Stress-Driven Algorithm for Fiber Alignment in Smart Materials for Controlled Deformation in 4D-Printed Soft Robotics

Abstract

This work proposes a path generation policy for self-actuating soft grippers by converting external deformation conditions into intrinsic load conditions. This transformation enables anisotropic material orientation control of functional materials — which can deform under stimuli — aligning with the deformation requirements of soft grippers to enhance controllability. Given a desired deformation for an arbitrary geometry, finite element method (FEM) analysis is used to determine the internal stress distribution. The second-order stress tensor is transformed into a traction vector field, guiding the alignment of material anisotropy for optimal deformation. A computational framework is developed to generate smooth, continuous printing paths by integrating along the vector field, ensuring internal morphology control of the target geometry. The proposed method is validated through FEM analysis, demonstrating a positional deviation rate of under 5% relative to the largest geometric feature in each test case across various tested shapes and deformation conditions. The results demonstrate that the algorithm effectively generates 4D printing paths that enable soft grippers to achieve target deformations with high matching rate.