Bhuwal, Akash Singh
(2023)
Failure analysis and mechanical behaviors of metamaterials.
PhD thesis, University of Nottingham.
Abstract
In recent years, mechanical metamaterials have been explored for their tunable nature with the continual development of Additive Manufacturing (AM) technologies. As a result, the failure mechanisms of the metamaterials and their mechanical behaviours under different boundary and environmental conditions have been investigated. Firstly, failure mechanisms of AM originated imperfections in the metamaterials have been investigated. In this, three types of imperfection have been considered in the numerical modelling of the metamaterials: distorted struts, missing struts, and strut diameter variation. Then a novel numerical framework was developed to overcome computational difficulties within the existing numerical approaches beyond the elastic region. Three modes of microscopic localisation were observed in metamaterials before failure: crushing band, shear band and void coalescence. The results showed that a clear separation exists between the three modes of localisation depending upon the type and level of defects and loading condition. Under compressive loading, all metamaterials failed due to the crushing band; the distorted lattices are prone to shear band localisation with increased distortion, whereas missing lattices majorly fail due to void coalescence at high missing struts defect.
The study on imperfect metamaterials has suggested that it can exhibit either ductile, damage-tolerant behaviours or sudden, catastrophic failure mode, depending on the distribution of the introduced disorderliness. Thus, a data-driven approach has been developed, combining deep-learning and global optimisation algorithms, to tune the distribution of the disorderliness/imperfections to achieve damage-tolerant metamaterial designs. A case study on the metamaterial created from a periodic Face Centred Cubic (FCC) lattice has demonstrated that the optimised metamaterials can generate high-quality designs with improved ductility, enabling them to sustain larger deformations without failure at a lower cost to strength and stiffness. This has been validated by an experimental study on an optimised metamaterial design. The results showed that the optimized designs can achieve up to 100% increase in ductility at the expense of less than 5% stiffness and 8-15% tensile strength.
Finally, the creep behaviour of Inconel 718 metamaterial has been investigated at an elevated temperature to understand the effects of the microstructural defects. A Kachanov's damage modelling has been used to predict the creep performance of the metamaterials. The analysis and experimental results indicated that the creep resistance of the metamaterials is dependent on the microstructure and loading conditions. The creep behaviour of the metamaterials is significantly different from that of the bulk material due to their complex microstructure.
Overall, this study contributes to the development of mechanical metamaterials with improved mechanical properties using AM technologies. The neural network-based data-driven methodology offers a promising avenue for designing high-quality metamaterials that are cost-effective and have desirable mechanical properties. The results of this study have significant implications for various applications, including structural engineering, biomechanics, and aerospace engineering, including in understanding, and designing for the creep behavior of Inconel 718 metamaterials.
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