Analysis of pulsed laser bending of sheet metal using neural networks and neuro-fuzzy system

Abstract

Laser bending is a nontraditional forming process, where sheet metal gets plastically deformed by laser-induced thermal stresses. The objective of this study is to establish the relationships between bending angles and process parameters in a pulsed laser bending process using soft computing–based methods, that is, neural networks and neuro-fuzzy system. Laser power, scan speed, spot diameter and pulse duration were considered as inputs, and bending angle was taken as output for modeling the bending angle (called forward analysis). In the case of inverse analysis or process synthesis (i.e. to determine the process parameters in order to achieve the desired outputs), bending angle and pulse duration were considered as inputs, and laser power, scan speed and spot diameter were treated as outputs. For both forward and inverse analyses, neural networks and neuro-fuzzy systems were trained in a batch mode with experimental data using two different algorithms, that is, genetic algorithm and back-propagation algorithm. The optimized networks were used for the predictions of bending angles and process parameters for some test cases. All the developed models were found to be satisfactory for both the analyses. Genetic algorithm was found to perform better than the back-propagation algorithm for both the networks in terms of prediction accuracy but at the cost of computational time. Neural networks trained with genetic algorithm were seen to perform better than the other models in predicting bending angles and process parameters. The developed models might be helpful in automating the pulsed laser forming process.