Kerf quality prediction and optimisation for pulsed Nd:YAG laser cutting of aluminium alloy sheets using GA-ANN hybrid model. (1st January 2012)

• Record Type: Journal Article
• Authors: Chaki, Sudipto; Ghosal, Sujit; Bathe, Ravi N.
• Is Part Of: International journal of mechatronics and manufacturing systems. Volume 5:Number 3/4(2012)
• Page Start: 263
• Page End: 279
• Publication Date: 2012-01-01
• Subjects: laser cutting -- kerf quality -- neural networks -- genetic algorithm -- GA -- aluminium alloy sheets; Mechatronics -- Periodicals; 629.89
• Languages: English
• ISSNs: 1753-1039

Abstract

During precision cutting of thin sheet metals with pulsed Nd:YAG laser beam, best cut quality can obtained by uniform kerf with minimum kerf width. An integrated GA-ANN model has been developed and employed in the present work for prediction and optimisation of cut quality where trained ANN is used for computation of fitness value. A full factorial experiment on laser cutting of aluminium alloy using pulsed Nd:YAG laser has been accomplished for generation of dataset for the model. It is observed that, the ANN module can predict kerf width and kerf deviation with mean absolute % error of 0.42% and 1.05%. Optimised kerf width and kerf deviation determined by GA optimisation results in absolute % error of 1.87% and 2.00% only during comparison with results of validation experiment. By way of using this model a considerable improvement in kerf quality has been achieved with optimised parameter setting.