Print Email Facebook Twitter Predictive analytical modelling and experimental validation of processing maps in additive manufacturing of nitinol alloys Title Predictive analytical modelling and experimental validation of processing maps in additive manufacturing of nitinol alloys Author Zhu, Jia-Ning (TU Delft Team Vera Popovich) Borisov, Evgenii (Peter the Great Saint-Petersburg Polytechnic University) Liang, X. (TU Delft Team Marcel Hermans) Farber, Eduard (Peter the Great Saint-Petersburg Polytechnic University) Hermans, M.J.M. (TU Delft Team Marcel Hermans) Popovich, V. (TU Delft Team Vera Popovich; Peter the Great Saint-Petersburg Polytechnic University) Date 2021 Abstract Nitinol (NiTi) shape memory alloys fabricated by Laser Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) have attracted much attention in recent years, as compared with conventional manufacturing processes it allows to produce Nitinol parts with high design complexity. Avoidance of defects during L-PBF is crucial for the production of high quality Nitinol parts. In this study, analytical models predicting melt pool dimensions and defect formation criteria were synergistically used to develop processing maps demonstrating boundary conditions for the formation of such defects, as balling, keyhole-induced pores, and lack of fusion. Experimental validation has demonstrated that this method can provide an accurate estimation and guide manufacturability of defect-free Nitinol alloys. Moreover, the crack formation phenomena were experimentally analysed, which showed that a low linear energy density (El) should be chosen to avoid cracks in the optimized process windows. Based on model predictions and experimental calibrations, Nitinol samples with a relative density of more than 99% were successfully fabricated. Subject Analytical modelDefect formationLaser powder bed fusionNitinol alloysProcess optimization To reference this document use: http://resolver.tudelft.nl/uuid:04e9184a-cbef-4712-b8f8-e88ba757d7e6 DOI https://doi.org/10.1016/j.addma.2020.101802 ISSN 2214-8604 Source Additive Manufacturing, 38 Part of collection Institutional Repository Document type journal article Rights © 2021 Jia-Ning Zhu, Evgenii Borisov, X. Liang, Eduard Farber, M.J.M. Hermans, V. Popovich Files PDF 1_s2.0_S221486042031174X_main.pdf 15.93 MB Close viewer /islandora/object/uuid:04e9184a-cbef-4712-b8f8-e88ba757d7e6/datastream/OBJ/view