Parameters prediction in additively manufactured Al-Cu alloy using back propagation neural network

References

• Armstrong M, Mehrabi H, Naveed N. An overview of modern metal additive manufacturing technology. J Manuf Process. 2022;84:1001–1029. doi:10.1016/j.jmapro.2022.10.060
   Web of Science ®Google Scholar
• Zhan X, Wang Q, Wang L, et al. Regionalization of microstructure and mechanical properties of Ti6Al4V transition area fabricated by WAAM-LMD hybrid additive manufacturing. J Alloy Compd. 2022;929:167345. doi:10.1016/j.jallcom.2022.167345
   Web of Science ®Google Scholar
• Srivastava M, Rathee S, Tiwari A, et al. Wire arc additive manufacturing of metals: a review on processes, materials and their behaviour. Mater Chem Phys. 2023;294:126988. doi:10.1016/j.matchemphys.2022.126988
   Web of Science ®Google Scholar
• Li Q, Wang F, Wang G, et al. Wire and arc additive manufacturing of lightweight metal components in aeronautics and astronautics. Aeronaut Manuf Technol. 2018;61:74–89.
   Google Scholar
• Liu J, Xu Y, Ge Y, et al. Wire and arc additive manufacturing of metal components: a review of recent research developments. Int J Adv Manuf Tech. 2020;111:149–198. doi:10.1007/s00170-020-05966-8
   Web of Science ®Google Scholar
• Fu R, Tang S, Lu J, et al. Hot-wire arc additive manufacturing of aluminum alloy with reduced porosity and high deposition rate. Mater Des. 2021;199:109370. doi:10.1016/j.matdes.2020.109370
   Web of Science ®Google Scholar
• Bartsch H, Kühne R, Citarelli S, et al. Fatigue analysis of wire arc additive manufactured (3D printed) components with unmilled surface. Structures. 2021;31:576–589. doi:10.1016/j.istruc.2021.01.068
   Web of Science ®Google Scholar
• Qiu Z, Dong B, Wu B, et al. Tailoring the surface finish, dendritic microstructure and mechanical properties of wire arc additively manufactured Hastelloy C276 alloy by magnetic arc oscillation. Addit Manuf. 2021;48:102397. doi:10.1016/j.addma.2021.102397
   Web of Science ®Google Scholar
• Palani P, Murugan N. Optimization of weld bead geometry for stainless steel claddings deposited by FCAW. J Mater Process Tech. 2007;190:291–299. doi:10.1016/j.jmatprotec.2007.02.035
   Web of Science ®Google Scholar
• Karaoğlu S, Seçgin A. Sensitivity analysis of submerged arc welding process parameters. J Mater Process Tech. 2008;202:500–507. doi:10.1016/j.jmatprotec.2007.10.035
   Web of Science ®Google Scholar
• Sondagar H, Bhadauria S, Sharma V. Artificial neural network (ANN) based prediction of process parameters in additive manufacturing. IOP Conf Ser. 2021;1136:012026. doi:10.1088/1757-899X/1136/1/012026
   Google Scholar
• McCulloch W, Pitts W. A logical calculus of the ideas immanent in nervous activity. B Math Biol. 1943;5:115–133.
   Google Scholar
• Shevchik S, Kenel C, Leinenbach C, et al. Acoustic emission for in situ quality monitoring in additive manufacturing using spectral convolutional neural networks. Addit Manuf. 2018;21:598–604. doi:10.1016/j.addma.2017.11.012
   Web of Science ®Google Scholar
• Zhang Y, Hong G, Ye D, et al. Extraction and evaluation of melt pool, plume and spatter information for powder-bed fusion AM process monitoring. Mater Des. 2018;156:458–469. doi:10.1016/j.matdes.2018.07.002
   Web of Science ®Google Scholar
• Scime L, Beuth J. A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process. Addit Manuf. 2018;24:273–286. doi:10.1016/j.addma.2018.09.034
   Web of Science ®Google Scholar
• Alhaddad W, He M, Halabi Y, et al. Optimizing the material and printing parameters of the additively manufactured fiber-reinforced polymer composites using an artificial neural network model and artificial bee colony algorithm. Structures. 2022;46:1781–1795. doi:10.1016/j.istruc.2022.10.134
   Web of Science ®Google Scholar
• Ling Y, Ni J, Antonissen J, et al. Numerical prediction of microstructure and hardness for low carbon steel wire arc additive manufacturing components. Simul Model Pract Theory. 2023;122:102664. doi:10.1016/j.simpat.2022.102664
   Web of Science ®Google Scholar
• Leirmo T, Martinsen K. Evolutionary algorithms in additive manufacturing systems: discussion of future prospects. Procedia CIRP. 2019;81:671–676. doi:10.1016/j.procir.2019.03.174
   Google Scholar
• Joy R, Kuzhivelil J, Kannan R, et al. ANN modelling of additively manufactured carbon fibre integrated ABS. Mater Today. 2022;72:3137–3143. doi:10.1016/j.matpr.2022.10.004
   Google Scholar
• Patpatiya P, Shastri A, Sharma S, et al. ANN-predictive modeling and GA-optimization for minimizing dimensional tolerance in polyjet additive manufacturing. CIRP J Manuf Sci Technol. 2022;38:320–339. doi:10.1016/j.cirpj.2022.05.004
   Web of Science ®Google Scholar
