SAMPE Journal - May/June 2020 - 41

An exercise in operating the software in a live
production environment for an extended period of
time may yield useful information about the performance and improvement of the system with
time. Thus, while these results are still squarely
under the regime of preliminary, it is expected that
a full understanding of the system capabilities will
follow in the near future.
Additional area of potential work includes the
integration of our system with steered tow designs. Preliminary investigation indicates that fiber
steering produces a number of defects including
wrinkles17. Inspection can also be utilized as an
experimental check on any number of path planning operations that may contribute to the defect
production process18.
ACKNOWLEDGMENTS
The material is based upon work supported
by NASA under Award Nos. NNL09AA00A and
80LARC17C0004.
Any opinions, findings, and conclusions or recommendations expressed in this material are those
of the author(s) and do not necessarily reflect the
views of the National Aeronautics and Space Administration.
REFERENCES
1. R. Harik, C. Saidy, S. J. Williams, Z. Gurdal, and B. Grimsley, "Automated fiber placement defect identity cards:
cause, anticipation, existence, significance, and progression," in SAMPE Conference Proceedings, 2018.
2. L. G. Bahamonde Jácome, M. A. Albazzan, P. L. Chevalier, Z. Gürdal, B. F. Tatting, and R. Harik, "Rapid tools for

8. C. Cortes and V. Vapnik, "Support-Vector Networks,"
Mach. Learn., vol. 20, no. 3, pp. 273-297, 1995.
9. J. Brüning, B. Denkena, M. A. Dittrich, and T. Hocke,
"Machine Learning Approach for Optimization of Automated Fiber Placement Processes," Procedia CIRP, vol.
66, pp. 74-78, 2017.
10. M. Cacciola, S. Calcagno, F. C. Morabito, and M. Versaci, "Computational intelligence aspects for defect classification in aeronautic composites by using ultrasonic
pulses," IEEE Trans. Ultrason. Ferroelectr. Freq. Control,
vol. 55, no. 4, pp. 870-878, 2008.
11. G. D'Angelo and S. Rampone, "Feature extraction and
soft computing methods for aerospace structure defect
classification," Meas. J. Int. Meas. Confed., vol. 85, pp.
192-209, 2016.
12. C. Sacco, A. B. Radwan, R. Harik, and M. Van Tooren,
"Automated Fiber Placement Defects: Automated Inspection and Characterization," SAMPE 2018 Proceeding,
13. J. Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation," Proc. IEEE
Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol.
07-12-June, pp. 3431-3440, 2015.
14. X. Glorot and Y. Bengio, "Understanding the difficulty
of training deep feedforward neural networks," Pmlr, vol.
9, pp. 249-256, 2010.
15. B. Krawczyk, "GPU-accelerated extreme learning machines for imbalanced data streams with Concept Drift,"
Procedia Comput. Sci., vol. 80, pp. 1692-1701, 2016.
16. M. A. Franco and J. Bacardit, "Large-scale experimental evaluation of GPU strategies for evolutionary machine
learning," Inf. Sci. (Ny)., vol. 330, pp. 385-402, 2016.

an AFP manufacturing defects assessment framework,"

17. R. Wehbe, R. Harik, and Z. Gürdal, "In-Plane Tow Defor-

Int. SAMPE Tech. Conf., May, 2018.

mations Due to Steering in Automated Fiber Placement,"

3. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet

in AIAA Scitech 2019 Forum, pp. 1-13.

Classification with Deep Convolutional Neural Networks,"

18. G. Rousseau, R. Wehbe, J. Halbritter, and R. Harik, "Au-

Adv. Neural Inf. Process. Syst., pp. 1-9, 2012.

tomated fiber placement path planning: A state-of-the-art

4. S. Wu, S. Zhong, and Y. Liu, "Deep residual learning for

review," Computer-Aided Design and Applications. 2019.

image steganalysis," Multimed. Tools Appl., pp. 1-17, 2017.
5. M. Meng, Y. J. Chua, E. Wouterson, and C. P. K. Ong,
"Ultrasonic signal classification and imaging system for
composite materials via deep convolutional neural networks," Neurocomputing, vol. 257, pp. 128-135, 2017.
6. M. Kuhl, T. Wiener, and M. Krauß, "Multisensorial
self-learning systems for quality monitoring of carbon
fiber composites in aircraft production," Procedia CIRP,
vol. 12, pp. 103-108, 2013.
7. H. D. Benítez, H. Loaiza, E. Caicedo, C. Ibarra-Castane-

Machining & Testing
of Advanced
Composite Materials

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* Mechanical * Physical * Thermal
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Email: info@metcutctl.com
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do, A. H. Bendada, and X. Maldague, "Defect characterization in infrared non-destructive testing with learning
machines," NDT E Int., vol. 42, no. 7, pp. 630-643, 2009.

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