Multi-objective optimization of cutting parameters in turning process using differential evolution and non-dominated sorting genetic algorithm-II approaches

In this chapter, the technical issues of two-stage TEC were discussed. After that, a new method of optimizing the dimension of TECs using differential evolution to maximize the cooling rate and coefficient of performance was proposed. A input current to hot side and cold side of and the number ratio between the hot stage and cold stage are searched the optima solutions. Thermal resistance is taken into consideration. The results of optimization obtained by using differential evolution were validated by comparing with those obtained by using genetic algorithm and show better performance in terms of stability, computational efficiency, robustness. This work revealed that differential evolution more stable than genetic algorithm and the Pareto front obtained from multi-objective optimization balances the important role between cooling rate and coefficient of performance.

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Modeling and multi-objective optimization of cutting parameters in the high-speed milling using RSM and improved TLBO algorithm

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-020-06284-9 ◽

2020 ◽

Vol 111 (7-8) ◽

pp. 2323-2335

Author(s):

Bo Li ◽

Xitian Tian ◽

Min Zhang

Keyword(s):

High Speed ◽

Cutting Parameters ◽

Multi Objective Optimization ◽

High Speed Milling ◽

Multi Objective ◽

Tlbo Algorithm ◽

Optimization Of Cutting Parameters

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

Metals ◽

10.3390/met10020217 ◽

2020 ◽

Vol 10 (2) ◽

pp. 217 ◽

Cited By ~ 5

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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Research on Optimization of Cutting Parameters Based on Genetic Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.121-126.4640 ◽

2011 ◽

Vol 121-126 ◽

pp. 4640-4645 ◽

Cited By ~ 1

Author(s):

Shu Ren Zhang ◽

Xue Guang Li ◽

Jun Wang ◽

Hui Wei Wang

Keyword(s):

Genetic Algorithm ◽

Surface Roughness ◽

Surface Quality ◽

Bp Neural Network ◽

Prediction Method ◽

Cutting Parameters ◽

Multi Objective Optimization ◽

Experiment Data ◽

Optimization Of Cutting Parameters ◽

Working Efficiency

Three elements of Cutting dosages have a great effect on parts surface quality and working efficiency, the best organization of cutting three elements for parts surface quality must be found before machining, in order to surface roughness and machining cost of parts, multi-objective optimization model is established in this paper, model is solved by using genetic algorithm. Based on the BP neural network of three layers, forecasting model of surface roughness is established. According to existing experiment data and optimized cutting dosages, analysis and prediction of surface roughness is done. Machining experiment is done by using optimized data. The experiment result verifies feasibility of this optimistic method and prediction method of surface roughness.

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