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Proceedings Paper

Estimating non-metallic coating thickness using artificial neural network modeled time-resolved thermography: capacity and constraints
Author(s): Hongjin Wang; Sheng-Jen Hsieh
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Paper Abstract

Current studies suggest that thermography-based measurements may provide a feasible solution for measuring the thickness of non-metallic coatings. The focus of this research was to build an artificial neural network model to predict coating thickness using active thermography and thickness samples that have not previously been seen by the model. Best results (7.5% error) were achieved when using an ANN model with the derivative of a temperature increment’s real part Laplace transform over the real axis as the input, the gradient descent with momentum back-propagation training algorithm, and 20 hidden nodes.

Paper Details

Date Published: 21 May 2014
PDF: 9 pages
Proc. SPIE 9105, Thermosense: Thermal Infrared Applications XXXVI, 91050K (21 May 2014); doi: 10.1117/12.2049903
Show Author Affiliations
Hongjin Wang, Texas A&M Univ. (United States)
Sheng-Jen Hsieh, Texas A&M Univ. (United States)


Published in SPIE Proceedings Vol. 9105:
Thermosense: Thermal Infrared Applications XXXVI
Gregory R. Stockton; Fred P. Colbert; Sheng-Jen (Tony) Hsieh, Editor(s)

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