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Application of Artificial Neural Networks and Genetic Algorithm for Optimizing Process Parameters in Pocket Milling of AA7075

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Title Application of Artificial Neural Networks and Genetic Algorithm for Optimizing Process Parameters in Pocket Milling of AA7075
 
Creator Rajyalakshmi, M.
Rao, M Venkateswara
 
Subject Aluminium
ANN
Multi objective genetic algorithms
RSM
Tool trajectory
 
Description 911-921
Mould preparation is an important phase in the injection moulding process. The surface roughness of the mould affects
the surface finish of the final plastic product. Quality product with a better production rate is required to meet the
competition in the present market. To achieve this objective, manufacturers try to select the best combination of parameters.
Multi-objective optimization is one such technique to obtain the optimal process parameters that give better quality with a
good production rate. The current paper describes the application of Multi-Objective Genetic Algorithms (MOGA) on the
Artificial Neural Network (ANN) model for pocket milling on AA7075. Through the application of ANN with MOGA
minimum Surface Roughness (SR) is achieved with a better Material Removal Rate (MRR). From the confirmation
experiments, it is evident that follow-periphery tool path gives a better surface finish with higher MRR and the percentage
error observed is 1.9553 and 1.8282 respectively.
 
Date 2022-09-06T11:49:47Z
2022-09-06T11:49:47Z
2022-09
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/60493
https://doi.org/10.56042/jsir.v81i09.55874
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.81(09) [Sep 2022]