Multi-Objective Optimization of Cutting Parameters in Turning AISI 304 Austenitic Stainless Steel

Abstract

Energy conservation and emission reduction is an essential consideration in sustainable manufacturing. However, the traditional optimization of cutting parameters mostly focuses on machining cost, surface quality, and cutting force, ignoring the influence of cutting parameters on energy consumption in cutting process. This paper presents a multi-objective optimization method of cutting parameters based on grey relational analysis and response surface methodology (RSM), which is applied to turn AISI 304 austenitic stainless steel in order to improve cutting quality and production rate while reducing energy consumption. Firstly, Taguchi method was used to design the turning experiments. Secondly, the multi-objective optimization problem was converted into a simple objective optimization problem through grey relational analysis. Finally, the regression model based on RSM for grey relational grade was developed and the optimal combination of turning parameters (ap = 2.2 mm, f = 0.15 mm/rev, and v = 90 m/s) was determined. Compared with the initial turning parameters, surface roughness (Ra) decreases 66.90%, material removal rate (MRR) increases 8.82%, and specific energy consumption (SEC) simultaneously decreases 81.46%. As such, the proposed optimization method realizes the trade-offs between cutting quality, production rate and energy consumption, and may provide useful guides on turning parameters formulation.

Formability and Failure Evaluation of AA3003-H18 Sheets in Single-Point Incremental Forming Process through the Design of Experiments

“…Considering the distinguishing coefficient, , as 0.5, the grey relational coefficient was estimated using operation 3, as tabulated in Table 7 . Before performing operations 4 and 5, the weight estimation was required for the selected responses; the Shannon entropy method, widely adopted in the decision-making process, was used for the estimation process [ 37 , 38 , 39 , 40 , 41 , 42 , 43 ]. …”

“…The optimal combination was determined from the higher GRG rank, which was experimental run 14, as listed in Table 8 . Algorithm 1: Procedures used for grey relational analysis (GRA) [ 37 , 38 , 39 , 40 , 41 , 42 , 43 ]. 1 Normalization: If the likelihood is the-smaller-the-netter (SB) or the-higher-the-better (HB), 2 Evaluation of : 3 Grey relational coefficient calculation: where is the grey relational coefficient between and .…”

A Proposal of an Adaptive Neuro-Fuzzy Inference System for Modeling Experimental Data in Manufacturing Engineering

“…Finally, it is worth mentioning the employment of techniques based on the DOE and regression techniques that have also been widely used for the study of technological variables found in manufacturing processes as shown in research studies such as that of Airao et al [54] which analyzed the effect of cutting speed, feed rate, and axial depth of cut on surface roughness obtained in end-milling of a stainless steel, that of Kasdekara et al [55], which employed a 2 4 full factorial (DOE) for determining the most important factors which influence MRR in Electro-chemical machining of AA6061 by using MLP and regression, and that of Ahmed et al [56] in which the MRR, in laser milling of three alloys (Ti6Al4V, Inconel 718 and AA 2024), was evaluated using the response surface method and DOE. On the other hand, Aslantas et al [57] obtained empirical relations between cutting speed, feed rate, depth of cut and surface roughness parameters using the RSM for the micro-turning process in a Ti6Al4V alloy and Su et al [58] employed a multi-objective optimization method based on grey relational analysis and RSM along with Taguchi method for analyzing surface roughness and MRR in turning of an AISI 304 austenitic stainless steel. Regression analysis is also used by Zajac et al [59] to make predictions of cutting tool durability in turning processes.…”

Finite element simulation and regression modeling of machining attributes on turning AISI 304 stainless steel

“…Yu Su et al [5] have optimized the machining parameters using grey relational modeling and response surface methodology in turning AISI 304 stainless steel. They have reported the optimum condition of process parameters to achieve minimum surface roughness such as, depth of cut À 2.2 mm, feed rate À 0.15 mm/rev and cutting speed À 90 m/min in turning AISI 304 stainless steel.…”