Apr 2022
Abstract:
A truly bioinspired approach to computational design optimization should follow the energetically favorable natural paradigm of “minimum inventory with maximum diversity”. In this talk, I will present a topological design optimization framework inspired by constructive regression of trabecular bone – a natural process of network connectivity optimization occurring early in skeletal development. During trabecular network optimization, the original excessively connected network undergoes incremental pruning of redundant elements, resulting in a functional and adaptable structure operating at lowest metabolic cost. This biological network topology optimization algorithm was recapitulated by first designing in silico an excessively connected network in which elements are dimension-independent linear connections among nodes. Based on bioinspired regression principles, least-loaded connections were iteratively pruned upon simulated loading. Evolved networks were produced along this optimization trajectory when pre-set convergence criteria were met. These biomimetic networks were compared to each other, and to the reference network derived from mature trabecular bone. The results replicated the natural network optimization algorithm in uniaxial compressive loading. However, following triaxial loading, the optimization algorithm resulted in lattice networks that were more stretch-dominated than the reference network, and more capable of uniform load distribution. As assessed by 3D printing and mechanical testing, this network optimization framework opens new possibilities for parametric material design.
Bio:
Ammar Alsheghri is a postdoctoral fellow in the department of Computer and Software Engineering at Polytechnique Montreal (affiliated with the University of Montreal). He completed his PhD in Materials Engineering from McGill University in 2020, his M.Sc. in Mechanical Engineering from Masdar Institute of Khalifa University, in 2015, and his B.Sc. in Mechanical Engineering from Khalifa University in 2013. His research interests are in bioinspired computational design of materials, the confluence of deep learning and 3D design optimization for dental and biomedical applications, biomechanics, topology optimization in the solid mechanics framework for material design, computational and experimental mechanics for biomaterials, and phenomenological modeling of damage and healing of materials. He has published over 15 highly influential research papers and articles on a diverse range of topics. Ammar received highly competitive scholarships from the Institute for Data Valorization of Quebec (IVADO) and Research Fund of Quebec - Nature and Technologies (FRQNT) to fund his postdoctoral and PhD studies, respectively.
Registration link to join the seminar:
https://kaust.zoom.us/webinar/register/WN_mxGB1dg5QF6rOlreJF90aA