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

Liuchao Jin; Xiaoya Zhai; Jingchao Jiang; Kang Zhang; Wei-Hsin Liao

doi:10.1117/12.3014941

9 May 2024 Optimizing stimuli-based 4D printed structures: a paradigm shift in programmable material response

Liuchao Jin, Xiaoya Zhai, Jingchao Jiang, Kang Zhang, Wei-Hsin Liao

Proceedings Volume 12949, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2024; 129491D (2024) https://doi.org/10.1117/12.3014941
Event: SPIE Smart Structures + Nondestructive Evaluation, 2024, Long Beach, California, United States

Abstract

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.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Liuchao Jin, Xiaoya Zhai, Jingchao Jiang, Kang Zhang, and Wei-Hsin Liao "Optimizing stimuli-based 4D printed structures: a paradigm shift in programmable material response", Proc. SPIE 12949, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2024, 129491D (9 May 2024); https://doi.org/10.1117/12.3014941

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