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Sensors 2018, 18(1), 288; https://doi.org/10.3390/s18010288

Machine Learning and Infrared Thermography for Fiber Orientation Assessment on Randomly-Oriented Strands Parts

1
School of Computer Science, Federal University of Uberlandia, Uberlandia 38408-100, Brazil
2
Department of Electrical and Computer Engineering, Computer Vision and Systems Laboratory (CVSL), Laval University, Quebec City, QC G1V 0A6, Canada
3
Department of Mechanical Engineering, Laboratory of Teaching and Researching on Heat Transfer, Federal University of Uberlandia, Uberlandia 38408-100, Brazil
4
Department of Industrial and Information Engineering and Economics, University of L’Aquila, Roio Poggio, L’Aquila (AQ) 67100, Italy
*
Author to whom correspondence should be addressed.
Received: 26 October 2017 / Revised: 20 December 2017 / Accepted: 20 December 2017 / Published: 19 January 2018
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Abstract

The use of fiber reinforced materials such as randomly-oriented strands has grown in recent years, especially for manufacturing of aerospace composite structures. This growth is mainly due to their advantageous properties: they are lighter and more resistant to corrosion when compared to metals and are more easily shaped than continuous fiber composites. The resistance and stiffness of these materials are directly related to their fiber orientation. Thus, efficient approaches to assess their fiber orientation are in demand. In this paper, a non-destructive evaluation method is applied to assess the fiber orientation on laminates reinforced with randomly-oriented strands. More specifically, a method called pulsed thermal ellipsometry combined with an artificial neural network, a machine learning technique, is used in order to estimate the fiber orientation on the surface of inspected parts. Results showed that the method can be potentially used to inspect large areas with good accuracy and speed. View Full-Text
Keywords: infrared thermography; flying laser spot; fiber orientation; randomly-oriented strands infrared thermography; flying laser spot; fiber orientation; randomly-oriented strands
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Fernandes, H.; Zhang, H.; Figueiredo, A.; Malheiros, F.; Ignacio, L.H.; Sfarra, S.; Ibarra-Castanedo, C.; Guimaraes, G.; Maldague, X. Machine Learning and Infrared Thermography for Fiber Orientation Assessment on Randomly-Oriented Strands Parts. Sensors 2018, 18, 288.

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