An Autonomous Technique for Multi Class Weld Imperfections Detection and Classification by Support Vector Machine
Author:
Publisher
Springer Science and Business Media LLC
Subject
Mechanical Engineering,Mechanics of Materials
Link
https://link.springer.com/content/pdf/10.1007/s10921-021-00801-w.pdf
Reference28 articles.
1. Malarvel, M., Singh, H.: An autonomous technique for weld defects detection and classification using multi-class support vector machine in X-ray. Optik (2021). https://doi.org/10.1016/j.ijleo.2021.166342
2. Yang, L., Jiang, H.: Weld defect classification in X-ray images using unified deep neural network with multi-level features. J. Intell. Manuf. (2021). https://doi.org/10.1007/s10845-020-01581-2
3. Patil, R., Reddy, Y.: Multi-class weld defect detection and classification by support vector machine and artificial neural network. Smart Innov. Sys. Tech. (2021). https://doi.org/10.1007/978-981-15-9829-6_33
4. Kumar, G.S., Natarajan, U., Ananthan, S.: Vision inspection system for the identification and classification of defects in MIG welding joints. Int J. Adv. Manu. Tech. (2014). https://doi.org/10.1007/s00170-011-3770-z
5. Patil, R., Reddy, Y.: Weld imperfection classification by texture features extraction and local binary pattern. Smart Innov. Sys. Tech. (2021). https://doi.org/10.1007/978-981-15-9829-6_28
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