Applications of deep learning for fault detection in industrial cold forging-Reference-Cited by-同舟云学术

Applications of deep learning for fault detection in industrial cold forging

Published:2021-03-09 Issue:16 Volume:59 Page:4826-4835
ISSN:0020-7543
Container-title:International Journal of Production Research
language:en
Short-container-title:International Journal of Production Research

Author:

Glaeser Andrew¹,Selvaraj Vignesh¹^ORCID,Lee Sooyoung¹²,Hwang Yunseob¹²,Lee Kangsan¹²,Lee Namjeong¹²,Lee Seungchul²^ORCID,Min Sangkee¹^ORCID

Affiliation:

1. University of Wisconsin-Madison, Madison, WI, USA

2. Pohang University of Science and Technology, Pohang-si, Gyeongsangbuk-do, South Korea

Funder

Institute for Information and Communication Technology Planning and Evaluation

Publisher

Informa UK Limited

Subject

Industrial and Manufacturing Engineering,Management Science and Operations Research,Strategy and Management

Link

https://www.tandfonline.com/doi/pdf/10.1080/00207543.2021.1891318

Reference37 articles.

1. Ansari, Ali Imran, Santosh J Chauhan, and Pallavi Khaire. 2017. “Effect of crack on natural frequency in rotor system.” In AIP Conference Proceedings, Vol. 1859, 020101. AIP Publishing LLC., Andhra Pradesh, India.

2. On tool stresses in cold heading of fasteners

3. Acoustic emission technique for online monitoring during cold forging of steel components: a promising approach for online crack detection in metal forming processes

4. A Review of Feature Extraction Methods in Vibration-Based Condition Monitoring and Its Application for Degradation Trend Estimation of Low-Speed Slew Bearing

5. Chien, Chen-Fu, Stéphane Dauzère-Pérès, Woonghee Tim Huh, Young Jae Jang, and James R Morrison. 2020. “Artificial intelligence in manufacturing and logistics systems: algorithms, applications, and case studies.”.

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