@ARTICLE{Sultana_Mst._Nazma_Hybrid_2021,
 author={Sultana, Mst. Nazma and Dhar, Nikhil Ranjan},
 volume={vol. 68},
 number={No 1},
 journal={Archive of Mechanical Engineering},
 pages={23-49},
 howpublished={online},
 year={2021},
 publisher={Polish Academy of Sciences, Committee on Machine Building},
 abstract={The objective of the present study is to optimize multiple process parameters in turning for achieving minimum chip-tool interface temperature, surface roughness and specific cutting energy by using numerical models. The proposed optimization models are offline conventional methods, namely hybrid Taguchi-GRA-PCA and Taguchi integrated modified weighted TOPSIS. For evaluating the effects of input process parameters both models use ANOVA as a supplementary tool. Moreover, simple linear regression analysis has been performed for establishing mathematical relationship between input factors and responses. A total of eighteen experiments have been conducted in dry and cryogenic cooling conditions based on Taguchi L18 orthogonal array. The optimization results achieved by hybrid Taguchi-GRA-PCA and modified weighted TOPSIS manifest that turning at a cutting speed of 144 m/min and a feed rate of 0.16 mm/rev in cryogenic cooling condition optimizes the multi-responses concurrently. The prediction accuracy of the modified weighted TOPSIS method is found better than hybrid Taguchi-GRA-PCA using regression analysis.},
 type={Article},
 type={Artykuł /Article},
 title={Hybrid GRA-PCA and modified weighted TOPSIS coupled with Taguchi for multi-response process parameter optimization in turning AISI 1040 steel},
 URL={http://www.journals.pan.pl/Content/115039/PDF/AME_2021_131707.pdf},
 doi={10.24425/ame.2020.131707},
 keywords={grey relational analysis, principal component analysis, Taguchi method, analysis of variance, cryogenic cooling},
}