Researchers from the SUTD Digital Manufacturing and Design Centre have developed a novel computational design optimization and manufacturing toolchain for the creation of complex 3D curved rod structures with spatially variable material distributions. These structures exhibit active deformation behavior, which is enabled by the shape memory effect of 3D printed photopolymers – so-called 4D printing.
The framework developed by research fellow Oliver Weeger, PhD student Benjamin Kang and professors Sai-Kit Yeung and Martin L. Dunn optimizes the cross-sectional properties of a 3-dimensional rod structure, in particular the Young’s modulus, such that under given loading conditions it can obtain one or more target shapes resulting from geometrically nonlinear deformation. Due to the shape memory effect of 3D printed polymer composites, the structure can then actively deform back from this target shape to the original, 3D-printed shape.
Outline of overall pipeline based on the Armadillo use case. A surface mesh is converted into a rod structure, then forces and a target shape are defined for the shape memory training phase, and subsequently the material distribution in terms of Young’s modulus is optimized to match deformation with the target shape. Finally, the rod mesh with optimized material distribution is converted into a voxel-level representation and 3D printed.
The approach includes a novel algorithm to generate physical realizations from the computational design model, which allows their direct fabrication via printing of shape memory composites with voxel-level compositional control with a multi-material 3D printer. The design and manufacture digital toolchain allows the continuous variation of multiple active materials as a route to optimize mechanical as well as active behavior of a structure, without changing the original shape of the 3D rod structure, which is not possible with a single material. The entire design-fabrication-test approach is demonstrated and its capabilities are illustrated with examples including 3D characters, personalized medical applications, and complex structures that exhibit instabilities during their nonlinear deformation.
Active Armadillo figure: (a) Comparison of training behavior of computational and actual 3D printed models with initial uniform and optimized material distributions. While the uniform material version bends to the front during training, the optimized one remains straight. (b) Snapshots of the full active shape recovery process from deformed target (left) back to printed shape (right) inside a hot water bath.
This work was recently published in 3D Printing and Additive Manufacturing journal and the full article can be downloaded here: http://online.liebertpub.com/doi/10.1089/3dp.2016.0039. More information can also be found on the project website and in the YouTube video.