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Article

Deep Quality Assessment of a Solar Reflector Based on Synthetic Data: Detecting Surficial Defects from Manufacturing and Use Phase

Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, 265 04 Patras, Greece
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Sensors 2020, 20(19), 5481; https://doi.org/10.3390/s20195481
Received: 31 August 2020 / Revised: 14 September 2020 / Accepted: 22 September 2020 / Published: 24 September 2020
Vision technologies are used in both industrial and smart city applications in order to provide advanced value products due to embedded self-monitoring and assessment services. In addition, for the full utilization of the obtained data, deep learning is now suggested for use. To this end, the current work presents the implementation of image recognition techniques alongside the original the quality assessment of a Parabolic Trough Collector (PTC) reflector surface to locate and identify surface irregularities by classifying images as either acceptable or non-acceptable. The method consists of a three-step solution that promotes an affordable implementation in a relatively small time period. More specifically, a 3D Computer Aided Design (CAD) of the PTC was used for the pre-training of neural networks, while an aluminum reflector surface was used to verify algorithm performance. The results are promising, as this method proved applicable in cases where the actual part was manufactured in small batches or under the concept of customized manufacturing. Consequently, the algorithm is capable of being trained with a limited number of data. View Full-Text
Keywords: defect detection; vision techniques; image recognition; neural networks; parabolic reflector; surface monitoring defect detection; vision techniques; image recognition; neural networks; parabolic reflector; surface monitoring
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MDPI and ACS Style

Papacharalampopoulos, A.; Tzimanis, K.; Sabatakakis, K.; Stavropoulos, P. Deep Quality Assessment of a Solar Reflector Based on Synthetic Data: Detecting Surficial Defects from Manufacturing and Use Phase. Sensors 2020, 20, 5481. https://doi.org/10.3390/s20195481

AMA Style

Papacharalampopoulos A, Tzimanis K, Sabatakakis K, Stavropoulos P. Deep Quality Assessment of a Solar Reflector Based on Synthetic Data: Detecting Surficial Defects from Manufacturing and Use Phase. Sensors. 2020; 20(19):5481. https://doi.org/10.3390/s20195481

Chicago/Turabian Style

Papacharalampopoulos, Alexios, Konstantinos Tzimanis, Kyriakos Sabatakakis, and Panagiotis Stavropoulos. 2020. "Deep Quality Assessment of a Solar Reflector Based on Synthetic Data: Detecting Surficial Defects from Manufacturing and Use Phase" Sensors 20, no. 19: 5481. https://doi.org/10.3390/s20195481

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