Data-Driven Bending Elasticity Design by Shell Thickness

Journal Article (2016)
Author(s)

Xiaoting Zhang (The Chinese University of Hong Kong, Dartmouth College)

Xinyi Le (Shanghai Jiao Tong University, The Chinese University of Hong Kong)

Zihao Wu (The Chinese University of Hong Kong)

Charlie C.L. Wang (TU Delft - Materials and Manufacturing, The Chinese University of Hong Kong)

Research Group
Materials and Manufacturing
DOI related publication
https://doi.org/10.1111/cgf.12972
More Info
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Publication Year
2016
Language
English
Research Group
Materials and Manufacturing
Issue number
5
Volume number
35
Pages (from-to)
157-166

Abstract

We present a method to design the deformation behavior of 3D printed models by an interactive tool, where the variation of bending elasticity at different regions of a model is realized by a change in shell thickness. Given a soft material to be used in 3D printing, we propose an experimental setup to acquire the bending behavior of this material on tubes with different diameters and thicknesses. The relationship between shell thickness and bending elasticity is stored in an echo state network using the acquired dataset. With the help of the network, an interactive design tool is developed to generate non-uniformly hollowed models to achieve desired bending behaviors. The effectiveness of this method is verified on models fabricated by different 3D printers by studying whether their physical deformation can match the designed target shape.

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