Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment

By Grace X. Gu, Chun-Teh Chen, Deon J. Richmond, Markus J. Buehler. 2018

425 citations recorded by the source corpus.

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.

Subfields: Automotive Engineering, Building and Construction, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/cfc1d089990f9b8652dade0dc8e221589c6430ce

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

  1. Lynn H. Kaack
  2. Felix Creutzig
  3. Demis Hassabis

In Building and Construction

  1. Lynn H. Kaack
  2. Felix Creutzig

In Materials Chemistry

  1. Yoshua Bengio
  2. Alexandre Lacoste

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