Multi-optimization of Stellite 6 Turning Parameters for Better Surface Quality and Higher Productivity Through RSM and Grey Relational Analysis Brahim Ben Fathallah 2(&) , Riadh Saidi 1 , Tarek Mabrouki 1 , Salim Belhadi 3 , and Mohamed Athmane Yallese 3 1 Applied Mechanics and Engineering Laboratory (LR-11-ES19), University of Tunis El Manar, ENIT, BP 37, Le Belvédère, 1002 Tunis, Tunisia {brahim.benfathallah,riadh.saidi, tarek.mabrouki}@enit.utm.tn 2 Mechanical, Material and Process Laboratory (LR99ES05) ENSIT, University of Tunis, 5 AV Taha Hussein Montfleury, Tunis, Tunisia 3 Mechanics and Structures Research Laboratory (LMS), May 8th 1945 University, 401, 24000 Guelma, Algeria Abstract. The present paper consists of an experimental study to the effect of turning parameters on surface roughness of Cobalt alloy (Stellite 6) and the optimization of machining parameters based on Grey relational analysis. Taguchi’s design of experiments (DOE) is used to carry out the tests. The response surface methodology is successfully applied in the analysis of the effect the turning parameters on surface roughness parameters. Second order mathe- matical models in terms of machining parameters are developed from experi- mental results. The experiment is carried out by considering four machining conditions, namely noise radius, cutting depth, cutting speed and feed rate as independent variables and average arithmetic roughness as response variables. It can be seen that the tool noise radius and feed rate are the most influential parameters on the surface roughness. The adequacy of the surface roughness model was established using analysis of variance (ANOVA). An attempt was also made to optimize cutting parameters using a Grey relational analysis to achieve minimum surface roughness and maximum material removal rate. Keywords: Surface roughness Á Optimization Á Turning Á Dif ficult-of-cut Á RSM Á GRA Abbreviations ANOVA Analysis of variance a p Depth of cut (mm) f Feed rate (mm/rev) GRA Grey relational analysis HRC Rockwell hardness MRR Material removal rate (cm 3 /min) © Springer Nature Switzerland AG 2020 N. Aifaoui et al. (Eds.): CMSM 2019, LNME, pp. 382–391, 2020. https://doi.org/10.1007/978-3-030-27146-6_41