An energy survey to optimize the technological parameters during the milling of AISI 304L steel using the RSM, ANN and genetic algorithm

References

• U.S. Energy Information Administration (EIA). International Energy Outlook 2019. Office of energy analysis. U.S. Department Of Energy, Washington, DC 20585, 2019 September.
   Google Scholar
• International Energy Agency. World energy outlook 2019. International Energy Agency; 2019. p. 1–810 .
   Google Scholar
• U.S. Energy Information Administration (EIA). International Energy Outlook 2016. Washington, DC: U.S. Center for Strategic and International Studies; 2016. May.
   Google Scholar
• Andrius Z, Vaida P. Energy consumption capital expenditures R&D cost and company profitability: evidence from paper and allied industry. Energetika. 2019;65(4):197–204. doi: 10.6001/energetika.v65i4.4248
   Google Scholar
• Hamdi A, Merghache SM, Aliouane T. Effect of cutting variables on bearing area curve parameters (BAC-P) during hard turning process. Archive Mechanical Engineering. 2020;67(1):73–95.
   Web of Science ®Google Scholar
• Seung-Jun S, Woo J, Rachuri S, et al. Standard data-based predictive modeling for power consumption in turning machining. Sustainability. 2018;10(3):598. doi: 10.3390/su10030598
   Web of Science ®Google Scholar
• Luoke H, Liu Y, Lohse N, et al. Sequencing the features to minimise the non-cutting energy consumption in machining considering the change of spindle rotation speed. Energy. 2017;139:935–946. doi: 10.1016/j.energy.2017.08.032
   Web of Science ®Google Scholar
• Lirong Z, Li J, Li F, et al. Optimization parameters for energy efficiency in end milling. Procedia CIRP. 2018;69:312–317. doi: 10.1016/j.procir.2017.12.005
   Google Scholar
• Rajesh K. Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of al alloy SiC particle composites. J Clean Prod. 2018;39:242–254. doi: 10.1016/j.jclepro.2012.08.008
   Google Scholar
• Tapoglou N, Mehnen J, Butans J, et al. Online on-board optimization of cutting parameter for energy efficient CNC milling. Procedia CIRP. 2016;40:384–389. doi: 10.1016/j.procir.2016.01.072
   Google Scholar
• Oda Y, Mori M, Ogawa K, et al. Study of optimal cutting condition for energy efficiency improvement in ball end milling with tool-workpiece inclination. CIRP Annals – Manufacturing Technology. 2012;61(1):119–122. doi: 10.1016/j.cirp.2012.03.034
   Web of Science ®Google Scholar
• Mori M, Fujishima M, Inamasu Y, et al. A study on energy efficiency improvement for machine tools. CIRP Annals -Manufacturing Technology. 2011;60(1):145–148. doi: 10.1016/j.cirp.2011.03.099
   Web of Science ®Google Scholar
• Liu N, Zhang YF, Lu WF. A hybrid approach to energy consumption modeling based on cutting power: a milling case. J Clean Prod. 2015;104:264–272. doi: 10.1016/j.jclepro.2015.05.049
   Web of Science ®Google Scholar
• Bousnina K, Hamza A. Reducing the energy consumed and increasing energy efficiency in the turning process. Int Journal Of Modern Manufacturing Technologies. 2020;XII(2):23–28.
   Google Scholar
• Belaidi I, Mohammedi K, Brachemi B Mise en œuvre de l’Algorithme NSGAII pour l’Optimisation Multi-Objectif des paramètres de coupe en Fraisage en bout. 19ème Congrès Français de Mécanique. 2009; Marseille: 24–28.
   Google Scholar
• Girish K, Kuldip SS. Predictive modelling and optimization of machining parameters to minimize surface roughness using artificial neural network Coupled with genetic algorithm. Procedia CIRP. 2015;31:453–458. doi: 10.1016/j.procir.2015.03.043
   Google Scholar
• Manoj IV, Soni H, Narendranath S, et al. Examination of machining parameters and prediction of cutting velocity and surface roughness using RSM and ANN using WEDM of Altemp HX. Advances In Materials Science And Engineering. 2022;2022:1–9. doi: 10.1155/2022/5192981
   Web of Science ®Google Scholar
• Eser A, Aşkar Ayyıldız E, Ayyıldız M, et al. Artificial intelligence-based surface roughness estimation modelling for milling of AA6061 alloy. Adv Mater Sci Eng. 2021;2021:1–10. doi: 10.1155/2021/5576600
   Web of Science ®Google Scholar
• Karthik R, Malghan RL, Kara F, et al. Influence of support vector regression (SVR) on cryogenic face milling. Adv Mater Sci Eng. 2021;2021:1–18. doi: 10.1155/2021/9984369
   Web of Science ®Google Scholar
• Shi KN, Zhang DH, Liu N, et al. A novel energy consumption model for milling process considering tool wear progression. J Clean Prod. 2018;184:152–159. doi: 10.1016/j.jclepro.2018.02.239
   Web of Science ®Google Scholar
• Pimenov DY, Mia M, Gupta MK, et al. Resource saving by optimization and machining environments for sustainable manufacturing: A review and future prospects. Renew Sust Energ Rev. 2022;166:112660. doi: 10.1016/j.rser.2022.112660
   Web of Science ®Google Scholar
• Wang Q, Liu F, Li C. An integrated method for assessing the energy efficiency of machining workshop. J Clean Prod. 2013;52:122–133. doi: 10.1016/j.jclepro.2013.03.020
   Web of Science ®Google Scholar
• Yansong G, Loenders J, Duflou J, et al. Optimization of energy consumption and surface quality in finish turning. Procedia CIRP. 2012;1:512–517. doi: 10.1016/j.procir.2012.04.091
   Google Scholar
• Karthikeyan R, Senthil Kumar V, Punitha A, et al. An integrated ANN-GA approach to Maximize the material removal rate and to minimize the surface roughness of wire cut EDM on titanium alloy. Adv Mater Process Technol. 2020;8(1):1–11. doi: 10.1080/2374068X.2020.1793267
   Google Scholar
• Bousnina K, Hamza A, Ben Yahia N. An approach to the influence of the machining process on power consumption and surface quality during the milling of 304L austenitic stainless steel. J Mech Eng Sci. 2020;16(3):9093–9109. doi: 10.15282/jmes.16.3.2022.11.0720
   Web of Science ®Google Scholar
• Sonawane SA, Kulkarni LM. Multi-response optimization of wire electrical discharge machining for titanium grade-5 by weighted principal component analysis. International Journal Of Engineering And Technology Innovation. 2018;8(2):133–145.
