Issue 68

M. Sarparast et alii, Frattura ed Integrità Strutturale, 68 (2024) 340-356; DOI: 10.3221/IGF-ESIS.68.23

[24] Prieto, A., Prieto, B., Ortigosa, E.M., Ros, E., Pelayo, F., Ortega, J. and Rojas, I., (2016). Neural networks: An overview of early research, current frameworks and new challenges. Neurocomputing, 214, pp.242-268. [25] Nartu, M.S.K.K.Y., Dasari, S., Sharma, A., Mantri, S.A., Sharma, S., Pantawane, M.V., McWilliams, B., Cho, K., Dahotre, N.B. and Banerjee, R., (2021). Omega versus alpha precipitation mediated by process parameters in additively manufactured high strength Ti–1Al–8V–5Fe alloy and its impact on mechanical properties. Materials Science and Engineering: A, 821, p.141627. [26] Tsai, K.M. and Luo, H.J., (2017). An inverse model for injection molding of optical lens using artificial neural network coupled with genetic algorithm. Journal of Intelligent Manufacturing, 28, pp.473-487. [27] Wang, F., Zhao, J. and Zhu, N., (2016). Constitutive equations and ANN approach to predict the flow stress of Ti-6Al 4V alloy based on ABI tests. Journal of Materials Engineering and Performance, 25, pp.4875-4884. [28] Nimbagal, V., Banapurmath, N.R., Sajjan, A.M., Patil, A.Y. and Ganachari, S.V., (2021). Studies on hybrid bio nanocomposites for structural applications. Journal of Materials Engineering and Performance, 30(9), pp.6461-6480. [29] Maleki, E., Bagherifard, S. and Guagliano, M., (2021). Application of artificial intelligence to optimize the process parameters effects on tensile properties of Ti-6Al-4V fabricated by laser powder-bed fusion. International Journal of Mechanics and Materials in Design, pp.1-24. [30] Wang, C., Tan, X.P., Tor, S.B. and Lim, C.S., (2020). Machine learning in additive manufacturing: State-of-the-art and perspectives. Additive Manufacturing, 36, p.101538. [31] Chinchanikar, S., Shinde, S., Gaikwad, V., Shaikh, A., Rondhe, M. and Naik, M., (2022). ANN modelling of surface roughness of FDM parts considering the effect of hidden layers, neurons, and process parameters. Advances in Materials and Processing Technologies, pp.1-11. [32] Mehrpouya, M., Gisario, A., Nematollahi, M., Rahimzadeh, A., Baghbaderani, K.S. and Elahinia, M., (2021). The prediction model for additively manufacturing of NiTiHf high-temperature shape memory alloy. Materials today communications, 26, p.102022. [33] Jimenez-Martinez, M., and Alfaro-Ponce, M. (2021). Effects of synthetic data applied to artificial neural networks for fatigue life prediction in nodular cast iron. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 43(1), p.10. [34] Trivedi, P., Vansjalia, R., Erra, S., Narayanan, S. and Nagaraju, D., (2023). A fuzzy CRITIC and fuzzy WASPAS-based integrated approach for wire arc additive manufacturing (WAAM) technique selection. Arabian Journal for Science and Engineering, 48(3), pp.3269-3288. [35] Popovich, A., Sufiiarov, V., Borisov, E. and Polozov, I.A., (2015). Microstructure and mechanical properties of Ti-6Al 4V manufactured by SLM. Key Engineering Materials, 651, pp.677-682. [36] Kumar, S.L., Aravind, H.B. and Hossiney, N., (2019). Digital image correlation (DIC) for measuring strain in brick masonry specimen using Ncorr open source 2D MATLAB program. Results in Engineering, 4, p.100061. [37] Butler, D. and Woolliams, P., (2020). Standards in additive manufacturing. In Precision Metal Additive Manufacturing (pp. 133-156). CRC Press. [38] Wilson-Heid, A. E. (2021). Additively Manufactured Metals: Effect of Microstructure and Defects on Multiaxial Plasticity and Fracture Behavior. The Pennsylvania State University. [39] Wilson-Heid, A.E., Wang, Z., McCornac, B. and Beese, A.M., (2017). Quantitative relationship between anisotropic strain to failure and grain morphology in additively manufactured Ti-6Al-4V. Materials Science and Engineering: A, 706, pp.287-294. [42] Shafaie, M., Khademi, M., Sarparast, M. and Zhang, H., (2022). Modified GTN parameters calibration in additive manufacturing of Ti-6Al-4 V alloy: a hybrid ANN-PSO approach. The International Journal of Advanced Manufacturing Technology, 123(11), pp.4385-4398. [43] F. Abaqus, (2014), Dassault systemes simulia corporation, Providence, Rhode Island, USA. [44] Barfeh, A., Hashemi, R., Safdarian, R., Rahmatabadi, D., Aminzadeh, A. and Sattarpanah Karganroudi, S., (2023). Predicting the forming limit diagram of the fine-grained AA 1050 sheet using GTN damage model with experimental verifications. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 237(14), pp.2325-2335. [45] Lutz, M. (2001). Programming python. " O'Reilly Media, Inc.". [40] V. MATLAB, 9.6. 0.1072779 (R2019a), The MathWorks Inc.: Natick, MA, USA. [41] Needleman, A. and Tvergaard, V., (1992). Analyses of plastic flow localization in metals.

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