TY - JOUR
T1 - A deep learning approach for inverse design of gradient mechanical metamaterials
AU - Zeng, Qingliang
AU - Zhao, Zeang
AU - Lei, Hongshuai
AU - Wang, Panding
N1 - Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2023/2/15
Y1 - 2023/2/15
N2 - Mechanical metamaterials with unique micro-architectures possess excellent physical properties in terms of stiffness, toughness, vibration isolation, and thermal expansion. Meanwhile, meta-structures in organisms or geography operate efficiently under complex service conditions thanks to their heterogeneous and gradient distribution of naturally evolved micro-architectures that are difficult to obtain by forward design. In this paper a multi-network deep learning system that satisfies the different design property requirements of microstructures is proposed, and the network predicts the configuration with 99.09% accuracy. The analogy between color space and mechanical parameter space is used to transform parametric design into pixel matching. The microstructures are prepared by AM (additive manufacturing) and their properties are verified by DIC (Digital Image Correlation) experiments (the property error of the structures was less than 2%). Multiscale inverse design of multifunctional and gradient mechanical metamaterials is realized, with special attention payed to the automatic customization of biomimetic structures. The design flow takes only 2 s and the geometric connectivity between microstructure units is considered to ensure compatibility between adjacent microstructures for AM. The proposed design strategy accelerates the emergence of high-performance structures, and provides a reference for topology optimization design of mechanical metamaterials.
AB - Mechanical metamaterials with unique micro-architectures possess excellent physical properties in terms of stiffness, toughness, vibration isolation, and thermal expansion. Meanwhile, meta-structures in organisms or geography operate efficiently under complex service conditions thanks to their heterogeneous and gradient distribution of naturally evolved micro-architectures that are difficult to obtain by forward design. In this paper a multi-network deep learning system that satisfies the different design property requirements of microstructures is proposed, and the network predicts the configuration with 99.09% accuracy. The analogy between color space and mechanical parameter space is used to transform parametric design into pixel matching. The microstructures are prepared by AM (additive manufacturing) and their properties are verified by DIC (Digital Image Correlation) experiments (the property error of the structures was less than 2%). Multiscale inverse design of multifunctional and gradient mechanical metamaterials is realized, with special attention payed to the automatic customization of biomimetic structures. The design flow takes only 2 s and the geometric connectivity between microstructure units is considered to ensure compatibility between adjacent microstructures for AM. The proposed design strategy accelerates the emergence of high-performance structures, and provides a reference for topology optimization design of mechanical metamaterials.
KW - Additive manufacturing
KW - Deep learning
KW - Functionally gradient materials
KW - Metamaterial
KW - Topology optimization
UR - http://www.scopus.com/inward/record.url?scp=85142513306&partnerID=8YFLogxK
U2 - 10.1016/j.ijmecsci.2022.107920
DO - 10.1016/j.ijmecsci.2022.107920
M3 - Article
AN - SCOPUS:85142513306
SN - 0020-7403
VL - 240
JO - International Journal of Mechanical Sciences
JF - International Journal of Mechanical Sciences
M1 - 107920
ER -