Predicting porosity in wire arc additive manufacturing (WAAM) using wavelet scattering networks and sparse principal component analysis
Author:
Funder
Onderzoeksraad, KU Leuven
Flanders Make
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s40194-024-01709-5.pdf
Reference28 articles.
1. Chaturvedi M, Scutelnicu E, Rusu CC, Mistodie LR, Mihailescu D, Subbiah AV (2021) Wire arc additive manufacturing: review on recent findings and challenges in industrial applications and materials characterization. Metals 11:939. https://doi.org/10.3390/met11060939
2. Chabot A, Rauch M, Hascoët J-Y (2021) Novel control model of Contact-Tip-to-Work Distance (CTWD) for sound monitoring of arc-based DED processes based on spectral analysis. Int J Adv Manuf Technol 116:3463–3472. https://doi.org/10.1007/s00170-021-07621-2
3. Hauser T, Reisch RT, Kamps T, Kaplan AFH, Volpp J (2022) Acoustic emissions in directed energy deposition processes. Int J Adv Manuf Technol 119:3517–3532. https://doi.org/10.1007/s00170-021-08598-8
4. Tang F, Luo Y, Cai Y, Yang S, Zhang F, Peng Y (2022) Arc length identification based on arc acoustic signals in GTA-WAAM process. Int J Adv Manuf Technol 118:1553–1563. https://doi.org/10.1007/s00170-021-08044-9
5. Rohe M, Stoll BN, Hildebrand J, Reimann J, Bergmann JP (2021) Detecting process anomalies in the GMAW process by acoustic sensing with a convolutional neural network (CNN) for classification. J Manuf Mater Process 5:135. https://doi.org/10.3390/jmmp5040135
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