Artificial intelligence-based modelling and multi-objective optimization of friction stir welding of dissimilar AA5083-O and AA6063-T6 aluminium alloys
IR@NML: CSIR-National Metallurgical Laboratory, Jamshedpur
View Archive Info| Field | Value | |
| Title |
Artificial intelligence-based modelling and multi-objective optimization of friction stir welding of dissimilar AA5083-O and AA6063-T6 aluminium alloys
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| Creator |
Gupta, S K
Pandey, K N Kumar, Rajneesh |
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| Subject |
Materials Science
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| Description |
The present research investigates the application of artificial intelligence tool for modelling and multi-objective optimization of friction stir welding parameters of dissimilar AA5083-O–AA6063-T6 aluminium alloys. The experiments have been conducted according to a well-designed L27 orthogonal array. The experimental results obtained from L27 experiments were used for developing artificial neural network-based mathematical models for tensile strength, microhardness and grain size. A hybrid approach consisting of artificial neural network and genetic algorithm has been used for multi-objective optimization. The developed artificial neural network-based models for tensile strength, microhardness and grain size have been found adequate and reliable with average percentage prediction errors of 0.053714, 0.182092 and 0.006283%, respectively. The confirmation results at optimum parameters showed considerable improvement in the performance of each response.
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| Publisher |
SAGE Publications
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| Date |
2018-04
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| Type |
Article
PeerReviewed |
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| Relation |
https://doi.org/10.1177/1464420715627293
http://eprints.nmlindia.org/7558/ |
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| Identifier |
Gupta, S K and Pandey, K N and Kumar, Rajneesh (2018) Artificial intelligence-based modelling and multi-objective optimization of friction stir welding of dissimilar AA5083-O and AA6063-T6 aluminium alloys. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, 232(4) (IF-1.281). pp. 333-342.
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