Deep learning of buckling instability in geometrically symmetry-breaking kirigami

Zhang, Yunce1,2; Wang, Yafei3,4; Tao, Qiang5; Liu, Yuanpeng1,2; Wang, Changguo1,2

2024-10-15

10.1016/j.ijmecsci.2024.109331

INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES   影响因子和分区

0020-7403

1879-2162

Kirigami, subjected to escalating strain, frequently exhibits pronounced instability, coupled with remarkable flexibility and extraordinary extensibility. This behavior holds significant relevance for domains associated with malleable and reconfigurable surfaces, including stretchable electronics and modifiable functional devices. Nonetheless, conventional design methodologies, anchored in geometric symmetry and governed by minimum energy principles, tend to manifest buckling instabilities restricted to symmetric and anti-symmetric modes. scrutinize the mechanisms of buckling behavior that disrupt geometric symmetry and comprehend the influence of geometry on programmability during reconfiguration, we propose an innovative strategy for kirigami's design. This strategy capitalizes on advanced deep learning methodologies, employing convolutional neural networks (CNNs) for categorizing buckling modes and recurrent neural networks (RNNs) for prognosticating constitutive relationships. Our approach furnishes a programmable design solution adept at identifying optimal kirigami patterns, characterized by their superior tensile strength and distinct buckling conformations, thereby fulfilling a diverse array of functional necessities. Our results illustrate that the proposed method displays a level of precision in distinguishing between buckling modes of geometric symmetry and patterns that deviate from such symmetry. The buckling mode space has been extended and rediscovered, allowing unique modes to have the potential to be adopted into functional devices. Additionally, it demonstrates minimal losses predicting constitutive relationships. Intriguingly, we discovered that tensile responses are geometry-centric and adjustable. Buckling modes showcase a dependency on geometry, with certain geometric parameters either significantly augmenting the sensitivity of buckling modalities or causing the buckling instability modes to become apathetic and unresponsive. Guided by the principle of target-led pattern parameter design, proffer prospective tactics for the design of kirigami capable of delivering the desired mechanical performance. Moreover, we explore the feasibility of employing alternative biological materials in these designs.

Geometry-dependent kirigami Buckling instability Mechanics programmability Symmetry breaking Deep learning

SCI ; EI

英语

通讯

National Natural Science Foundation of China["12172102","12002109","12372097","12202105"] ; Fundamental Research Funds for the Central Universities[HIT.OCEF.2022013] ; Science Foundation of the National Key Laboratory of Science and Technology on Advanced Composites in Special Environments[JCKYS2022603C015] ; Natural Science Foundation of Chongqing, China[cstc2021jcyj-msxmX1035] ; China National Postdoctoral Program for Innovative Talents[BX20220086] ; China Postdoctoral Science Foundation[2022M710751] ; Shanghai Post-doctoral Excellence Program[2022732]

Engineering ; Mechanics

Engineering, Mechanical ; Mechanics

WOS:001266420600001

PERGAMON-ELSEVIER SCIENCE LTD

20242816659109

Biological materials ; Buckling ; Recurrent neural networks ; Stability ; Tensile strength

Biological Materials and Tissue Engineering:461.2 ; Mathematics:921

ENGINEERING

Web of Science

1.Harbin Inst Technol, Natl Key Lab Sci & Technol Adv Composites Special, Harbin 150001, Peoples R China
2.Harbin Inst Technol, Ctr Composite Mat & Struct, Harbin 150001, Peoples R China
3.Fudan Univ, Dept Aeronaut & Astronaut, Shanghai 200433, Peoples R China
4.Southern Univ Sci & Technol, Dept Mech & Energy Engn, Innovat Soft Mat Engn Lab, Shenzhen 518055, Peoples R China
5.Qingdao Univ, Coll Mech & Elect Engn, Qingdao 266071, Peoples R China

Zhang, Yunce,Wang, Yafei,Tao, Qiang,et al. Deep learning of buckling instability in geometrically symmetry-breaking kirigami[J]. INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES,2024,280.

Zhang, Yunce,Wang, Yafei,Tao, Qiang,Liu, Yuanpeng,&Wang, Changguo.(2024).Deep learning of buckling instability in geometrically symmetry-breaking kirigami.INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES,280.

Zhang, Yunce,et al."Deep learning of buckling instability in geometrically symmetry-breaking kirigami".INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES 280(2024).