TY - GEN
T1 - The Optimization of the Surface Roughness of Milled Polypropylene + 60wt.% Quarry Dust Composite Using the Taguchi Technique
AU - Shagwira, Harrison
AU - Mwema, F. M.
AU - Obiko, J. O.
AU - Mbuya, T. O.
AU - Akinlabi, E. T.
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2021/7/6
Y1 - 2021/7/6
N2 - This study is based on the optimization of the parameters that influence the computer numerical control (CNC) milling operation during the machining of polypropylene+60wt.% quarry dust composite. The input parameters studied are the cutting speed, the feed rate and the depth of cut. These input parameters were optimized using the Taguchi optimization technique with the output response taken into consideration was the surface roughness. An L9 orthogonal array (OA) was selected and formulated in a commercial software Minitab 19 based on three factors and three levels combination. The signal-to-noise (S/N) ratio was analysed to give a combination of values of the input parameters that produced optimum results for surface roughness. The analysis of variance (ANOVA) was then conducted to determine the significance and percentage contribution of each parameter. From the results, the optimum values obtained were cutting speed of 1000 rpm, feeding rate of 120 mm/min and depth of cut of either 0.5 mm or 0.8 mm. The cutting speed had the highest contribution towards the surface roughness at 81.98%, followed by the depth of cut at 7.43% and the feed rate having the least contribution at 3.69%.
AB - This study is based on the optimization of the parameters that influence the computer numerical control (CNC) milling operation during the machining of polypropylene+60wt.% quarry dust composite. The input parameters studied are the cutting speed, the feed rate and the depth of cut. These input parameters were optimized using the Taguchi optimization technique with the output response taken into consideration was the surface roughness. An L9 orthogonal array (OA) was selected and formulated in a commercial software Minitab 19 based on three factors and three levels combination. The signal-to-noise (S/N) ratio was analysed to give a combination of values of the input parameters that produced optimum results for surface roughness. The analysis of variance (ANOVA) was then conducted to determine the significance and percentage contribution of each parameter. From the results, the optimum values obtained were cutting speed of 1000 rpm, feeding rate of 120 mm/min and depth of cut of either 0.5 mm or 0.8 mm. The cutting speed had the highest contribution towards the surface roughness at 81.98%, followed by the depth of cut at 7.43% and the feed rate having the least contribution at 3.69%.
UR - http://www.scopus.com/inward/record.url?scp=85112284468&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-3641-7_20
DO - 10.1007/978-981-16-3641-7_20
M3 - Conference contribution
AN - SCOPUS:85112284468
SN - 9789811636400
SN - 9789811636431
T3 - Lecture Notes in Mechanical Engineering (LNME)
SP - 169
EP - 174
BT - Advances in Material Science and Engineering
A2 - Awang, Mokhtar
A2 - Emamian, Seyed Sattar
PB - Springer
CY - Singapore, Asia
T2 - 6th International Conference on Mechanical, Manufacturing and Plant Engineering, ICMMPE 2020
Y2 - 25 November 2020 through 26 November 2020
ER -