Session: 16-01-01: NSF-funded Research (Grad & Undergrad)

Paper Number: 77464

Start Time: Wednesday, 02:25 PM

77464 - Evolutionary Algorithm-Guided Voxel-Encoding Printing of Functional Hard-Magnetic Soft Active Materials 

Hard-magnetic soft active materials (hmSAMs), functional soft composites that consist of hard-magnetic particles such as NdFeB embedded in soft polymeric matrices, have attracted a great number of research interests due to their fast-transforming, untethered control as well as excellent programmability, promising applications in soft robotics, active metamaterials, morphing devices, and biomedical devices. To enable functional actuation of hmSAMs with complex deformation, a well-designed magnetization distribution needs to be encoded into the structure. To increase the programmability and fabrication flexibility of hmSAMs, the additive manufacturing technique of direct-ink-write (DIW) was developed. However, the DIW printing-based fabrication of hmSAM parts and structures only permits programmable magnetic direction with a constant magnetic density. Also, the existing designs rely on the brute-force approach to generate the assignment of magnetization direction distribution, which can only produce intuitional deformations. These two factors significantly limit the design space and the application potentials of hmSAMs.

To address these issues, we introduce a voxel-encoding DIW printing method to program both the magnetic density and direction distributions during the hmSAM printing. In this printing method, each voxel consists of multiple hmSAM layers, and by controlling the printing direction of each layer in the voxel, both the magnetic density and direction of the entire voxel can be programmed. For example, a three-layer voxel can be encoded with seven variations, and with more layers in the printed voxel, finer magnetic density tunability is feasible, which dramatically enhances the programmability of hmSAMs. Although the voxel-encoding DIW printing provides a solution for programming the hmSAMs, the assignment of magnetization in individual voxels to achieve a predetermined shape change is impossible via the brute-force approach due to a large number of variations and the high complexity of the voxel setting. Thus, we introduce an evolutionary algorithm (EA)-guided design strategy to address this challenge of programming magnetization distributions for complex curvature distributions and dynamic motions. To achieve a highly autonomous inverse design of magnetization distributions for voxel-encoding DIW printing, the desired deformation is fed into the EA-guided design strategy. After iterations of EA-based optimization, the deformation of the structure from the finite element simulation evolves towards the target morphology and curvature distribution. When a specific criterion is reached, the EA process terminates and exports the magnetization distribution for further printing. With the new EA-guided voxel-encoding DIW printing technique, we demonstrate the functional hmSAMs that produce complicated shape morphing with desired curvature distributions for advanced applications such as biomimetic motions. These demonstrations indicate that the proposed EA-guided voxel-encoding DIW printing method can significantly broaden the application potentials of the hmSAMs.

Presenting Author: Shuai Wu Stanford University

Authors:

Shuai Wu Stanford University
Craig Hamel Georgia Institute of Technology
H. Jerry Qi Georgia Institute of Technology
Ruike Zhao Stanford University