Open Access
Issue |
Int. J. Simul. Multidisci. Des. Optim.
Volume 14, 2023
|
|
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Article Number | 20 | |
Number of page(s) | 17 | |
DOI | https://doi.org/10.1051/smdo/2023012 | |
Published online | 19 December 2023 |
- M. Siva Kumar, D. Rajamani, E. Abouel Nasr, E. Balasubramanian, H. Mohamed, A. Astarita, A hybrid approach of anfisartificial bee colony algorithm for intelligent modeling and optimization of plasma arc cutting on monelâ 400 alloy, Materials 14, 6373 (2021) [CrossRef] [Google Scholar]
- K. Salonitisa, S. Vatousianos, in 45th CIRP Conference on Manufacturing Systems 2012 (Elsevier, 2012), pp. 287–292 [Google Scholar]
- P. Patel, S. Soni, N. Kotkunde, N. Khanna, Study the effect of process parameters in plasma arc cutting on quard-400 material using analysis of variance, Mater. Today: Proc. 5, 6023–6029 (2018) [CrossRef] [Google Scholar]
- M. Gostimirović, D. Rodić, M. Sekulić, A. Aleksić, An experimental analysis of cutting quality in plasma arc machining, Adv. Technol. Mater. 45, 1–8 (2020) [Google Scholar]
- D. Rajamani, K. Ananthakumar, E. Balasubramanian, J. Paulo Davim, Experimental investigation and optimization of pac parameters on monel 400 superalloy, Mater. Manuf. Process. 33, 1864–1873 (2018) [CrossRef] [Google Scholar]
- M.R.C. Bidajwala, M.M.A. Trivedi, M.H.M. Gajera, M.T.S. Raol, Parametric optimization on plasma arc cutting machine for aisi 1018, Scientific Journal of Impact Factor (SJIF) 3 (2015) [Google Scholar]
- S.R. Mangaraj, D.K. Bagal, N. Parhi, S.N. Panda, A. Barua, S. Jeet, Experimental study of a portable plasma arc cutting system using hybrid rsm-nature inspired optimization technique, Mater. Today: Proc. 50, 867–878 (2022) [CrossRef] [Google Scholar]
- S. Mittal, M. Mahajan, Multi-response parameter optimization of cnc plasma arc machining using Taguchi methodology, J. Ind. Eng. 11, (2018) [Google Scholar]
- K.R.P. Pallavi H. Agarwal, Optimizing plasma arc cutting parameters for structural steel using grey relational analysis, Int. J. Eng. Res. Technol. 8, (2019) [Google Scholar]
- S. Sharma, M.K. Gupta, R. Kumar, N. Bindra, Experimental analysis and optimization of process parameters in plasma arc cutting machine of en-45a material using Taguchi and Anova method, Int. J. Mech. Aerospace Ind. Mechatron. Manuf. Eng. 11, 1387–1391 (2017) [Google Scholar]
- D.B. Ghane, Optimization of design parameters and nozzle wear on cnc plasma machine by experimentation, Int. Res. J. Eng. Technol. (IRJET) 6, (2019) [Google Scholar]
- E. Agbonoga, O. Adedipe, U. Okoro, F. Usman, O. Kafayat, S. Lawal, Effect of process parameters on the surface roughness and kerf width of mild steel during plasma arc cutting using response surface methodology, FUOYE J. Eng. Technol. 5, (2020). https://doi.org/10.46792/fuoyejet.v5i1.464 [CrossRef] [Google Scholar]
- G. Singh, S. Akhai, Experimental study and optimization of mrr in cnc plasma arc cutting, J. Eng. Res. Appl. 5, (2015) [Google Scholar]
- P. Pittayachaval, Y. Aupkaew, S. Sakhonkhan, T. Sukan, C. Patchaikhonang, Investigating plasma-nozzle wear based on processing time and current ampere, Mater. Sci. Forum 987, 171–176 (2020) [CrossRef] [Google Scholar]
- H. Pothur, V. Reddy, R. Ganesan, Experimental investigations on process parameters of stainless steel 410 alloy by plasma arc machining process using grey relational analysis with entropy measurement, Mater. Today: Proc. 62, 559–565 (2022) [CrossRef] [Google Scholar]
- A. Belhocine, D. Shinde, R. Patil, Thermomechanical coupled analysis-based design of ventilated brake disc using genetic algorithm and particle swarm optimization, JMST Adv. (2021). https://doi.org/10.1007/s42791-021-00040-0 [Google Scholar]
- H. Patel, H. Gohil, N. Vasawala, J. Patel, Experiment and analyse material removal rate in plasma arc cutting of ss410 for various parameters using Anova, Int. J. Adv. Eng. Manag. 5(2), 179–187 (2023) [Google Scholar]
- T.K. Gupta, K. Raza, Optimization of ann architecture: a review on nature-inspired techniques, Mach. Learn. Bio-Signal Anal. Diag. Imaging (2019) [Google Scholar]
- A. Equbal, M. Shamim, I.A. Badruddin, M.I. Equbal, A.K. Sood, N.N. Nik Ghazali, Z.A. Khan, Application of the combined ann and ga for multi-response optimization of cutting parameters for the turning of glass fiber-reinforced polymer composites, Mathematics 8, (2020) [Google Scholar]
