Experimental Optimization of Nimonic 263 Laser Cutting Using a Particle Swarm Approach
2019
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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 parame...ters 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.
Ključne reči:
surface roughness / simulated annealing (SA) / particle swarm optimization / parameters optimization / Nimonic 263 / microstructural characterization / microhardness / laser cutting / artificial neural networks (ANNs)Izvor:
Metals, 2019, 9, 11Izdavač:
- MDPI AG
Finansiranje / projekti:
- Ministry of Education, Science and Technological Development of the Republic of Serbia
DOI: 10.3390/met9111147
ISSN: 2075-4701
WoS: 000504411600015
Scopus: 2-s2.0-85078170110
Institucija/grupa
Institut za opštu i fizičku hemijuTY - JOUR AU - Sibalija, Tatjana AU - Petronić, Sanja AU - Milovanović, Dubravka PY - 2019 UR - https://riofh.iofh.bg.ac.rs/handle/123456789/646 AB - 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. PB - MDPI AG T2 - Metals T1 - Experimental Optimization of Nimonic 263 Laser Cutting Using a Particle Swarm Approach IS - 11 VL - 9 DO - 10.3390/met9111147 UR - conv_820 ER -
@article{ author = "Sibalija, Tatjana and Petronić, Sanja and Milovanović, Dubravka", year = "2019", abstract = "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.", publisher = "MDPI AG", journal = "Metals", title = "Experimental Optimization of Nimonic 263 Laser Cutting Using a Particle Swarm Approach", number = "11", volume = "9", doi = "10.3390/met9111147", url = "conv_820" }
Sibalija, T., Petronić, S.,& Milovanović, D.. (2019). Experimental Optimization of Nimonic 263 Laser Cutting Using a Particle Swarm Approach. in Metals MDPI AG., 9(11). https://doi.org/10.3390/met9111147 conv_820
Sibalija T, Petronić S, Milovanović D. Experimental Optimization of Nimonic 263 Laser Cutting Using a Particle Swarm Approach. in Metals. 2019;9(11). doi:10.3390/met9111147 conv_820 .
Sibalija, Tatjana, Petronić, Sanja, Milovanović, Dubravka, "Experimental Optimization of Nimonic 263 Laser Cutting Using a Particle Swarm Approach" in Metals, 9, no. 11 (2019), https://doi.org/10.3390/met9111147 ., conv_820 .