Artificial intelligence based modeling and optimization of heat affected zone in Nd:YAG laser cutting of duralumin sheet

Abstract

Duralumin is an alloy of aluminium which has some unique properties such as high strength to weight ratio, high resistance to corrosion, light in weight, and more demanding alloy in various sectors such as space craft, marine, chemical industries, construction and automobile. These applications require very precise and complex shapes which may not be obtained with conventional machining. Pulsed Nd:YAG laser cutting may be used to fulfill these objectives by using optimum setting of process parameters. The present research paper has experimentally investigated the modeling and optimization of heat affected zone in the pulsed Nd:YAG laser cutting of Duralumin sheet with the aim to minimize heat affected zone. The quality is improved by the proper control of different process parameters such as gas pressure, pulse width, pulse frequency and scanning speed. Artificial intelligence (AI) algorithms have been used to solve the many engineering problems successfully through development of Genetic Algorithm (GA), Fuzzy Logic (FL) and Artificial Neural Network (ANN) systems. The optimization of heat affected zone has been carried out by using Hybrid Approach of Multiple Regression Analysis (MRA) and GA. In this methodology, the second order regression model has been developed by using MRA with the help of experimental data obtained by L 27 orthogonal array (OA). Further this equation has been used as objective function in GA based optimization. The significant factors have been found with further discussion of their effect on the heat affected zone.

Dual-stage artificial neural network (ANN) model for sequential LBMM-μEDM-based micro-drilling

“…Vagheesan et al [12] also applied a neural network and particle swarm optimization (PSO) based modelling and optimization approach for the study of laser cutting (CO2 laser) of aluminium alloys. Norkey et al [13] also attempted an ANN-based modelling technique for studying the HAZ for laser machining of aerospace material like duralumin.…”

Systematic review of optimization techniques for laser beam machining

“…NNs are capable of predicting ideal parameter settings by effectively modelling intricate and non-linear interactions. This predictive ability contributes to the improvement of several objectives, including surface roughness, dross height, kerf width, kerf taper angle, and striation [123][124][125][126][127]. Researchers introduced a novel approach for predicting the quality of laser-cutting via the use of an artificial neural network.…”

Laser cutting optimization model with constraints: Maximization of material removal rate in CO2 laser cutting of mild steel

“…9,19,20 Other cut quality characteristics, such as surface roughness, the width of HAZ and dross formation, were also used in optimization studies as primary objective criteria. 6,18,21–25…”

“…9,19,20 Other cut quality characteristics, such as surface roughness, the width of HAZ and dross formation, were also used in optimization studies as primary objective criteria. 6,18,[21][22][23][24][25] From the literature review, it can be observed that the majority of laser cutting optimization research studies examined Nd:YAG laser cutting of aluminum alloys and stainless steel sheets of smaller thicknesses. Also, the majority of previous optimization studies considered simultaneous or separate optimization of two or more objective functions.…”