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Open AccessArticle

Experimental Optimization of Nimonic 263 Laser Cutting Using a Particle Swarm Approach

1
Faculty of Information Technology, Faculty of Management, Belgrade Metropolitan University, Tadeusa Koscuska 63, 11000 Belgrade, Serbia
2
Innovation Centre Faculty of Mechanical Engineering in Belgrade, Kraljice Marije 16, 11120 Belgrade, Serbia
3
Institute of General and Physical Chemistry, Studentski trg 12/V, 11000 Belgrade, Serbia
*
Author to whom correspondence should be addressed.
Metals 2019, 9(11), 1147; https://doi.org/10.3390/met9111147
Received: 1 October 2019 / Revised: 17 October 2019 / Accepted: 23 October 2019 / Published: 25 October 2019
This paper presents an experimental study carried out on Nimonic 263 alloy sheets to determine the optimal combination of laser cutting control factors (assisted gas pressure, beam focus position, laser power, and cutting speed), with respect to multiple characteristics of the cut area. With the aim of designing laser cutting parameters that satisfy the specifications of multiple responses, an advanced multiresponse optimization methodology was used. After the processing of experimental data to develop the process measure using statistical methods, the functional relationship between cutting parameters and the process measure was determined by artificial neural networks (ANNs). Using the trained ANN model, particle swarm optimization (PSO) was employed to find the optimal values of laser cutting parameters. Since the effectiveness of PSO could be affected by its parameter tuning, the settings of PSO algorithm-specific parameters were analyzed in detail. The optimal laser cutting parameters proposed by PSO were implemented in the validation run, showing the superior cut characteristics produced by the optimized parameters and proving the efficacy of the suggested approach in practice. In particular, it is demonstrated that the quality of the Nimonic 263 cut area and the microstructure were significantly improved, as well as the mechanical characteristics. View Full-Text
Keywords: laser cutting; Nimonic 263; microstructural characterization; microhardness; surface roughness; particle swarm optimization; parameters optimization; simulated annealing (SA); artificial neural networks (ANNs) laser cutting; Nimonic 263; microstructural characterization; microhardness; surface roughness; particle swarm optimization; parameters optimization; simulated annealing (SA); artificial neural networks (ANNs)
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Sibalija, T.; Petronic, S.; Milovanovic, D. Experimental Optimization of Nimonic 263 Laser Cutting Using a Particle Swarm Approach. Metals 2019, 9, 1147.

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