Laser powder bed fusion of bio-inspired metamaterials for energy absorption applications: A review

A significant amount of research has been done in the last few decades to reduce the risk of injury for occupants and the structures that are subjected to impact loading. Metamaterials have been proven to be useful in energyabsorbing structures to improve a structure 's crashworthiness performance by reducing the negative impacts during collision. The metal additive manufacturing industry, especially Laser Powder Bed Fusion (LPBF), has made it easier to produce complex metamaterials with remarkable mechanical characteristics like lightweight, high specific strength, and effective energy absorption. This review paper investigates the transformative potential of bio-inspired metamaterial designs, which are additively manufactured using LPBF machines, for use in protective energy-absorbing structures. First, biomimicry in engineering is briefly discussed. The review focuses on the energy absorption performance of different designs, like thin-walled structures and different bio-inspired metamaterials. It discusses the effects of base metal, process conditions, and manufacturing defects. Optimization methods to enhance the design and crashworthiness of these bio-inspired energy absorbers are investigated. Various characterization methodologies including experimental techniques and numerical simulations, are highlighted, with a particular emphasis on integrating manufacturing defects into simulations. Finally, possible applications and future trends in aerospace, automotive, construction, and medical applications are reviewed. Despite decades of research into energy absorbers, there remains a lack of a comprehensive review on the use of additively manufactured metamaterials as energy absorbers, particularly those inspired by nature. This review paper addresses this gap by examining recent studies in the field, assessing the effectiveness of various bioinspired metamaterial designs, their crashworthiness, and the associated characterization methods under different loading scenarios.