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Title: Tribological behavior prediction of friction materials for ultrasonic motors using Monte Carlo-based artificial neural network

Authors:
 [1];  [1];  [1];  [2]; ORCiD logo [3];  [3];  [3]
  1. State Key Laboratory of Solid Lubrication, Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences, Lanzhou 730000 China, University of Chinese Academy of Sciences, Beijing 100049 China
  2. School of Information Science and Engineering, Lanzhou University, Lanzhou 730000 China
  3. State Key Laboratory of Solid Lubrication, Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences, Lanzhou 730000 China
Publication Date:
Sponsoring Org.:
USDOE Office of Electricity Delivery and Energy Reliability (OE), Power Systems Engineering Research and Development (R&D) (OE-10)
OSTI Identifier:
1479560
Grant/Contract Number:  
QYZDJ-SSW-SLH056; 2016YFF0101000
Resource Type:
Journal Article: Publisher's Accepted Manuscript
Journal Name:
Journal of Applied Polymer Science
Additional Journal Information:
Journal Name: Journal of Applied Polymer Science Journal Volume: 136 Journal Issue: 10; Journal ID: ISSN 0021-8995
Publisher:
Wiley Blackwell (John Wiley & Sons)
Country of Publication:
United States
Language:
English

Citation Formats

Li, Song, Shao, Mingchao, Duan, Chunjian, Yan, Yingnan, Wang, Qihua, Wang, Tingmei, and Zhang, Xinrui. Tribological behavior prediction of friction materials for ultrasonic motors using Monte Carlo-based artificial neural network. United States: N. p., 2018. Web. doi:10.1002/app.47157.
Li, Song, Shao, Mingchao, Duan, Chunjian, Yan, Yingnan, Wang, Qihua, Wang, Tingmei, & Zhang, Xinrui. Tribological behavior prediction of friction materials for ultrasonic motors using Monte Carlo-based artificial neural network. United States. doi:10.1002/app.47157.
Li, Song, Shao, Mingchao, Duan, Chunjian, Yan, Yingnan, Wang, Qihua, Wang, Tingmei, and Zhang, Xinrui. Tue . "Tribological behavior prediction of friction materials for ultrasonic motors using Monte Carlo-based artificial neural network". United States. doi:10.1002/app.47157.
@article{osti_1479560,
title = {Tribological behavior prediction of friction materials for ultrasonic motors using Monte Carlo-based artificial neural network},
author = {Li, Song and Shao, Mingchao and Duan, Chunjian and Yan, Yingnan and Wang, Qihua and Wang, Tingmei and Zhang, Xinrui},
abstractNote = {},
doi = {10.1002/app.47157},
journal = {Journal of Applied Polymer Science},
issn = {0021-8995},
number = 10,
volume = 136,
place = {United States},
year = {2018},
month = {10}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on October 16, 2019
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