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-1.625). pp. 333-342.
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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.
|Uncontrolled Keywords:||intelligence-based modelling,multi-objective optimization,friction stir welding,aluminium alloys,artificial intelligence tool|
|Deposited By:||Sahu A K|
|Deposited On:||16 Aug 2017 12:37|
|Last Modified:||05 Apr 2018 12:23|
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