Targeted mechanical and energy absorption properties of 3D printed aluminium metamaterials

The potential of 3D-printed AlSi10Mg auxetic structures for diverse mechanical and energy-absorbing needs remains untapped. This article reveals a multi-criteria framework for the laser powder bed fused (L-PBF) - v architecture considering elastic modulus (E), yield strength (Oy), specific energy absorption (SEA), peak crush force (PCF) and crush force efficiency (CFE). The framework seamlessly combines trial data, multi-criteria decision-making, and performance indicators. Five auxetic structures were 3D-printed, characterised for mechanical and energy absorption traits within a 0.17–0.26 relative density range. The outcomes revealed a range of values for various parameters, including the Poisson’s ratio (−0.03 to −0.22), porosity (80.87–87.60 %), CFE (33–83 %), elastic modulus (100–632 MPa), yield strength (1.8–10 MPa), and SEA (0.5–6.8 kJ/kg). The reliability of these structures was ensured through a meticulous selection process based on an extensive literature review and empirical validation. To address the limitations of theoretical models, our work goes beyond theoretical predictions by experimentally validating these properties and integrating advanced methodologies such as the ‘analytic hierarchy process’ (AHP) and the ‘technique for order of preference by similarity to ideal solution’ (TOPSIS). This allows us to determine the best-performing auxetic architecture. The decision-making process was informed by five user-defined parameters prioritised in the order of CFE> -v > E> Oy > SEA based on their relative closeness identifying AUX5 as the best performing auxetic architecture. This study introduces an innovative method for crafting scenario-based auxetic architectures with varying performance levels based on their relative importance.

Citation

Singh, M. Arjunan, A., Baroutaji, A., WanniarachchiC. et al (2025) Targeted mechanical and energy absorption properties of 3D printed aluminium metamaterials, Next Materials, 7, Article No. 100356. DOI: https://doi.org/10.1016/j.nxmate.2024.100356

Type

Journal article

Language

en

Description

© 2024 The Authors. Published by Elsevier. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1016/j.nxmate.2024.100356

ISSN

2949-8228

EISSN

2949-8228

Collections

Faculty of Science and Engineering