Efficient optimization of process parameters in 2.5 D end milling using neural network and genetic algorithm

2018 ◽  
Vol 9 (5) ◽  
pp. 1198-1205
Author(s):  
Dinesh Kumar ◽  
Pankaj Chandna ◽  
Mahesh Pal
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Process Parameters ◽  
End Milling ◽  
Optimization Of Process Parameters

Application of BP-GA Algorithm in Optimization of Process Parameters in Thin Strip Tandem Cold Rolling

2013 ◽  
Vol 774-776 ◽  
pp. 1042-1045
Author(s):  
Li Chen Wang ◽  
Ji Shun Song ◽  
Jian Zhang ◽  
Pan Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Cold Rolling ◽  
Process Parameters ◽  
Bp Neural Network ◽  
Rolling Force ◽  
Thin Strip ◽  
Tandem Cold Rolling ◽  
Optimization Of Process Parameters ◽  
Ga Algorithm

The process parameters of thin strip tandem cold rolling were optimized based on the BP neural network and the genetic algorithm with which the rolling energy consumption required was reduced and could contribute to the rolling force and the thickness control.

Optimization of Process Parameters in Plasma Arc Cutting Applying Genetic Algorithm and Fuzzy Logic

2018 ◽  
pp. 123-139
Author(s):  
Nehal Dash ◽  
Apurba Kumar Roy ◽  
Sanghamitra Debta ◽  
Kaushik Kumar
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Fuzzy Logic ◽  
Process Parameters ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Plasma Arc ◽  
Plasma Arc Cutting ◽  
Optimization Of Process Parameters

Plasma Arc Cutting (PAC) process is a widely used machining process in several fabrication, construction and repair work applications. Considering gas pressure, arc current and torch height as the inputs and among all possible outputs, in the present work Material Removal Rate and Surface Roughness would be considered as factors that determines the quality, machining time and machining cost. In order to reduce the number of experiments Design of Experiments (DOE) would be carried out. In later stages applications of Genetic Algorithm (GA) and Fuzzy Logic would be used for Optimization of process parameters in Plasma Arc Cutting (PAC). The output obtained would be minimized and maximized for Surface Roughness and Material Removal Rate respectively using Genetic Algorithm (GA) and Fuzzy Logic.

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