Optimizing stimuli-based 4D printed structures: a paradigm shift in programmable material response

Jin, Liuchao1,2,3; Zhai, Xiaoya4; Jiang, Jingchao5; Zhang, Kang1; Liao, Wei-Hsin1,6

10.1117/12.3014941

2024

Conference on Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems

0277-786X

1996-756X

978-1-5106-7204-8

SENSORS AND SMART STRUCTURES TECHNOLOGIES FOR CIVIL, MECHANICAL, AND AEROSPACE SYSTEMS 2024

12949

MAR 25-28, 2024

null,Long Beach,CA

1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA

SPIE-INT SOC OPTICAL ENGINEERING

This paper introduces a methodology for optimizing 4D printing design through the integration of Residual Neural Network (ResNet) and Genetic Algorithms (GA). Departing from traditional forward design approaches, our inverse design methodology addresses both the forward prediction and inverse optimization problems. ResNet efficiently predicts the performance of 4D-printed parts given their design, while GA optimizes material allocation and stimuli distribution to achieve desired configurations. The ResNet model exhibits high accuracy, converging to a small error (10-3), as validated across diverse cases. The GA demonstrates effectiveness in achieving optimal or near-optimal solutions, illustrated through case studies shaping parts into a parabola and a sinusoid. Experimental results align with optimized and simulated outcomes, showcasing the practical applicability of our approach in 4D printing design optimization.

4D printing inverse design additive manufacturing machine learning residual neural network genetic algorithm

其他

英语

CPCI-S ; EI

Chinese University of Hong Kong[3110174] ; Research Grants Council, Hong Kong Special Administrative Region, China[C4074-22G]

Construction & Building Technology ; Engineering

Construction & Building Technology ; Engineering, Multidisciplinary

WOS:001239374700038

Web of Science

1.Chinese Univ Hong Kong, Dept Mech & Automat Engn, Hong Kong, Peoples R China
2.Southern Univ Sci & Technol, Shenzhen Key Lab Soft Mech & Smart Mfg, Shenzhen 518055, Peoples R China
3.Southern Univ Sci & Technol, Dept Mech & Energy Engn, Shenzhen 518055, Peoples R China
4.Univ Sci & Technol China, Sch Math Sci, Hefei 230026, Peoples R China
5.Univ Exeter, Dept Engn, Exeter, England
6.Chinese Univ Hong Kong, Inst Intelligent Design & Mfg, Hong Kong, Peoples R China

Jin, Liuchao,Zhai, Xiaoya,Jiang, Jingchao,et al. Optimizing stimuli-based 4D printed structures: a paradigm shift in programmable material response[C]. 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA:SPIE-INT SOC OPTICAL ENGINEERING,2024.