Towards end-to-end optimization in multimaterial 3D printing

Multimaterial 3D printing enables the fabrication of functionally graded components, but optimizing their spatial material distribution alongside structural topology remains a formidable challenge due to high-dimensional design spaces and complex constitutive modeling. This paper presents an end-to-end computational framework integrating sparsified physics-augmented neural networks with finite-element-based topology optimization. By extracting closed-form, composition-aware hyperelastic constitutive laws from experimental data, this approach facilitates exact symbolic differentiation via the adjoint state method implemented with FEniCSx, efficiently circumventing the bottlenecks of applying neural network constitutive models. This pipeline is deployed on soft robotic gripper applications, demonstrating continuous composition optimization for highly anisotropic contact responses, and the concurrent optimization of macroscopic topology and material distribution under non-failure stretch constraints. This methodology could replace laborious empirical prototyping, establishing interpretable machine-learning models as practical, robust design primitives for advanced multimaterial additive manufacturing.

Autonomous Material Composite Morphing Wing

Aeronautics research has continually sought to achieve the adaptability and morphing performance of avian wings, but in practice, wings of all scales continue to use the same hinged control-surface embodiment. Recent research into compliant and bio-inspired mechanisms for morphing wings and control surfaces has indicated promising results, though often these are mechanically complex, or limited in the number of degrees-of-freedom (DOF) they can control. Seeking to improve on these limitations, we apply a new paradigm denoted Autonomous Material Composites to the design of avian-scale morphing wings. With this methodology, we reduce the need for complex actuation and mechanisms, and allow for three-dimensional placement of stretchable fiber optic strain gauges (Optical Lace) throughout the metamaterial structure. This structure centers around elastomeric conformal lattices, and by applying functionally-graded warping and thickening to this lattice, we allow for local tailoring of the compliance properties to fit the desired morphing. As a result, the wing achieves high-deformation morphing in three DOF: twist, camber, and extension/compression, with these morphed shapes effectively modifying the aerodynamic performance of the wing, as demonstrated in low-Reynolds wind tunnel testing. Our sensors also successfully demonstrate differentiable trends across all degrees of morphing, enabling the future state estimation and control of this wing.