Results 291 to 300 of about 4,830,203 (376)

Comparative Wear and Friction Analysis of Sliding Surface Materials for Hydrostatic Bearing under Oil Supply Failure Conditions

open access: yesAdvanced Engineering Materials, EarlyView.
Hydrostatic bearings excel in high‐precision applications, but their performance hinges on a continuous external supply. This study evaluates various material combinations for sliding surfaces to mitigate damage during supply failures or misalignment and to discover the most effective materials identified for enhancing the reliability and efficiency of
Michal Michalec   +6 more
wiley   +1 more source

A Novel Simulation Approach for Damage Evolution during Tailored Forming

open access: yesAdvanced Engineering Materials, EarlyView.
Traditional damage models are struggling to accurately and efficiently simulate large‐scale three‐dimensional models with a great number of degrees of freedoms. A new gradient‐enhanced damage model based on the extended Hamilton principle can significantly reduce the computation time while ensuring mesh‐independence which is suitable to use in tailored
Fangrui Liu   +2 more
wiley   +1 more source

Transformative bioprinting: 4D printing and its role in the evolution of engineering and personalized medicine. [PDF]

open access: yesDiscov Nano
Mathur V   +5 more
europepmc   +1 more source

Analysis of Temperature and Stress Distribution on the Bond Properties of Hybrid Tailored Formed Components

open access: yesAdvanced Engineering Materials, EarlyView.
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

Static and Dynamic Behavior of Novel Y‐Shaped Sandwich Beams Subjected to Compressive Loadings: Integration of Supervised Learning and Experimentation

open access: yesAdvanced Engineering Materials, EarlyView.
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

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