Results 251 to 260 of about 1,838,834 (287)

Predictability of wear status provided by fractal dimensions of wear particles

Journal of Materials Science Letters, 1996
Wear particles are produced when materials rub against each other. It has been identified that wear particles carry substantial information about the wear processes experienced by a material working in a tribological environment. Through careful examination of the wear particles, the wear mechanisms and the cause of wear can be successfully deduced [I].
MingQiu Zhang, Le Song, HanMin Zeng, K. Friedrich   +3 more
openaire   +1 more source

Predictions of cam wear profiles

1989
This paper describes a computational method for predicting the profiles into which cams and followers wear in service. The method is conceptually simple. However, there are several salient features required to model the subtleties of tribological interactions. The first step is to solve the kinematic constraint problem for the cam and follower.
openaire   +1 more source

Prediction of Fabric Wear

Textile Research Journal, 1971
G. Alon, L.I. Weiner
openaire   +1 more source

Physics-informed meta learning for machining tool wear prediction

Journal of Manufacturing Systems, 2022
Jinjiang Wang, Robert Gao
exaly  

A review of vibration-based gear wear monitoring and prediction techniques

Mechanical Systems and Signal Processing, 2023
Qing Ni, Michael Beer, Ke Feng
exaly  

Tool wear identification and prediction method based on stack sparse self-coding network

Journal of Manufacturing Systems, 2023
Xianli Liu, Yiyuan Qin
exaly  

Wear Stage Judgment and Wear Failure Prediction Based on Dissipative Theory of Wear

2023
Haoran Liao, Ying Liu, Hongju Li, Xiang Zhao   +3 more
openaire   +1 more source

Predictive Models for Sliding Wear

1988
Wear is defined as “damage to a solid surface, generally involving progressive loss of material due to relative motion between that surface and a contacting substance or substances” [1]. Examination of worn machine elements indicates that the wear process is rather complex and can occur by various mechanisms [2].
openaire   +1 more source

Prediction of rail profile evolution on metro curved tracks: wear model and validation

International Journal of Rail Transportation, 2023
Gongquan Tao, Dexiang Ren, Zefeng Wen
exaly  

Wear energy density for wear prediction of displaceable spline couplings

Tribologie und Schmierungstechnik
This study presents a comprehensive predictive methodology combining the Archard wear model, Kragelski’s molecular-mechanical fatigue theory, and Fleischer’s energetic wear framework to accurately forecast linear wear depth in displaceable spline couplings.
openaire   +1 more source