Applied Chemical Engineering (2024) Volume 7 Issue 1 doi: 10.24294/ace.v7i1.2599 1 Original Research Article Multi-response optimization in cutting mild steels Yusuf Şahin * , Demiral Akbar Department of Mechanical Engineering, Faculty of Engineering, Ostim Technical University, 06374 Ankara, Turkey * Corresponding author: Yusuf Şahin, yusuf.sahin@ostimteknik.edu.tr ABSTRACT Machine tools are very important metal cutting process that used widely in manufacture/construction and energy sector. Material removal rate in any metal cutting process is very important because it significantly affects the production rate, generated energy/forces, tool life. Improper choice of the machine tools, cutting tools or parameters will lead to be produced early wear, more energy and deterioration of surface qualities of machined mechanical components. The cutting process should be controlled during cutting or shaping process. In this study, therefore, multi-response optimization is carried out on AISI 1040 hardened mild steels when machined with ceramic cutting tools using response surface methodology under different cutting conditions. It can be noted that there are two responses. One is the surface roughness (SR) while the second is the material removal rate (MRR). The experimental results exhibits that all three factors reveal significant influence on generating metal cutting energy. Optimal levels are found out in A3, B3 and C3 level. Namely; cutting tests are carried out at 170 m/min cutting speed, 0.15 mm/rev. feed rate and 0.5 mm depth of cut conditions in terms of multi response performance index (MRPI). Analysis of variance and Pareto chart indicate that besides basic factors, A × C, A × B, B × C interactions have also an influence on MRPI (combination of MRR with SR). It is concluded that the correlation coefficient is found about 99.06%. Therefore, MRPI approach is capable of providing good modelling results for the combination of SR and MRR. Keywords: mild steel; cutting speed; feed rate; surface roughness; metal removal rate; muti-response optimization 1. Introduction The metal cutting process is a material removal process by means of usage of different cutting tools such as carbide, coated carbides, ceramics, coated ceramics and boron nitride. These tools are used because of their hot hardness/wear resistance [1] . In any metal cutting process, material removal rate (MRR) has played importance role because of significantly affecting the rate of production, consumption of energy, forces and tool’s service life [2] . Surface roughness (SR) is also very vital parameter due to determine the quality of any components. The quality of products is one of costumers’ requirements. SR affects the fatigue and fracture strength, friction/wear properties of mechanical parts surfaces [2] . Tool life is actively cutting service time for indicating a performance satisfactorily. Thus, to reduce the cost and improve productivity, more longer tool life should be provided [3] . Improper choice of these selection parameters affects the surface quality, lead to abrasive wear and process efficiency [4] . Therefore, optimum cutting parameters should be determined. In this case, a better way is to apply some methodology like Taguchi, factorial, response surface and artificial neural network approach to limit the experiment runs, hence, leading ARTICLE INFO Received: 15 August 2023 Accepted: 18 September 2023 Available online: 21 December 2023 COPYRIGHT Copyright © 2023 by author(s). Applied Chemical Engineering is published by EnPress Publisher, LLC. This work is licensed under the Creative Commons Attribution- NonCommercial 4.0 International License (CC BY-NC 4.0). https://creativecommons.org/licenses/by- nc/4.0/