Title: Kerf quality prediction and optimisation for pulsed Nd:YAG laser cutting of aluminium alloy sheets using GA-ANN hybrid model

Authors: Sudipto Chaki; Sujit Ghosal; Ravi N. Bathe

Addresses: Department of Automobile Engineering, MCKV Institute of Engineering, 243, G.T. Road (N), Liluah, Howrah-711204, West Bengal, India. ' Department of Mechanical Engineering, Jadavpur University, Kolkata-700032, West Bengal, India. ' Centre for Laser Processing of Materials (CLPM), International Advance Research Centre for Power Metallurgy and New Materials (ARCI), Po. Balapur, Hyderabad-500005, Andhra Pradesh, India

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.

Keywords: laser cutting; kerf quality; artificial neural networks; ANNs; genetic algorithms; GAs; aluminium alloy sheet; aluminium alloys; quality prediction; Nd:YAG lasers; optimisation; cut quality; kerf width; kerf deviation.

DOI: 10.1504/IJMMS.2012.048233

International Journal of Mechatronics and Manufacturing Systems, 2012 Vol.5 No.3/4, pp.263 - 279

Received: 31 Mar 2011
Accepted: 20 Oct 2011
Published online: 21 Aug 2014 *

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