scholarly journals Optimization of Cutting Parameters for Sustainable Machining of Titanium Ti-5553 Alloy using Genetic Algorithm

2020 ◽  
pp. 309-315
Author(s):  
Ali Osman KABİL ◽  
Yusuf KAYNAK
Keyword(s):  
Genetic Algorithm ◽  
Cutting Parameters ◽  
Sustainable Machining ◽  
Optimization Of Cutting Parameters

Optimization of Cutting Parameters in Milling Process Using Genetic Algorithm and ANOVA (March 2020)

2020 ◽  
Vol 38 (10A) ◽  
pp. 1489-1503
Author(s):  
Marwa Q. Ibraheem
Keyword(s):  
Genetic Algorithm ◽  
Tool Wear ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Optimal Value ◽  
Optimization Of Cutting Parameters

In this present work use a genetic algorithm for the selection of cutting conditions in milling operation such as cutting speed, feed and depth of cut to investigate the optimal value and the effects of it on the material removal rate and tool wear. The material selected for this work was Ti-6Al-4V Alloy using H13A carbide as a cutting tool. Two objective functions have been adopted gives minimum tool wear and maximum material removal rate that is simultaneously optimized. Finally, it does conclude from the results that the optimal value of cutting speed is (1992.601m/min), depth of cut is (1.55mm) and feed is (148.203mm/rev) for the present work.

The Optimization of Cutting Parameters of Cast Iron Based on the Genetic Algorithm

2012 ◽  
Vol 424-425 ◽  
pp. 1102-1106
Author(s):  
Hao Jin Yang ◽  
Xu Hong Guo ◽  
Dong Dong Wan ◽  
Ting Ting Chen
Keyword(s):  
Genetic Algorithm ◽  
Cast Iron ◽  
Production Efficiency ◽  
Cutting Parameters ◽  
Optimum Combination ◽  
Algorithm Analysis ◽  
Process Data ◽  
Optimization Of Cutting Parameters ◽  
The Mathematical Model ◽  
New Situation

Data timeliness of cutting process data is so intenser under the new situation than ever .It’s especially necessary to propose the physical model on which the optimization of cutting parameters is based. In this paper the mathematical model is established by the analysis of the data measured from the cast iron experiments, then use MATLAB genetic algorithm analysis to calculate the optimum combination of cutting parameters. The results show that the optimum combination of cutting parameters could improve the production efficiency in practice.

Research on Optimization of Cutting Parameters Based on Genetic Algorithm

2011 ◽  
Vol 121-126 ◽  
pp. 4640-4645 ◽  
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.

Optimization of Multipass Turning Parameters by Genetic Algorithm

2011 ◽  
Vol 264-265 ◽  
pp. 1545-1550 ◽  
Author(s):  
Nafis Ahmad ◽  
Tomohisa Tanaka ◽  
Yoshio Saito
Keyword(s):  
Genetic Algorithm ◽  
Cutting Parameters ◽  
Interface Temperature ◽  
Depth Of Cut ◽  
Optimum Number ◽  
Optimization Strategy ◽  
Turning Operation ◽  
Multipass Turning ◽  
Cad Cam ◽  
Optimization Of Cutting Parameters

Optimization of cutting parameters is one of the key obstacles for CAD/CAM integration. In this work optimum cutting parameters, the best sequence, number, and type of passes of turning operation are determined by Genetic Algorithm (GA). Proposed optimization strategy ensures that no constraint will be violated at the optimum condition and determines the optimum number and type of passes such as rough, finish and semi-finish passes to complete a multipass turning operation. Here objective function is the unit production cost and constraints are limits of cutting force, power, tool life, stability condition, tool chip interface temperature, surface finish, feed rate to depth of cut ratio and the available rotational speed of spindle of machine tool.

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