Optimizing CNC turning process parameter to reduce surface roughness of aluminium alloy 6061 in wet and dry conditions

Manivanan, Logeswaran (2017) Optimizing CNC turning process parameter to reduce surface roughness of aluminium alloy 6061 in wet and dry conditions. Project Report. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia. (Submitted)

	Text (24 Pages) Optimizing CNC Turning Process Parameter To Reduce Surface Roughness Of Aluminium Alloy 6061 In Wet And Dry Conditions - Logeswaran Manivanan - 24 pages.pdf - Submitted Version Download (561kB)
	Text (Full Text) Optimizing CNC turning process parameter to reduce surface roughness of aluminium alloy 6061 in wet and dry conditions.pdf Restricted to Repository staff only Download (1MB)

Abstract

In this project, the Taguchi method was used to find the optimal CNC turning process parameters to reduce surface roughness of aluminium alloy 6061. This is because low surface roughness is essential for high quality automotive parts. The main controllable turning parameters that have been investigated in this project are cutting speed, feed and depth of cut. The parameter levels of cutting speed used in this project were 250, 300, and 350 m/min. Feed levels that was used in this project were 0.10, 0.15, and 0.20 mm/rev. The parameter levels of depth of cut were 0.30, 0.50, 0.70 mm. The main scope of this project is to achieve surface roughness of N5 grade which is around 0.4-0.8 μm. This range of surface roughness value is considered as a finishing surface. The objectives of this project was to investigate effect of cutting parameters on surface roughness, to compare the effect of wet and dry machining on surface roughness and to validate the optimal cutting parameter. The cutting tool insert used in this project was an uncoated carbide insert, VCGT 160408N-AW. The work material that was used is aluminum alloy 6061. The cutting fluid employed in the project was water soluble cutting oil. The surface roughness reading was taken using portable surface roughness tester, SJ-310. Analysis was performed using experimental result, main effect analysis, ANOVA, and regression model. The findings of these analysis were that feed is the most significant factor while other factors are insignificant. Other than that, the machining condition also found to have minimal influence on surface roughness with wet machining giving the better result. The optimal parameters were found to be 300 m/min cutting speed, 0.1 mm/rev feed, and 0.5 mm depth of cut for dry machining. While, the optimal parameters were found to be 300 m/min cutting speed, 0.1 mm/rev feed, and 0.3 mm depth of cut for wet machining. Both of these optimal conditions were validated with percentage deviation of 4.2% and 7.5% for dry and wet machining respectively. This project also proposed two regression models to predict the surface roughness in dry and wet machining conditions with percentage deviation of less than 10%.

Actions (login required)

View Item