Indirect porosity detection and root-cause identification in WAAM
Author:
Funder
Onderzoeksraad, KU Leuven
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Industrial and Manufacturing Engineering,Software
Link
https://link.springer.com/content/pdf/10.1007/s10845-023-02128-x.pdf
Reference31 articles.
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3. Chen, K., Pashami, S., Fan, Y., & Nowaczyk, S. (2019). Predicting air compressor failures using long short term memory networks. In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11804 LNAI (pp. 596–609). https://doi.org/10.1007/978-3-030-30241-250
4. Cho, H. W., Shin, S. J., Seo, G. J., Kim, D. B., & Lee, D. H. (2022). Realtime anomaly detection using convolutional neural network in wire arc additive manufacturing: Molybdenum material. Journal of Materials Processing Technology, 302, 117495. https://doi.org/10.1016/j.jmatprotec.2022.117495
5. Derekar, K. S. (2018). Materials science and technology a review of wire arc additive manufacturing and advances in wire arc additive manufacturing of aluminium a review of wire arc additive manufacturing and advances in wire arc additive manufacturing of aluminium. Materials Science and Technology, 34, 895–916. https://doi.org/10.1080/02670836.2018.1455012
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