A novel hybridization of artificial neural network and moth-flame optimization (ANN–MFO) for multi-objective optimization in magnetic abrasive finishing of aluminium 6060

2019 ◽  
Vol 41 (6) ◽  
Author(s):  
Rajneesh Kumar Singh ◽  
Swati Gangwar ◽  
D. K. Singh ◽  
Vimal Kumar Pathak
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multi Objective Optimization ◽  
Magnetic Abrasive Finishing ◽  
Multi Objective ◽  
Abrasive Finishing ◽  
Artificial Neural

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

2016 ◽  
Vol 232 (4) ◽  
pp. 333-342 ◽  
Author(s):  
Saurabh Kumar Gupta ◽  
KN Pandey ◽  
Rajneesh Kumar
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Grain Size ◽  
Tensile Strength ◽  
Aluminium Alloys ◽  
Multi Objective Optimization ◽  
Friction Stir ◽  
Multi Objective ◽  
Artificial Neural

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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