Volume 43, No 1, 2021, Pages 57-65
Multi-objective Optimization in Turning Operation of AISI 1055 Steel Using DEAR Method
Received: 17 November 2020
Revised: 15 December 2020
Accepted: 3 January 2021
Published: 15 March 2021
This paper presents a study on multi-objective optimization of turning process AISI 1055 steel. It designs 9 experiments (L9) for a Taguchi test series matrix. The four parameters of input include spindle speed, feed rate, depth of cut, tool nose radius. The AISI 1055 steel machining operation experiments are carried out based on the matrix created. They are performed on a conventional lathe. The factors considered for evaluating the machining quality include surface roughness, cutting force in X, Y, Z directions and material removal rate (MRR). First, the research is carried out to identify the impact of the input parameters on the output parameters. Analysis of experimental results show that spindle speed significantly affects all three components of cutting force, but slightly influences the surface roughness. Regarding feed rate, this is the parameter that has a strong effect on surface roughness and cutting force Fx but not on the cutting force Fy and Fz. Meanwhile, the depth of cut has a considerable influence on the force in the x and y directions but a limited impact on the surface roughness and the force in the z direction. Similar to the cutting speed, the tool nose radius is noticeable to all three components of the cutting force and negligible to surface roughness. The second aim of this study is to determine the value of the cutting parameters to achieve the minimum of surface roughness and cutting force and the maximum of MRR. The Data Envelopment Analysis-based Ranking (Dear) method is applied to solve multi-objective problems. The paper identified the optimum values of spindle speed, feed rate, depth of cut and tool nose radius are 910 rev/min, 0.194 mm/rev, 0.2 mm and 0.2 mm, respectively.
Turning AISI 1055 steel, Multi-objective optimization, Surface roughness, Cutting force, MRR, Taguchi, DEAR