Equipment and Machine Learning in Welding Monitoring-Reference-Cited by-同舟云学术

Equipment and Machine Learning in Welding Monitoring

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 5th International Conference on Mechatronics and Robotics Engineering - ICMRE'19
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Author:

Cai Wang¹,Wang Jianzhuang¹,Zhou Qi¹,Yang Yang²,Jiang Ping¹

Affiliation:

1. Huazhong University of, Science & Technology, Wuhan, P.R. China

2. Huazhong Agricultural University, Wuhan, P.R. China

Publisher

ACM Press

Reference73 articles.

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2. PritschowHaug K, P.G. 1998. Robust Laser-stripe Sensor for Automated Weld-seam-tracking in the Shipbuilding Industry. Proceedings of the 24th Annual Conference of the IEEE. 1236--1241.

3. Abderrazak, K., W. Ben Salem, H. Mhiri, P. Bournot and M. Autric. 2009. Nd:YAG Laser Welding of AZ91 Magnesium Alloy for Aerospace Industries. Metallurgical and Materials Transactions B. 54--61. DOI= 10.1007/s11663-008-9218-7.

4. Harman, G. and J. Albers. 1977. The ultrasonic welding mechanism as applied to aluminum-and gold-wire bonding in microelectronics. IEEE Transactions on parts, hybrids, and packaging. 406--412.

5. Stavridis, J., A. Papacharalampopoulos and P. Stavropoulos. 2017. Quality assessment in laser welding: a critical review. The International Journal of Advanced Manufacturing Technology. 1825--1847. DOI= 10.1007/s00170-017-0461-4.

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