TY - JOUR
T1 - Bayesian Machine Learning in metamaterial design
T2 - Fragile becomes supercompressible
AU - Bessa, Miguel A.
AU - Głowacki, Piotr
AU - Houlder, Michael
PY - 2019
Y1 - 2019
N2 - Designing future-proof materials goes beyond a quest for the best. The next generation of materials needs to be adaptive, multipurpose, and tunable. This is not possible by following the traditional experimentally guided trial-and-error process, as this limits the search for untapped regions of the solution space. Here, a computational data-driven approach is followed for exploring a new metamaterial concept and adapting it to different target properties, choice of base materials, length scales, and manufacturing processes. Guided by Bayesian machine learning, two designs are fabricated at different length scales that transform brittle polymers into lightweight, recoverable, and supercompressible metamaterials. The macroscale design is tuned for maximum compressibility, achieving strains beyond 94% and recoverable strengths around 0.1 kPa, while the microscale design reaches recoverable strengths beyond 100 kPa and strains around 80%. The data-driven code is available to facilitate future design and analysis of metamaterials and structures (https://github.com/mabessa/F3DAS).
AB - Designing future-proof materials goes beyond a quest for the best. The next generation of materials needs to be adaptive, multipurpose, and tunable. This is not possible by following the traditional experimentally guided trial-and-error process, as this limits the search for untapped regions of the solution space. Here, a computational data-driven approach is followed for exploring a new metamaterial concept and adapting it to different target properties, choice of base materials, length scales, and manufacturing processes. Guided by Bayesian machine learning, two designs are fabricated at different length scales that transform brittle polymers into lightweight, recoverable, and supercompressible metamaterials. The macroscale design is tuned for maximum compressibility, achieving strains beyond 94% and recoverable strengths around 0.1 kPa, while the microscale design reaches recoverable strengths beyond 100 kPa and strains around 80%. The data-driven code is available to facilitate future design and analysis of metamaterials and structures (https://github.com/mabessa/F3DAS).
KW - additive manufacturing
KW - data-driven design
KW - deep learning
KW - machine learning
KW - optimization
UR - http://www.scopus.com/inward/record.url?scp=85074010581&partnerID=8YFLogxK
U2 - 10.1002/adma.201904845
DO - 10.1002/adma.201904845
M3 - Article
AN - SCOPUS:85074010581
SN - 0935-9648
VL - 31
JO - Advanced Materials
JF - Advanced Materials
IS - 48
M1 - 1904845
ER -