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


Affiliations
1 Acharya Nagarjuna University, Guntur 522 510, Andhra Pradesh, India
2 Department of Mechanical Engineering, Bapatla Engineering College, Bapatla 522 102, Andhra Pradesh, India
 

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.

Keywords

Aluminium, ANN, Multi objective genetic algorithms, RSM, Tool trajectory
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  • Application of Artificial Neural Networks and Genetic Algorithm for Optimizing Process Parameters in Pocket Milling of AA7075

Abstract Views: 97  |  PDF Views: 71

Authors

M. Rajyalakshmi
Acharya Nagarjuna University, Guntur 522 510, Andhra Pradesh, India
M. Venkateswara Rao
Department of Mechanical Engineering, Bapatla Engineering College, Bapatla 522 102, Andhra Pradesh, India

Abstract


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.

Keywords


Aluminium, ANN, Multi objective genetic algorithms, RSM, Tool trajectory

References