Genetic‐based multi‐objective optimization of alkylation process by a hybrid model of statistical and artificial intelligence approaches

2021 ◽  
Author(s):  
Farimah Mahmoudian ◽  
Amin Hedayati oghaddam ◽  
Seyed Mohammad Davachi
Keyword(s):  
Artificial Intelligence ◽  
Hybrid Model ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Alkylation Process

scholarly journals Multi-Objective Optimization of Manufacturing Process in Carbon Fiber Industry Using Artificial Intelligence Techniques

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 67576-67588 ◽  
Author(s):  
Gelayol Golkarnarenji ◽  
Minoo Naebe ◽  
Khashayar Badii ◽  
Abbas S. Milani ◽  
Ali Jamali ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Carbon Fiber ◽  
Manufacturing Process ◽  
Multi Objective Optimization ◽  
Artificial Intelligence Techniques ◽  
Multi Objective

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.

Sign in / Sign up

Export Citation Format

Share Document