Abstract
Original scientific paper In this paper, multi-objective optimization of the cut quality characteristics in CO2 laser cutting of AISI 304 stainless steel was discussed. Three mathematical models for the prediction of cut quality characteristics such as surface roughness, kerf width and heat affected zone were developed using the artificial neural networks (ANNs). The laser cutting experiment was planned and conducted according to the Taguchi’s L27 orthogonal array and the experimental data were used to train single hidden layer ANNs using the Levenberg-Marquardt algorithm. The ANN mathematical models were developed considering laser power, cutting speed, assist gas pressure, and focus position as the input parameters. Multi-objective optimization problem was formulated using the weighting sum method in which the weighting factors that are used to combine cut quality characteristics into the single objective function were determined using the analytic hierarchy process method.
Highlights
Laser cutting is one of the most extensively used nonconventional material removal processes applied for processing a wide variety of materials
Three mathematical models for the prediction of cut quality characteristics such as the surface roughness, kerf width and heat-affected zone (HAZ) were developed using the artificial neural networks (ANNs) on the basis of experimental data obtained from Taguchi’s L27 orthogonal array (OA)
The response functions are the cut quality characteristics such as surface roughness (Ra), kerf width (Kw) and width of the heat affected zone (HAZ) which are the functions of independent parameters, i.e. laser cutting parameters such as laser power, cutting speed, assist gas pressure and focus position
Summary
Laser cutting is one of the most extensively used nonconventional material removal processes applied for processing a wide variety of materials. Satisfying multiple performance characteristics call for mathematical modelling of the laser cutting process, formulation of multi-objective optimization problem and subsequently determination of acceptable (near optimal) cutting conditions through the use of optimization methods. The analysis of reference books revealed that there is a lack of research on the multi-objective optimization of CO2 laser cutting, and there is no investigation reported on the stainless steel. In the present research paper an attempt has been made for the multi-objective optimization of CO2 laser cutting of stainless steel To this aim, three mathematical models for the prediction of cut quality characteristics such as the surface roughness, kerf width and HAZ were developed using the ANNs on the basis of experimental data obtained from Taguchi’s L27 orthogonal array (OA). The multi-objective optimization results with the corresponding optimal values of laser cutting parameters were presented for three different scenarios, that is better profitability, higher productivity, and better profitability and higher productivity at the same time
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