Application of Artificial Neural Networks and Genetic Algorithm for Optimizing Process Parameters in Pocket Milling of AA7075
NOPR - NISCAIR Online Periodicals Repository
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Title |
Application of Artificial Neural Networks and Genetic Algorithm for Optimizing Process Parameters in Pocket Milling of AA7075
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Creator |
Rajyalakshmi, M.
Rao, M Venkateswara |
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Subject |
Aluminium
ANN Multi objective genetic algorithms RSM Tool trajectory |
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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. |
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Date |
2022-09-06T11:49:47Z
2022-09-06T11:49:47Z 2022-09 |
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Type |
Article
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Identifier |
0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/60493 https://doi.org/10.56042/jsir.v81i09.55874 |
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Language |
en
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Publisher |
NIScPR-CSIR,India
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Source |
JSIR Vol.81(09) [Sep 2022]
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