New Paper Alert!
Our newest paper "Scalable path planning and reduced order modeling for temperature optimization in Direct Energy Deposition" has just been published in Additive Manufacturing
Dimensional inaccuracies in Direct Energy Deposition often stem from uneven heat distribution, a challenge when dealing with complex geometries. Our latest study tackles this with a fast and efficient framework that leverages GPyro, a machine learning-based reduced-order thermal model, and the Fast Fourier Transform to optimize toolpaths in a thermally informed way. The result? Up to 100× faster thermal field evaluations and planning, enabling high-quality, defect-free parts even for intricate geometries. A step closer to smarter, more precise additive manufacturing.
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