Results 271 to 280 of about 997,096 (396)

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

Adhesive Interface of Adhesive Products

open access: yesSeikei-Kakou, 2012
Yutaka Tosaki, Tadatoshi Nakanishi
openaire   +3 more sources

A Different Perspective on the Solid Lubrication Performance of Black Phosphorous: Friend or Foe?

open access: yesAdvanced Engineering Materials, EarlyView.
Researchers investigate black phosphorous (BP) as a standalone solid lubricant coating through ball‐on‐disc linear‐reciprocating sliding experiments in dry conditions. Testing on different metals shows BP doesn't universally reduce friction and wear. However, it achieves 33% friction reduction on rougher iron surfaces and 23% wear reduction on aluminum.
Matteo Vezzelli   +5 more
wiley   +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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