Applications of deep learning for fault detection in industrial cold forging
Author:
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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