Wear and Surface Roughness Characteristics in Machining ADC12 Piston Alloy Using DOE.”, Tribology in Industry, vol. 42, no. 1, 2020.