- J. Carr, An introduction to genetic algorithms (2014). https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&as_ylo=2016&as_yhi=2016&q=Carr%2C+J.%2C+An+Introduction+to+Genetic+Algorithms.+2014&btnG= [Google Scholar]
- J. Roberts, A. Cassula, J. Silveira, P. Prado, J. Freire, Gatoolbox: a matlab-based genetic algorithm toolbox for function optimization, in: The 12th Latin-American Congress on Electricity Generation and Transmission – Clagtee, 2017 [Google Scholar]
- M.A. Linger, T.M. Bogale, Parameters optimization of tungsten inert gas welding process on 304l stainless steel using grey based Taguchi method, Eng. Res. Express 5, (2023) [Google Scholar]
- A.K. Mengistie, T.M. Bogale, Development of automatic orbital pipe mig welding system and process parameters'™ optimization of aisi 1020 mild steel pipe using hybrid artificial neural network and genetic algorithm, Int. J. Adv. Manuf. Technol. (2023). https://doi.org/10.1007/s00170-023-11796-1 [Google Scholar]
- E.A. Berihun, T.M. Bogale, Parameter optimization of pet plastic preform bottles in injection molding process using grey-based Taguchi method, Adv. Mater. Sci. Eng. 2022, (2022) [Google Scholar]
- H. Ramakrishnan, R. Balasundaram, N.V. Ganesh, N. Karthikeyan, Experimental investigation of cut quality characteristics on ss321 using plasma arc cutting, J. Braz. Soc. Mech. Sci. Eng. 40, 1–11 (2018) [CrossRef] [Google Scholar]
- C. Mu, B.Z. Qiu, X.H. Liu, A new method for figuring the number of hidden layer nodes in bp algorithm, Int. J. Recent Innov. Trends Comput. Commun. 5, 101–114 (2017) [Google Scholar]
- F. Yang, H. Cho, H. Zhang, J. Zhang, Y. Wu, Artificial neural network (ann) based prediction and optimization of an organic rankine cycle (orc) for diesel engine waste heat recovery, Energy Convers. Manag. 164, 15–26 (2018) [CrossRef] [Google Scholar]
- S.H. Gökler, S. Boran, Determining optimal machine part replacement time using a hybrid ann-ga model, Sci. Iranica 29, 771–782 (2022) [Google Scholar]
- K.G. Sheela, S.N. Deepa, Review on methods to fix number of hidden neurons in neural networks, Math. Probl. Eng. 2013, 11 (2013) [CrossRef] [Google Scholar]
- S. Masoudi, M. Mirabdolahi, M. Dayyani, F. Jafarian, A. Vafadar, M.R. Dorali, Development of an intelligent model to optimize heat-affected zone, kerf, and roughness in 309 stainless steel plasma cutting by using experimental results, Mater. Manuf. Process. 34, 345–356 (2019) [CrossRef] [Google Scholar]
- A.E. Abere, A.A. Tsegaw, R.B. Nallamothu, Process parameters optimization of bobbin tool friction stir welding on aluminum alloy 6061-t6 using combined artificial neural network and genetic algorithm, J. Braz. Soc. Mech. Sci. Eng. 44, 566 (2022) [CrossRef] [Google Scholar]
- E.W. Fenta, A.A. Tsegaw, in Artificial Intelligence and Digitalization for Sustainable Development: 10th EAI International Conference, ICAST 2022, Bahir Dar, Ethiopia, November 4–6, 2022, Proceedings (Springer, 2023), pp. 13–26 [Google Scholar]
- V. Khezri, E. Yasari, M. Panahi, A. Khosravi, Hybrid artificial neural networkâ genetic algorithm-based technique to optimize a steady-state gas-to-liquids plant, Ind. Eng. Chem. Res. 59, 8674–8687 (2020) [CrossRef] [Google Scholar]
- S. Ahmad R.M. Singari, R.S. Mishra, Tri-objective constrained optimization of pulsating dc sourced magnetic abrasive finishing process parameters using artificial neural network and genetic algorithm, Mater. Manuf. Process. 36, 843–857 (2021) [CrossRef] [Google Scholar]
- U.M.R. Paturi, S. Cheruku, V.P.K. Pasunuri, S. Salike, N. Reddy, S. Cheruku, Machine learning and statistical approach in modeling and optimization of surface roughness in wire electrical discharge machining, Mach. Learn. Appl. 6, 100,099 (2021) [Google Scholar]
- A.J. Santhosh, A.D. Tura, I.T. Jiregna, W.F. Gemechu, N. Ashok, M. Ponnusamy, Optimization of cnc turning parameters using face centered ccd approach in rsm and ann-genetic algorithm for aisi 4340 alloy steel, Results Eng. 11, 100–251 (2021) [Google Scholar]
- D. Yang, Q. Guo, Z. Wan, Z. Zhang, X. Huang, Surface roughness prediction and optimization in the orthogonal cutting of graphite/polymer composites based on artificial neural network, Processes 9, 1858 (2021) [CrossRef] [Google Scholar]
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