Results 171 to 180 of about 3,889,100 (366)
Parameter optimization in differential geometry based solvation models. [PDF]
Wang B, Wei GW.
europepmc +1 more source
The 2007 Midwest Geometry Conference included a panel discussion devoted to open problems and the general direction of future research in fields related to the main themes of the conference.
Lawrence J. Peterson
doaj
Developing process parameters for the laser‐based Powder Bed Fusion of metals can be a tedious task. Based on melt pool depth, the process parameters are transferable to different laser scan speeds. For this, understanding the melt pool scaling behavior is essential, particularly for materials with high thermal diffusivity, as a change in scaling ...
Markus Döring+2 more
wiley +1 more source
Book Review: An Introduction to Differential Geometry with Use of the Tensor Calculus [PDF]
Gustav A. Hedlund
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Hybrid materials enable high‐performance components but are challenging to process. This study explores an inductive heating concept with spray cooling for steel–aluminum specimens in a two‐step process including friction welding and cup backward extrusion.
Armin Piwek+7 more
wiley +1 more source
Unsupervised eye pupil localization through differential geometry and local self-similarity matching. [PDF]
Leo M+3 more
europepmc +1 more source
This perspective article explores an innovative powder metallurgical approach to producing high‐nitrogen steels by utilizing a mixture of stainless steel and Si3N4. This mixture undergoes hot isostatic pressing followed by direct quenching. The article also examines adapting this method to laser powder bed fusion (PBF‐LB/M) to overcome nitrogen ...
Louis Becker+5 more
wiley +1 more source
Fundamental forms in the projective differential geometry of $m$-parametric families of hypersurfaces of the second order in the $n$-dimensional space [PDF]
Akitsugu Kawaguchi
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In this study, the mechanical response of Y‐shaped core sandwich beams under compressive loading is investigated, using deep feed‐forward neural networks (DFNNs) for predictive modeling. The DFNN model accurately captures stress–strain behavior, influenced by design parameters and loading rates.
Ali Khalvandi+4 more
wiley +1 more source