Compression behaviour of TPMS-filled stainless steel tubes

0559566 - ÚTAM 2023 RIV CH eng J - Journal Article
Novak, N. - Kytýř, Daniel - Rada, Václav - Doktor, T. - Al-Ketan, O. - Rowshan, R. - Vesenjak, M. - Ren, Z.
Compression behaviour of TPMS-filled stainless steel tubes.
Materials Science and Engineering A Structural Materials Properties Microstructure and Processing. Roč. 852, September (2022), č. článku 143680. ISSN 0921-5093. E-ISSN 1873-4936
Institutional support: RVO:68378297
Keywords : cellular structure * triply periodical minimal surface * TPMS * TPMS-filled tube * compressive loading * experimental testing * computational modelling * homogenised core
OECD category: Materials engineering
Impact factor: 6.4, year: 2022
Method of publishing: Open access
https://doi.org/10.1016/j.msea.2022.143680

One of the most promising options for future crashworthiness applications is thin-walled tubes filled with various cellular materials (e.g. metal foam). Of higher interest are the shell-based lattices, which have lately gained popularity due to their superior qualities over strut-based lattices. In this work, we investigate the mechanical response of foam-filled tubes where the tube's core was represented by Triply Periodic Minimal Surface (TPMS) diamond lattices. Samples made of stainless steel 316L comprising the diamond lattice core, empty tubes, and in-situ TPMS-filled tubes were additively manufactured and mechanically tested under compressive loading. As-fabricated welded tubes and ex-situ TPMS-filled tubes were also analysed and compared. Under the axial loading, the ex-situ and in-situ TPMS-filled tubes showed very similar behaviour. Enhanced energy absorption up to 21% and 44% compared to the sum of empty tubes and the core responses was noted. The energy absorption enhancement of 12% in the case of transversal loading is limited to in-situ TPMS-filled tubes, where the connection between the tube and core prevents the tube's walls from buckling. Computational models with homogenised core were developed and validated based on the experimental data. These straightforward, fast, and accurate computational models can be efficiently used for large-scale real-life applications, e.g. crash and impact.
Permanent Link: https://hdl.handle.net/11104/0333025