• Teharia R, Singari R, Kumar H. Optimization of process variables for additive manufactured PLA based tensile specimen using taguchi design and artificial neural network (ANN) technique. Mater Today. 2021;56:3426–3432. doi:10.1016/j.matpr.2021.10.376
   Google Scholar
• Li W, Zhang H, Wang G, et al. Deep learning based online metallic surface defect detection method for wire and arc additive manufacturing. Robot Comput Integr Manuf. 2023;80:102470. doi:10.1016/j.rcim.2022.102470
   Web of Science ®Google Scholar
• Oh W, Lee C, Kim D. Prediction of deposition bead geometry in wire arc additive manufacturing using machine learning. J Mater Res Technol. 2022;20:4283–4296. doi:10.1016/j.jmrt.2022.08.154
   Google Scholar
• Snell R, Sam T, Lova C, et al. Methods for rapid pore classification in metal additive manufacturing. JOM. 2020;72:101–109. doi:10.1007/s11837-019-03761-9
   Web of Science ®Google Scholar
• Thompson S, Bian L, Shamsaei N, et al. An overview of direct laser deposition for additive manufacturing; part I: transport phenomena, modeling and diagnostics. Addit Manuf. 2015;8:36–62. doi:10.1016/j.addma.2015.07.001
   Web of Science ®Google Scholar
• Fang Z, Wang R, Wang M, et al. Effect of reconstruction algorithm on the identification of 3D printing polymers based on hyperspectral CT technology combined with artificial neural network. Materials (Basel). 2020;13:1963. doi:10.3390/ma13081963
   PubMed Web of Science ®Google Scholar
• Ahmadi A, Mirzaeifar R, Moghaddam N, et al. Effect of manufacturing parameters on mechanical properties of 316L stainless steel parts fabricated by selective laser melting: a computational framework. Mater Design. 2016;112:328–338. doi:10.1016/j.matdes.2016.09.043
   Web of Science ®Google Scholar
• Chan B. Modelling gas metal arc weld geometry using artificial neural network technology. Can Metall Quart. 1999;38:43–51.
   Web of Science ®Google Scholar
• Kim I, Jong Y, Park C, et al. A study on prediction of bead height in robotic arc welding using a neural network. J Mater Process Tech. 2002;130:229–234. doi:10.1016/S0924-0136(02)00803-8
   Web of Science ®Google Scholar
• Geng C, Lin Y, Wang X, et al. Modeling research of artificial neural networks of YAG laser welding parameters. Trans China Weld Inst. 2001;22:37–40.
   Google Scholar
• Xiong J, Zhang W, Hu Q, et al. Optimizing of welding parameters of YAG laser welding titanium alloy based on artificial neural net. Appl Laser. 2001;21:243–246.
   Google Scholar
• Karmuhilan M, Anoop K. Intelligent process model for bead geometry prediction in WAAM. Mater Today. 2018;5:24005–24013. doi:10.1016/j.matpr.2018.10.193
   Google Scholar
• Farias F, Filho J, Oliveira V. Prediction of the interpass temperature of a wire arc additive manufactured wall: FEM simulations and artificial neural network. Addit Manuf. 2021;48:102387. doi:10.1016/j.addma.2021.102387
   Web of Science ®Google Scholar
• Kril C, Birringer R. Estimating grain-size distributions in nanocrystalline materials from X-ray diffraction profile analysis. Philos Mag. 1998;77:621–640. doi:10.1080/01418619808224072
   Web of Science ®Google Scholar
• He C, Wei J, Li Y, et al. Improvement of microstructure and fatigue performance of wire-arc additive manufactured 4043 aluminum alloy assisted by interlayer friction stir processing. J Mater Sci Technol. 2023;133:183–194. doi:10.1016/j.jmst.2022.07.001
   Web of Science ®Google Scholar
• Pan Y, Wang Y, Zhou P, et al. Activation functions selection for BP neural network model of ground surface roughness. J Intell Manuf. 2020;31:1825–1836. doi:10.1007/s10845-020-01538-5
   Web of Science ®Google Scholar
• Xu B, Zhang H, Wang Z, et al. Model and algorithm of BP neural network based on expanded multichain quantum optimization. Math Probl Eng. 2015;2015:1–11.
   Web of Science ®Google Scholar
• Chen B, Niu X. Quantum neural network with improved quantum learning algorithm. Int J Theor Phys. 2020;59:1978–1991. doi:10.1007/s10773-020-04470-9
   Web of Science ®Google Scholar
• Sheela K, Deepa S. Review on methods to fix number of hidden neurons in neural networks. Math Probl Eng. 2013: 1–11. doi:10.1155/2013/425740
   Web of Science ®Google Scholar
• Li Z, Li Y. A comparative study on the prediction of the BP artificial neural network model and the ARIMA model in the incidence of AIDS. BMC Med Inform Decis. 2020;20:143. doi:10.1186/s12911-020-01157-3
   PubMed Web of Science ®Google Scholar
• Yang B, Su X, Wang Y. BP neural network optimization based on an improved genetic algorithm. IEEE Xplore. 2002;1:64–68.
   Google Scholar
• Wang T, Kang J, Darnell M, et al. Ultrasonically assisted hot-wire arc additive manufacturing process of AA7075 metal matrix nanocomposite. J Alloy Compd. 2023;936:168298. doi:10.1016/j.jallcom.2022.168298
   Web of Science ®Google Scholar