Deep learning aided inverse design of the buckling-guided assembly for 3D frame structures

点云 屈曲 计算机科学 帧(网络) 反向 有限元法 深度学习 人工神经网络 点(几何) 人工智能 浮点型 算法 结构工程 几何学 工程类 数学 电信
作者
Tianqi Jin,Xu Cheng,Shiwei Xu,Yuchen Lai,Yihui Zhang
出处
期刊:Journal of The Mechanics and Physics of Solids [Elsevier BV]
卷期号:179: 105398-105398 被引量:13
标识
DOI:10.1016/j.jmps.2023.105398
摘要

Buckling-guided assembly of three-dimensional (3D) mesostructures from pre-defined 2D precursor patterns has arisen increasing attention, owing to the compelling advantages in developing 3D electronic devices and systems with novel functionalities and/or capabilities. Establishments of rational inverse design methods that allow accurate mapping of the target 3D configuration onto the initial 2D precursor pattern are crucial to the widespread application of buckling-guided assembly methods. While a few methods (e.g., those based on theoretical models and generic algorithms) have been reported for the inverse design of 3D frame structures with interconnected ribbons, limitations still exist in their applicable 3D geometries or computational efficiency. In this work, we report an effective inverse design method based on the point-cloud deep learning neural network (DLNN) model for the buckling-guided assembly of 3D frame structures. A structure-based database in the point-cloud form is established based on massive finite element analyses (FEA) of postbuckling deformations for diverse 2D precursor patterns with different numbers of intersections. The well-trained deep learning models assisted by transfer learning strategy utilizing datasets in the constructed database are verified to establish the end-to-end implicit mapping between the 3D frame structure and corresponding 2D precursor pattern. Computational and experimental demonstrations over a bunch of complexly shaped structures, including those resembling 3D shapes of real-world objects, illustrate the high efficiency and accuracy of the proposed deep learning aided inverse design method. In comparison to previously reported methods based on genetic algorithms, the proposed inverse design method can save much more computational efforts, and does not require the initial guess of the 2D precursor pattern. Furthermore, the proposed inverse design method offers an excellent extensibility, as the size and diversity of the structure-based database can be continuously expanded in a sustainable manner, with the future development of buckling-guided assembly methods.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
123发布了新的文献求助10
刚刚
刚刚
llllllll完成签到,获得积分10
2秒前
jzw完成签到,获得积分20
2秒前
dorothy_meng完成签到,获得积分10
2秒前
3秒前
3秒前
羊花花发布了新的文献求助20
3秒前
Z123完成签到,获得积分10
3秒前
帝休完成签到 ,获得积分10
4秒前
5秒前
LL完成签到,获得积分10
5秒前
jzw发布了新的文献求助10
5秒前
cen发布了新的文献求助10
5秒前
5秒前
6秒前
6秒前
6秒前
7秒前
宋宋宋2发布了新的文献求助10
7秒前
8秒前
8秒前
8秒前
9秒前
as完成签到,获得积分10
9秒前
自由大叔发布了新的文献求助10
9秒前
阿双发布了新的文献求助20
10秒前
涂图发布了新的文献求助30
10秒前
10秒前
顺利的战斗机应助liaomr采纳,获得10
10秒前
AlvinCZY发布了新的文献求助10
11秒前
可耐的青雪完成签到,获得积分10
11秒前
Ausna发布了新的文献求助10
12秒前
hahaha发布了新的文献求助10
12秒前
14秒前
14秒前
kuma完成签到,获得积分10
14秒前
行走的土豆完成签到,获得积分10
15秒前
纸抽盒发布了新的文献求助200
15秒前
15秒前
高分求助中
Picture Books with Same-sex Parented Families: Unintentional Censorship 700
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
Effective Learning and Mental Wellbeing 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3975165
求助须知:如何正确求助?哪些是违规求助? 3519595
关于积分的说明 11198781
捐赠科研通 3255912
什么是DOI,文献DOI怎么找? 1798001
邀请新用户注册赠送积分活动 877343
科研通“疑难数据库(出版商)”最低求助积分说明 806298