Deep Quality Assessment of a Solar Reflector Based on Synthetic Data: Detecting Surficial Defects from Manufacturing and Use Phase
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 curren...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/19/5481 |
| _version_ | 1797552722151473152 |
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| author | Alexios Papacharalampopoulos Konstantinos Tzimanis Kyriakos Sabatakakis Panagiotis Stavropoulos |
| author_facet | Alexios Papacharalampopoulos Konstantinos Tzimanis Kyriakos Sabatakakis Panagiotis Stavropoulos |
| author_sort | Alexios Papacharalampopoulos |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T16:05:09Z |
| format | Article |
| id | doaj.art-df49ba7912614747bfe180ec38b07830 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T16:05:09Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-df49ba7912614747bfe180ec38b078302023-11-20T15:00:29ZengMDPI AGSensors1424-82202020-09-012019548110.3390/s20195481Deep Quality Assessment of a Solar Reflector Based on Synthetic Data: Detecting Surficial Defects from Manufacturing and Use PhaseAlexios Papacharalampopoulos0Konstantinos Tzimanis1Kyriakos Sabatakakis2Panagiotis Stavropoulos3Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, 265 04 Patras, GreeceLaboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, 265 04 Patras, GreeceLaboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, 265 04 Patras, GreeceLaboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, 265 04 Patras, GreeceVision 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.https://www.mdpi.com/1424-8220/20/19/5481defect detectionvision techniquesimage recognitionneural networksparabolic reflectorsurface monitoring |
| spellingShingle | Alexios Papacharalampopoulos Konstantinos Tzimanis Kyriakos Sabatakakis Panagiotis Stavropoulos Deep Quality Assessment of a Solar Reflector Based on Synthetic Data: Detecting Surficial Defects from Manufacturing and Use Phase Sensors defect detection vision techniques image recognition neural networks parabolic reflector surface monitoring |
| title | Deep Quality Assessment of a Solar Reflector Based on Synthetic Data: Detecting Surficial Defects from Manufacturing and Use Phase |
| title_full | Deep Quality Assessment of a Solar Reflector Based on Synthetic Data: Detecting Surficial Defects from Manufacturing and Use Phase |
| title_fullStr | Deep Quality Assessment of a Solar Reflector Based on Synthetic Data: Detecting Surficial Defects from Manufacturing and Use Phase |
| title_full_unstemmed | Deep Quality Assessment of a Solar Reflector Based on Synthetic Data: Detecting Surficial Defects from Manufacturing and Use Phase |
| title_short | Deep Quality Assessment of a Solar Reflector Based on Synthetic Data: Detecting Surficial Defects from Manufacturing and Use Phase |
| title_sort | deep quality assessment of a solar reflector based on synthetic data detecting surficial defects from manufacturing and use phase |
| topic | defect detection vision techniques image recognition neural networks parabolic reflector surface monitoring |
| url | https://www.mdpi.com/1424-8220/20/19/5481 |
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