On the Potential of Machine Learning Assisted Tomography for Rapid Assessment of FRP Materials with Defects
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-45554-4_5
Reference19 articles.
1. Hörrmann S, Adumitroaie A, Viechtbauer C, Schagerl M (2016) The effect of fiber waviness on the fatigue life of cfrp materials. International Journal of Fatigue 90:139–147
2. Altmann A, Gesell P, Drechsler K (2015) Strength prediction of ply waviness in composite materials considering matrix dominated effects. Composite Structures 127:51–59
3. Puck A, Schürmann H (1998) Failure analysis of FRP laminates by means of physically based phenomenological models. Composites Science and Technology 58(7):1045–1067
4. Puck A, Schürmann H (2002) Failure analysis of FRP laminates by means of physically based phenomenological models. Composites Science and Technology 62(12):1633–1662
5. Lemanski SL, Wang J, Sutcliffe MPF, Potter KD, Wisnom MR (2013) Modelling failure of composite specimens with defects under compression loading. Composites Part A: Applied Science and Manufacturing 48:26–36
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