Výsledky vyhledávání - "Narra, Sneha P."

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Statistical analysis to assess porosity equivalence with uncertainty across additively manufactured parts for fatigue applications

Autor: Miner, Justin P., Narra, Sneha Prabha

Previous work on fatigue prediction in Powder Bed Fusion - Laser Beam has shown that the estimate of the largest pore size within the stressed volume is correlated with the resulting fatigue behavior in porosity-driven failures. However, single value

Externí odkaz: http://arxiv.org/abs/2411.03401

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Trajectory Optimization for Spatial Microstructure Control in Electron Beam Metal Additive Manufacturing

Autor: Khrenov, Mikhail, Tan, Moon, Fitzwater, Lauren, Hobdari, Michelle, Narra, Sneha Prabha

Metal additive manufacturing (AM) opens the possibility for spatial control of as-fabricated microstructure and properties. However, since the solid state diffusional transformations that drive microstructure outcomes are governed by nonlinear ODEs i

Externí odkaz: http://arxiv.org/abs/2410.18207

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ADDOPT: An Additive Manufacturing Optimal Control Framework Demonstrated in Minimizing Layer-Level Thermal Variance in Electron Beam Powder Bed Fusion

Autor: Khrenov, Mikhail, Templeton, William Frieden, Narra, Sneha Prabha

Additive manufacturing (AM) techniques hold promise but face significant challenges in process planning and optimization. The large temporal and spatial variations in temperature that can occur in layer-wise AM lead to thermal excursions, resulting i

Externí odkaz: http://arxiv.org/abs/2406.07408

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Data Scoping: Effectively Learning the Evolution of Generic Transport PDEs

Autor: Chen, Jiangce, Xu, Wenzhuo, Xu, Zeda, Gutiérrez, Noelia Grande, Narra, Sneha Prabha, McComb, Christopher

Transport phenomena (e.g., fluid flows) are governed by time-dependent partial differential equations (PDEs) describing mass, momentum, and energy conservation, and are ubiquitous in many engineering applications. However, deep learning architectures

Externí odkaz: http://arxiv.org/abs/2405.01319

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Limits of dispersoid size and number density in oxide dispersion strengthened alloys fabricated with powder bed fusion-laser beam

Autor: Wassermann, Nathan A., Li, Yongchang, Myers, Alexander J., Kantzos, Christopher A., Smith, Timothy M., Beuth, Jack L., Malen, Jonathan A., Shao, Lin, McGaughey, Alan J. H., Narra, Sneha P.

Previous work on additively-manufactured oxide dispersion strengthened alloys focused on experimental approaches, resulting in larger dispersoid sizes and lower number densities than can be achieved with conventional powder metallurgy. To improve the

Externí odkaz: http://arxiv.org/abs/2310.12416

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Capturing Local Temperature Evolution during Additive Manufacturing through Fourier Neural Operators

Autor: Chen, Jiangce, Xu, Wenzhuo, Baldwin, Martha, Nijhuis, Björn, Boogaard, Ton van den, Gutiérrez, Noelia Grande, Narra, Sneha Prabha, McComb, Christopher

High-fidelity, data-driven models that can quickly simulate thermal behavior during additive manufacturing (AM) are crucial for improving the performance of AM technologies in multiple areas, such as part design, process planning, monitoring, and con

Externí odkaz: http://arxiv.org/abs/2307.01804

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