LSTM-based welding quality forecasting system in smart manufacturing-Reference-Cited by-同舟云学术

LSTM-based welding quality forecasting system in smart manufacturing

Published:2023-11-17 Issue: Volume:43 Page:54-60
ISSN:
Container-title:Proceedings of the 2023 9th International Conference on Robotics and Artificial Intelligence
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Author:

Vo Quoc-Trinh¹^ORCID,Tran Dat Van²^ORCID,Nguyen Rin Vy³^ORCID,Le Ngoc Anh⁴^ORCID,Truong Cong Doan⁵^ORCID,Tran Tin Trung⁶^ORCID

Affiliation:

1. Department of Artificial Intelligence, FPT University, Vietnam

2. Institute for Research & Executive Education (VNUK), The University of Da Nang, Vietnam

3. Department of Software Engineering, FPT University, Vietnam

4. Innvation Space - Swinburne Vietnam, FPT University, Vietnam

5. International School, Vietnam National University, Vietnam

6. Department of Information Technology - Swinburne Vietnam, FPT University, Vietnam

Funder

FPT University

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637843.3637852

Reference13 articles.

1. Real-time prediction of weld quality using LSTM neural networks based on multi-sensor data fusion;Tao Zhang;Journal of Intelligent Manufacturing,2019

2. Weld-Quality Prediction Algorithm Based on Multiple Models Using Process Signals in Resistance Spot Welding

3. Machine learning with domain knowledge for predictive quality monitoring in resistance spot welding

4. Zhang, Y., Chen, G., & Lin, Z. 2004. Study on weld quality control of resistance spot welding using a neuro-fuzzy algorithm. Paper presented at the Knowledge-Based Intelligent Information and Engineering Systems: 8th International Conference, KES 2004, Wellington, New Zealand, September 20-25, Proceedings, Part III 8.

5. Yulia Svetashova Baifan Zhou Tim Pychynski Stefan Schmidt York Sure-Vetter Ralf Mikut and Evgeny Kharlamov.2020. Ontology-Enhanced machine learning: A Bosch use case of welding quality monitoring. In proceeding of 19th International Semantic Web Conference (ISWC). Athens Greece November 2-6. 531-550. https://doi.org/10.1007/978-3-030-62466-8_33