Effects of Machining Parameters on the Surface Roughness of Al-Si alloy

• B. V. Omidiji

  . Department of Mechanical Engineering Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria

• H. A. Owolabi

  . Department of Mechanical Engineering Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria

• T. A. Morakinyo

  2. Department of Food Science and Technology, ObafemiAwolowo University, Ile-Ife, Nigeria

• A. Olukoga

  . Department of Mechanical Engineering Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria

The effects of machining parameters, on surface roughness of aluminium alloy machined using face turning were investigated in this research. Taguchi's approach to design of experiment was employed to determine the number of runs of experiments. Three significant factors; cutting speed, depth of cut and feed rate, were taken as machining parameters at three levels to determine their combined and interaction effects on the surface roughness of the aluminium alloy machined. The experiment was designed and carried out on the basis of standard L Taguchi's 9 orthogonal array, leading to nine runs of experiments. The signal-to-noise (S/N) ratio, the analysis of variance (ANOVA) are employed to find the optimal levels and to analyze the effect of the turning parameters. It was discovered depth of cut dominantly influenced the surface roughness of the machined components by making a contribution of 67.84% and the lowest value of surface roughness was 0.6625μm.

The effects of machining parameters, on surface roughness of aluminium alloy machined using face turning were investigated in this research. Taguchi's approach to design of experiment was employed to determine the number of runs of experiments. Three significant factors; cutting speed, depth of cut and feed rate, were taken as machining parameters at three levels to determine their combined and interaction effects on the surface roughness of the aluminium alloy machined. The experiment was designed and carried out on the basis of standard L Taguchi's 9 orthogonal array, leading to nine runs of experiments. The signal-to-noise (S/N) ratio, the analysis of variance (ANOVA) are employed to find the optimal levels and to analyze the effect of the turning parameters. It was discovered depth of cut dominantly influenced the surface roughness of the machined components by making a contribution of 67.84% and the lowest value of surface roughness was 0.6625μm.

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