Artificial intelligence-based modelling and multi-objective optimization of friction stir welding of dissimilar AA5083-O and AA6063-T6 aluminium alloys. (April 2018)

• Record Type: Journal Article
• Authors: Gupta, Saurabh Kumar; Pandey, KN; Kumar, Rajneesh
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 232:Number 4(2018)
• Page Start: 333
• Page End: 342
• Publication Date: 2018-04
• Subjects: Friction stir welding -- aluminium alloys -- Taguchi method -- artificial neural network -- genetic algorithm; Materials -- Periodicals; Engineering design -- Periodicals; 620.11
• DOI: 10.1177/1464420715627293
• Languages: English
• ISSNs: 1464-4207

Abstract

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.