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.
Highlights
Cutting process is the main means of mechanical manufacturing, which plays an important role in the manufacturing industry
The objectives of this paper are to: (1) Investigate the multi-objective optimization framework of turning parameters for sustainable manufacturing; (2) propose the multi-objective optimization method based on grey relational analysis and response surface methodology (RSM); (3) verify the optimization method with wet turning experiments of AISI 304 austenitic stainless steel
The multi-objective optimization method based on grey relational analysis and RSM
Summary
Cutting process is the main means of mechanical manufacturing, which plays an important role in the manufacturing industry. It was found that the formulation of cutting parameters has significant influence on cutting quality, production rate, and energy consumption [1,2,3]. Most of the cutting parameters are determined according to engineering experience and specialized handbooks, which cannot obtain the optimal machining effect. The optimization of cutting parameters for different objectives has always been a hot issue in manufacturing enterprises and academia. The surface integrity, machining efficiency, and cutting force are usually taken as objectives in most of the traditional optimization of cutting parameters. Kumar [4] adopted surface roughness and material removal rate (MRR) as objectives to optimize the cutting parameters in turning
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