TY - JOUR
T1 - Analysis and optimization of surface quality while machining high strength aluminium alloy
AU - Manjunath Patel, G. C.
AU - Lokare, Deepak
AU - Chate, Ganesh R.
AU - Parappagoudar, Mahesh B.
AU - Nikhil, R.
AU - Gupta, Kapil
N1 - Publisher Copyright:
© 2019 Elsevier Ltd
PY - 2020/2
Y1 - 2020/2
N2 - This paper presents a systematic investigation on analysis and optimization of surface quality during turning of high strength Al 7075. In this central composite design based study, the effect of four important machining parameters such as cutting speed, feed rate, depth of cut, and tool nose radius on the surface quality characteristics namely form error i.e. cylindricity error and circularity error, and average surface roughness has been investigated. The material removal rate is also considered as an important response parameter. The analysis of variance study has been conducted to find the statistically significant parameters. The derived empirical relationships predicted the randomly generated test cases with less than 8.51%. Optimization for multiple outputs with conflicting requirements has been done using the principal component analysis and JAYA algorithm that resulted in the absolute deviation of 7.97% with average roughness- 0.64 µm, circularity error- 4.34 µm, cylindricity error- 0.365 µm, and material removal rate-28.63 cm3/min.
AB - This paper presents a systematic investigation on analysis and optimization of surface quality during turning of high strength Al 7075. In this central composite design based study, the effect of four important machining parameters such as cutting speed, feed rate, depth of cut, and tool nose radius on the surface quality characteristics namely form error i.e. cylindricity error and circularity error, and average surface roughness has been investigated. The material removal rate is also considered as an important response parameter. The analysis of variance study has been conducted to find the statistically significant parameters. The derived empirical relationships predicted the randomly generated test cases with less than 8.51%. Optimization for multiple outputs with conflicting requirements has been done using the principal component analysis and JAYA algorithm that resulted in the absolute deviation of 7.97% with average roughness- 0.64 µm, circularity error- 4.34 µm, cylindricity error- 0.365 µm, and material removal rate-28.63 cm3/min.
KW - Form error
KW - JAYA algorithm
KW - Machinability
KW - Material removal rate
KW - Optimization
KW - Surface roughness
UR - http://www.scopus.com/inward/record.url?scp=85076500215&partnerID=8YFLogxK
U2 - 10.1016/j.measurement.2019.107337
DO - 10.1016/j.measurement.2019.107337
M3 - Article
AN - SCOPUS:85076500215
SN - 0263-2241
VL - 152
JO - Measurement: Journal of the International Measurement Confederation
JF - Measurement: Journal of the International Measurement Confederation
M1 - 107337
ER -