   Web of Science ®Google Scholar
• Yongchuan L, Jianyou H, Jueyu W, et al. Modeling and optimization of high-grade compacted graphite iron milling force and surface roughness via response surface methodology. Aust J Mech Eng. 2017;16(1):50–57. doi: 10.1080/14484846.2017.1296531
   Web of Science ®Google Scholar
• Manoj K, Satsangi PS. A study on machining performance of wire electric discharge grinding (WEDG) process during machining of tungsten alloy micro-tools. Sådhanå. 2021;46(2):69. doi: 10.1007/s12046-021-01595-3
   Web of Science ®Google Scholar
• Yan J, Li L. Multi-objective optimization of milling parameters–the trade-offs between energy, production rate and cutting quality. J Clean Prod. 2013;52:462–471. doi: 10.1016/j.jclepro.2013.02.030
   Web of Science ®Google Scholar
• Stefan V, Kolev I, Ivanov K, et al. Empirical models for specific energy consumption and optimization of cutting parameters for minimizing energy consumption during turning. J Clean Prod. 2014;80:139–149. doi: 10.1016/j.jclepro.2014.05.099
   Web of Science ®Google Scholar
• Carmita CN. Optimization of cutting parameters for minimizing energy consumption in turning of AISI 6061 T6 using Taguchi methodology and ANOVA. J Clean Prod. 2013;53:195–203. doi: 10.1016/j.jclepro.2013.03.049
   Web of Science ®Google Scholar
• Neugebauer R, Schubert A, Reichmann B, et al. Influence exerted by tool properties on the energy efficiency during drilling and turning operations. CIRP J Manuf Sci Technol. 2011;4(2):161–169. doi: 10.1016/j.cirpj.2011.06.011
   Google Scholar
• Neugebauer R, Drossel W, Wertheim R, et al. Resource and energy efficiency in machining using high performance and Hybrid processes. Procedia CIRP. 2012;1:3–16. doi: 10.1016/j.procir.2012.04.002
   Google Scholar
• Diaz NRedelsheimer E Dornfeld D. Energy consumption characterization and reduction strategies for milling machine tool use. Proceedings of the 18th CIRP International Conference on Life Cycle Engineering. Technische Universität Braunschweig, Braunschweig, Germany; May 2nd–4th. 2011. 263–267
   Google Scholar
• Aherwar A, Singh A, Patnaik A. Prediction of effect of tungsten filled Co-30Cr-4Mo-1Ni metal matrix biomedical composite alloy on sliding wear peculiarity using Taguchi methodology and ANN. Adv Mater Process Technol. 2017;3(4):665–688. doi: 10.1080/2374068X.2017.1366104
   Google Scholar
• Bousnina K, Hamza A, Yahia NB. Energy optimization for milling 304L steel using artificial intelligence methods. International Journal Of Automotive And Mechanical Engineering. 2022;19(3):9928–9938. doi: 10.15282/ijame.19.3.2022.05.0765
   Web of Science ®Google Scholar
• Kara F, Karabatak M, Ayyıldız M, et al. Effect of machinability, microstructure and hardness of deep cryogenic treatment in hard turning of AISI D2 steel with ceramic cutting. J Mater Res Technol. 2020;9(1):969–983. doi: 10.1016/j.jmrt.2019.11.037
   Google Scholar
• Kara F, Aslantaş K, Cicek A. Prediction of cutting temperature in orthogonal machining of AISI 316L using artificial neural network. Appl Soft Comput. 2016;38:64–74. doi: 10.1016/j.asoc.2015.09.034
   Web of Science ®Google Scholar
• Kara F, Aslantas K, Çiçek A. ANN and multiple regression method-based modelling of cutting forces in orthogonal machining of AISI 316L stainless steel. Neural Comput Appl. 2015;26(1):237–250. doi: 10.1007/s00521-014-1721-y
   Web of Science ®Google Scholar
• Erkan Ö, Işık B, Çiçek A, et al. Prediction of damage factor in end milling of glass fibre reinforced plastic composites using artificial neural network. Appl Compos Mater. 2013;20(4):517–536. doi: 10.1007/s10443-012-9286-3
   Web of Science ®Google Scholar
• Chinchanikar S, Shinde S, Gaikwad V, et al. ANN modelling of surface roughness of FDM parts considering the effect of hidden layers, neurons, and process parameters. Adv Mater Process Technol. 2022;1–11.
   Google Scholar
• Kabil AO, Kaynak Y, Saruhan H, et al. Multi-objective optimization of cutting parameters for machining process of Ni-Rich NiTiHf high-temperature shape memory alloy using genetic algorithm. Shape Memory And Superelasticity. 2021;7(2):270–279. doi: 10.1007/s40830-021-00328-z
   Web of Science ®Google Scholar