arXiv subjects

Derek Aranguren van Egmond

Publications and source records attributed to Derek Aranguren van Egmond.

5 recordsLinked to original sources

arXiv2025

Automatic exposure volumetric additive manufacturing

Tomographic volumetric additive manufacturing (VAM) achieves high print speed and design freedom by continuous volumetric light patterning. This differs from traditional vat photopolymerization techniques that use brief sequential (2D) plane- or (1D) point-localized exposures. The drawback to volumetric light patterning is the small exposure window. Overexposure quickly leads to cured out-of-part voxels due to the nonzero background dose arising from light projection through the build volume. For tomographic VAM, correct exposure time is critical to achieving high repeatability, however, we find that correct exposure time varies by nearly 40% depending on resin history. Currently, tomographic VAM exposure is timed based on subjective human determination of print completion, which is tedious and yields poor repeatability. Here, we implement a robust auto exposure routine for tomographic VAM using real-time processing of light scattering data, yielding accurate and repeatable prints without human intervention. The resulting print fidelity and repeatability approaches, and in some cases, exceeds that of commercial resin 3D printers. We show that auto exposure VAM generalizes well to a wide variety of print geometries with small positive and negative features. The repeatability and accuracy of auto exposure VAM allows for building multi-part objects, fulfilling a major requirement of additive manufacturing technologies.

physics.optics↗

arXiv2023

Differentiable graph-structured models for inverse design of lattice materials

Architected materials possessing physico-chemical properties adaptable to disparate environmental conditions embody a disruptive new domain of materials science. Fueled by advances in digital design and fabrication, materials shaped into lattice topologies enable a degree of property customization not afforded to bulk materials. A promising venue for inspiration toward their design is in the irregular micro-architectures of nature. However, the immense design variability unlocked by such irregularity is challenging to probe analytically. Here, we propose a new computational approach using graph-based representation for regular and irregular lattice materials. Our method uses differentiable message passing algorithms to calculate mechanical properties, therefore allowing automatic differentiation with surrogate derivatives to adjust both geometric structure and local attributes of individual lattice elements to achieve inversely designed materials with desired properties. We further introduce a graph neural network surrogate model for structural analysis at scale. The methodology is generalizable to any system representable as heterogeneous graphs.

cond-mat.mtrl-sci↗

arXiv2022

NIDN: Neural Inverse Design of Nanostructures

In the recent decade, computational tools have become central in material design, allowing rapid development cycles at reduced costs. Machine learning tools are especially on the rise in photonics. However, the inversion of the Maxwell equations needed for the design is particularly challenging from an optimization standpoint, requiring sophisticated software. We present an innovative, open-source software tool called Neural Inverse Design of Nanostructures (NIDN) that allows designing complex, stacked material nanostructures using a physics-based deep learning approach. Instead of a derivative-free or data-driven optimization or learning method, we perform a gradient-based neural network training where we directly optimize the material and its structure based on its spectral characteristics. NIDN supports two different solvers, rigorous coupled-wave analysis and a finite-difference time-domain method. The utility and validity of NIDN are demonstrated on several synthetic examples as well as the design of a 1550 nm filter and anti-reflection coating. Results match experimental baselines, other simulation tools, and the desired spectral characteristics. Given its full modularity in regard to network architectures and Maxwell solvers as well as open-source, permissive availability, NIDN will be able to support computational material design processes in a broad range of applications.

physics.comp-ph↗

arXiv2022

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.

eess.IV↗

arXiv2021

The benefits of structural disorder in natural cellular solids

Structural cellular materials in nature, such as wood, trabecular bone, corals, and dentin combine complex biological functions with structural roles, such as skeletal support and impact protection1,2. They feature complex structural hierarchies from nano- to macroscale that enable optimization of both strength and toughness (flaw tolerance) simultaneously3-9. These hierarchies typically exhibit structural disorder in the arrangement of pores. The degree of disorder, however, has not been systematically quantified before, and its role in the mechanical performance of cellular biomaterials is generally unknown. Here we have applied Voronoi tessellations to quantify the cell size variation in 2D cross-sections of biological and engineered cellular materials, using a disorder parameter (d) ranging between 0 (highly disordered) to 1.0 (regular hexagonal honeycomb). We demonstrate that various plant, fungi, and animal cellular materials show characteristic ranges of disorder. Using 3D printed analogues and numerical methods, we demonstrate experimentally a range of pseudo-order (d=0.6 to 0.8) that exhibits a > 30% increase in fracture toughness (and equivalent strength) compared to hexagonal honeycombs (d=1.0) of equal density. Our results show this range of disorder is similar to that identified in the biological examples, which suggests convergent evolution. This optimal degree of structural disorder limits catastrophic failure, providing an evolutionary advantage for organism survival. Distributed structural damage limits cracks below a maximum threshold size and also enables tissue repair mechanisms after trauma. Our work shows that tailored disorder should be considered as a new design paradigm for digitally fabricated, lightweight architected materials to improve damage tolerance.

physics.app-ph↗