Tri-objective constrained optimization of pulsating DC sourced magnetic abrasive finishing process parameters using artificial neural network and genetic algorithm

2021 ◽  
pp. 1-15
Author(s):  
Shadab Ahmad ◽  
Ranganath M. Singari ◽  
R.S. Mishra
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Constrained Optimization ◽  
Process Parameters ◽  
Magnetic Abrasive Finishing ◽  
Finishing Process ◽  
Abrasive Finishing ◽  
Artificial Neural

Artificial Intelligence Optimization and Experimental Study of Auto Panel Stamping Process

2011 ◽  
Vol 55-57 ◽  
pp. 1794-1798
Author(s):  
Lin Zhou ◽  
Xiao Min Cheng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Process Parameters ◽  
Parameters Optimization ◽  
Nonlinear Material ◽  
Object Function ◽  
Stamping Process ◽  
Artificial Neural ◽  
Auto Panel

Auto panel stamping is a complicated plastic deformation process with geometry nonlinear, material nonlinearity and numerous process parameters. The stamping process of a typical auto panel wheel wrap was studied by artificial intelligent optimization and physical experiment. The prediction model of object function was established using artificial neural network. In object function, blank-holder force, drawbead height and fillet radius were selected as the optimized variables and prevention of rupture was considered as the optimization objective. Process parameters optimization was performed with genetic algorithm. The optimized process parameters were used to guide die design and testing, and the result of wheel wrap stamping showed that the forming quality was obviously improved. So the process optimization based on artificial neural network and genetic algorithm is feasible and efficient for auto panel stamping.

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