Multi Response Optimization of Face Milling Parameters Using Taguchi Method and Analysis of variance
Ahmed, M., Mansour, E., Lashin, M., Gaafer, A. (2020). Multi Response Optimization of Face Milling Parameters Using Taguchi Method and Analysis of variance. EKB Journal Management System, 44(1), 59-64. doi: 10.21608/erjsh.2020.405703
M. M. Ahmed; E. H. Mansour; M. M. Lashin; A. M. Gaafer. "Multi Response Optimization of Face Milling Parameters Using Taguchi Method and Analysis of variance". EKB Journal Management System, 44, 1, 2020, 59-64. doi: 10.21608/erjsh.2020.405703
Ahmed, M., Mansour, E., Lashin, M., Gaafer, A. (2020). 'Multi Response Optimization of Face Milling Parameters Using Taguchi Method and Analysis of variance', EKB Journal Management System, 44(1), pp. 59-64. doi: 10.21608/erjsh.2020.405703
Ahmed, M., Mansour, E., Lashin, M., Gaafer, A. Multi Response Optimization of Face Milling Parameters Using Taguchi Method and Analysis of variance. EKB Journal Management System, 2020; 44(1): 59-64. doi: 10.21608/erjsh.2020.405703

Multi Response Optimization of Face Milling Parameters Using Taguchi Method and Analysis of variance

Engineering Research Journal (Shoubra)
Volume 44, Issue 1, April 2020, Page 59-64  XML PDF (757.94 K)
Document Type: Research articles
DOI: 10.21608/erjsh.2020.405703
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Authors
M. M. Ahmed; E. H. Mansour; M. M. Lashin; A. M. Gaafer
Mechanical Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt.
Abstract
This work introduces the application of Taguchi method and analysis of variance (ANOVA) method in evaluating and optimizing the machining parameters of face milling for machining aluminum silicon reinforced with hybrid multi walled carbon nano tubes (MWCNTs) and nano aluminum oxide particulates (AL2O3). The conventional vertical milling machine was used to carry out the experiments based on Taguchi orthogonal L27 array. The type of nanofiller (0.50hybrid and 0.50MWCNTs), spindle speed, feed rate and depth of cut were considered as machining parameters. Surface roughness, flatness error and material removal rate were selected as a process response. The effect of various machining parameters was analyzed and the optimum combination of their levels of them were determined using both S/N ratio and ANOVA. The results of this work indicated that, the optimal levels of machining parameters are determined from main effects plot for SN ratios for different responses, for MRR the optimal combination levels are A3 B3C3D3, for surface roughness A2B3C3D3 while for flatness error A3B3C3D3. The most significant parameter on MRR was feed rate followed by depth of cut. Hybrid nano% was the most significant parameter on surface roughness followed by depth of cut, MWCNTs nano % was the most significant parameter on flatness error followed by spindle speed.
Keywords
Face milling; Nanocomposites; Surface roughness; Flatness error; Taguchi
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