Development of a machine learning model for deviation from trajectory detection in multi-pass TIG welding in a narrow gap
Author:
Publisher
Springer Science and Business Media LLC
Subject
Metals and Alloys,Mechanical Engineering,Mechanics of Materials
Link
https://link.springer.com/content/pdf/10.1007/s40194-023-01626-z.pdf
Reference15 articles.
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3. Sumesh A, Rameshkumar K, Mohandas K, Babu RS (2015) Use of machine learning algorithms for weld quality monitoring using acoustic signature. Procedia Computer Science 50:316–322. https://doi.org/10.1016/j.procs.2015.04.042
4. Sumesh A, Thekkuden DT, Nair BB, Rameshkumar K, Mohandas K (2015) Acoustic signature based weld quality monitoring for SMAW process using data mining algorithms. Appl Mech Mater 813–814:1104–1113. https://doi.org/10.4028/www.scientific.net/amm.813-814.1104
5. Mirapeix J, Cobo A, Conde OM, Jaúregui C, López-Higuera JM (2006) Real-time arc welding defect detection technique by means of plasma spectrum optical analysis. NDT and E Int 39(5):356–360. https://doi.org/10.1016/j.ndteint.2005.10.004
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