Results 231 to 240 of about 25,726 (266)

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

Prediction of Fabric Wear

Textile Research Journal, 1971
G. Alon, L.I. Weiner
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

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

Physics-informed meta learning for machining tool wear prediction

Journal of Manufacturing Systems, 2022
Yilin Li, Jinjiang Wang, Robert X Gao
exaly  

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

Mechanical Systems and Signal Processing, 2023
Ke Feng, Jinchen Ji, Qing Ni
exaly  

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

Journal of Manufacturing Systems, 2023
Yiyuan Qin, Caixu Yue, Xudong Wei
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

Intelligent tool wear monitoring and multi-step prediction based on deep learning model

Journal of Manufacturing Systems, 2022
Minghui Cheng, Pei Yan, Tianyang Qiu
exaly  

Physics guided neural network for machining tool wear prediction

Journal of Manufacturing Systems, 2020
Jinjiang Wang, Yilin Li, Robert X Gao
exaly