Biomimicry, adapting and implementing nature’s designs provides an adequate first-order solution to achieving superior mechanical properties. However, the design space is too vast even using biomimetic designs as prototypes for optimization. Here, we propose a new approach to design hierarchical materials using machine learning, trained with a database of hundreds of thousands of structures from finite element analysis, together with a self-learning algorithm for discovering high-performing materials where inferior designs are phased out for superior candidates. Results show that our approach can create microstructural patterns that lead to tougher and stronger materials, which are validated through additive manufacturing and testing. We further show that machine learning can be used as an alternative method of coarse-graining – analyzing and designing materials without the use of full microstructural data. This novel paradigm of smart additive manufacturing can aid in the discovery and fabrication of new material designs boasting orders of magnitude increase in computational efficacy over conventional methods.

Learn more (opens external site)

 

Leave a Reply

Submit a Team Connection

Click here to submit a new Bioinspired Design Connection (you must be logged in first).

Browse Team Connections

Choose by category, team or week:

BioDesign Connections by Category (2020 – 2022)

by Team (2022 only)

by Week (2022 only)

Most Recent Connections

Connection Interactions

Recent Comments

  1. to reduce the impact of car accidents, it may be possible to study the force diverting physics of cockroaches to…

Top Voted Connections