Differentiable graph-structured models for inverse design of lattice materials

Materials possessing flexible physico-chemical properties that adapt on-demand to the hostile environmental conditions of deep space will become essential in defining the future of space exploration. A promising venue for inspiration towards the design of environment-specific materials is in the intricate micro-architectures and lattice geometry found throughout nature. However, the immense design space covered by such irregular topologies is challenging to probe analytically. For this reason, most synthetic lattice materials have to date been based on periodic architectures instead. Here, we propose a computational approach using a graph representation for both regular and irregular lattice materials. Our method uses differentiable message passing algorithms to calculate mechanical properties, and therefore allows using automatic differentiation to adjust both the geometric structure and attributes of individual lattice elements to design materials with desired properties. The introduced methodology is applicable to any system representable as a heterogeneous graph, including other types of materials.

On-the-fly 3D metrology of volumetric additive manufacturing

Additive manufacturing techniques are revolutionizing product development by enabling fast turnaround from design to fabrication. However, the throughput of the rapid prototyping pipeline remains constrained by print optimization, requiring multiple iterations of fabrication and ex-situ metrology. Despite the need for a suitable technology, robust in-situ shape measurement of an entire print is not currently available with any additive manufacturing modality. Here, we address this shortcoming by demonstrating fully simultaneous 3D metrology and printing. We exploit the dramatic increase in light scattering by a photoresin during gelation for real-time 3D imaging of prints during tomographic volumetric additive manufacturing. Tomographic imaging of the light scattering density in the build volume yields quantitative, artifact-free 3D + time models of cured objects that are accurate to below 1% of the size of the print. By integrating shape measurement into the printing process, our work paves the way for next-generation rapid prototyping with real-time defect detection and correction.