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Article

Detection and Identification of Defects in 3D-Printed Dielectric Structures via Thermographic Inspection and Deep Neural Networks

1
Center for Electromagnetic Fields Engineering and High-Frequency Techniques, Faculty of Electrical Engineering, West Pomeranian University of Technology, 70-310 Szczecin, Poland
2
Department of Electrical Engineering, Iran University of Science and Technology, Teheran 13114-16846, Iran
*
Author to whom correspondence should be addressed.
Academic Editor: Georgios C. Psarras
Materials 2021, 14(15), 4168; https://doi.org/10.3390/ma14154168
Received: 24 May 2021 / Revised: 8 July 2021 / Accepted: 17 July 2021 / Published: 27 July 2021
In this paper, we propose a new method based on active infrared thermography (IRT) applied to assess the state of 3D-printed structures. The technique utilized here—active IRT—assumes the use of an external energy source to heat the tested material and to create a temperature difference between undamaged and defective areas, and this temperature difference is possible to observe with a thermal imaging camera. In the case of materials with a low value of thermal conductivity, such as the acrylonitrile butadiene styrene (ABS) plastic printout tested in the presented work, the obtained temperature differences are hardly measurable. Hence, the proposed novel IRT method is complemented by a dedicated algorithm for signal analysis and a multi-label classifier based on a deep convolutional neural network (DCNN). For the initial testing of the presented methodology, a 3D printout made in the shape of a cuboid was prepared. One type of defect was tested—surface breaking holes of various depths and diameters that were produced artificially by inclusion in the printout. As a result of examining the sample via the IRT method, a sequence of thermograms was obtained, which enabled the examination of the temporal representation of temperature variation over the examined region of the material. First, the obtained signals were analysed using a new algorithm to enhance the contrast between the background and the defect areas in the 3D print. In the second step, the DCNN was utilised to identify the chosen defect parameters. The experimental results show the high effectiveness of the proposed hybrid signal analysis method to visualise the inner structure of the sample and to determine the defect and size, including the depth and diameter. View Full-Text
Keywords: active thermography; deep learning; convolutional neural networks; 3D-Printed structure quality active thermography; deep learning; convolutional neural networks; 3D-Printed structure quality
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MDPI and ACS Style

Szymanik, B.; Psuj, G.; Hashemi, M.; Lopato, P. Detection and Identification of Defects in 3D-Printed Dielectric Structures via Thermographic Inspection and Deep Neural Networks. Materials 2021, 14, 4168. https://doi.org/10.3390/ma14154168

AMA Style

Szymanik B, Psuj G, Hashemi M, Lopato P. Detection and Identification of Defects in 3D-Printed Dielectric Structures via Thermographic Inspection and Deep Neural Networks. Materials. 2021; 14(15):4168. https://doi.org/10.3390/ma14154168

Chicago/Turabian Style

Szymanik, Barbara, Grzegorz Psuj, Maryam Hashemi, and Przemyslaw Lopato. 2021. "Detection and Identification of Defects in 3D-Printed Dielectric Structures via Thermographic Inspection and Deep Neural Networks" Materials 14, no. 15: 4168. https://doi.org/10.3390/ma14154168

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