Multi-objective Optimization of Cutting Parameters in Turning Process for Minimization of Carbon Emission and Processing Time

2021 ◽  
pp. 93-106
Author(s):  
Gunjan Agarwal ◽  
M. K. Khare ◽  
Ankit Kumar Singhal ◽  
Ravi Prakash
Keyword(s):  
Carbon Emission ◽  
Processing Time ◽  
Cutting Parameters ◽  
Multi Objective Optimization ◽  
Turning Process ◽  
Multi Objective ◽  
Optimization Of Cutting Parameters

Multi-objective optimization of cutting parameters in the SAE 4340 steel turning process

2019 ◽  
Author(s):  
Augusto Campidelli ◽  
Marco Antonio Bonelli Junior ◽  
Bernardo Murta ◽  
Bruno Silva de Lima
Keyword(s):  
Cutting Parameters ◽  
Multi Objective Optimization ◽  
Turning Process ◽  
Multi Objective ◽  
4340 Steel ◽  
Steel Turning ◽  
Optimization Of Cutting Parameters

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

Metals ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 217 ◽  
Author(s):  
Yu Su ◽  
Guoyong Zhao ◽  
Yugang Zhao ◽  
Jianbing Meng ◽  
Chunxiao Li
Keyword(s):  
Energy Consumption ◽  
Cutting Parameters ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Aisi 304 ◽  
Multi Objective ◽  
Relational Analysis ◽  
304 Austenitic Stainless Steel ◽  
Grey Relational ◽  
Optimization Of Cutting Parameters

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.

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