Tool wear prediction based on Canonical Correlation Analysis and Neural Network fitting method

Liandie Zhu; Wei Dai; Xiaoliang Wang

doi:10.1109/PHM-Shanghai49105.2020.9280931

Tool wear prediction based on Canonical Correlation Analysis and Neural Network fitting method

Liandie Zhu
, Wei Dai
, Xiaoliang Wang

可靠性与系统工程学院

Beihang University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

1 引用（Scopus）

摘要

Tool wear is an essential factor affecting the machining process of a machine tool. In order to improve the use efficiency of machinery and accurately predict the wear of the equipment in use, a method based on Canonical correlation analysis and Neural Network fitting is proposed in this paper. Using the time domain signal to extract multidimensional features, then build a initial feature set. Combined with Canonical correlation analysis, constructed the training feature set, finally, build a prediction model. Results show that the proposed method is able to predict tool wear with high accuracy.

源语言	英语
主期刊名	2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020
编辑	Wei Guo, Steven Li, Qiang Miao
出版商	Institute of Electrical and Electronics Engineers Inc.
ISBN（电子版）	9781728159454
DOI	https://doi.org/10.1109/PHM-Shanghai49105.2020.9280931
出版状态	已出版 - 16 10月 2020
活动	2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020 - Shanghai, 中国期限: 16 10月 2020 → 18 10月 2020

出版系列

姓名	2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020

会议

会议	2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020
国家/地区	中国
市	Shanghai
时期	16/10/20 → 18/10/20

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10.1109/PHM-Shanghai49105.2020.9280931

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引用此

Zhu, L., Dai, W., & Wang, X. (2020). Tool wear prediction based on Canonical Correlation Analysis and Neural Network fitting method. 在 W. Guo, S. Li, & Q. Miao (编辑), 2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020 文章 9280931 (2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PHM-Shanghai49105.2020.9280931

Zhu, Liandie ; Dai, Wei ; Wang, Xiaoliang. / Tool wear prediction based on Canonical Correlation Analysis and Neural Network fitting method. 2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020. 编辑 / Wei Guo ; Steven Li ; Qiang Miao. Institute of Electrical and Electronics Engineers Inc., 2020. (2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020).

@inproceedings{75d3b9c8bda14abd917b655e6f6ac817,

title = "Tool wear prediction based on Canonical Correlation Analysis and Neural Network fitting method",

abstract = "Tool wear is an essential factor affecting the machining process of a machine tool. In order to improve the use efficiency of machinery and accurately predict the wear of the equipment in use, a method based on Canonical correlation analysis and Neural Network fitting is proposed in this paper. Using the time domain signal to extract multidimensional features, then build a initial feature set. Combined with Canonical correlation analysis, constructed the training feature set, finally, build a prediction model. Results show that the proposed method is able to predict tool wear with high accuracy.",

keywords = "Canonical correlation analysis, component, Neural network fitting, Tool wear prediction",

author = "Liandie Zhu and Wei Dai and Xiaoliang Wang",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020 ; Conference date: 16-10-2020 Through 18-10-2020",

year = "2020",

month = oct,

day = "16",

doi = "10.1109/PHM-Shanghai49105.2020.9280931",

language = "英语",

series = "2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

editor = "Wei Guo and Steven Li and Qiang Miao",

booktitle = "2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020",

address = "美国",

}

Zhu, L, Dai, W & Wang, X 2020, Tool wear prediction based on Canonical Correlation Analysis and Neural Network fitting method. 在 W Guo, S Li & Q Miao (编辑), 2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020., 9280931, 2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020, Institute of Electrical and Electronics Engineers Inc., 2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020, Shanghai, 中国, 16/10/20. https://doi.org/10.1109/PHM-Shanghai49105.2020.9280931

Tool wear prediction based on Canonical Correlation Analysis and Neural Network fitting method. / Zhu, Liandie; Dai, Wei; Wang, Xiaoliang.
2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020. 编辑 / Wei Guo; Steven Li; Qiang Miao. Institute of Electrical and Electronics Engineers Inc., 2020. 9280931 (2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Tool wear prediction based on Canonical Correlation Analysis and Neural Network fitting method

AU - Zhu, Liandie

AU - Dai, Wei

AU - Wang, Xiaoliang

PY - 2020/10/16

Y1 - 2020/10/16

N2 - Tool wear is an essential factor affecting the machining process of a machine tool. In order to improve the use efficiency of machinery and accurately predict the wear of the equipment in use, a method based on Canonical correlation analysis and Neural Network fitting is proposed in this paper. Using the time domain signal to extract multidimensional features, then build a initial feature set. Combined with Canonical correlation analysis, constructed the training feature set, finally, build a prediction model. Results show that the proposed method is able to predict tool wear with high accuracy.

AB - Tool wear is an essential factor affecting the machining process of a machine tool. In order to improve the use efficiency of machinery and accurately predict the wear of the equipment in use, a method based on Canonical correlation analysis and Neural Network fitting is proposed in this paper. Using the time domain signal to extract multidimensional features, then build a initial feature set. Combined with Canonical correlation analysis, constructed the training feature set, finally, build a prediction model. Results show that the proposed method is able to predict tool wear with high accuracy.

KW - Canonical correlation analysis

KW - component

KW - Neural network fitting

KW - Tool wear prediction

UR - https://www.scopus.com/pages/publications/85099707612

U2 - 10.1109/PHM-Shanghai49105.2020.9280931

DO - 10.1109/PHM-Shanghai49105.2020.9280931

M3 - 会议稿件

AN - SCOPUS:85099707612

T3 - 2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020

BT - 2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020

A2 - Guo, Wei

A2 - Li, Steven

A2 - Miao, Qiang

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020

Y2 - 16 October 2020 through 18 October 2020

ER -

Zhu L, Dai W, Wang X. Tool wear prediction based on Canonical Correlation Analysis and Neural Network fitting method. 在 Guo W, Li S, Miao Q, 编辑, 2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9280931. (2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020). doi: 10.1109/PHM-Shanghai49105.2020.9280931

Tool wear prediction based on Canonical Correlation Analysis and Neural Network fitting method

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