A Multi-Feature Automatic Evaluation of the Aesthetics of 3D Printed Surfaces
Additive manufacturing is one of the continuously developing areas of technology that still requires reliable monitoring and quality assessment of obtained products. Considering the relatively long time necessary for manufacturing larger products, one of the most desired solutions is video quality m...
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MDPI AG
2025-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/9/4852 |
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| author | Jarosław Fastowicz Mateusz Tecław Krzysztof Okarma |
| author_facet | Jarosław Fastowicz Mateusz Tecław Krzysztof Okarma |
| author_sort | Jarosław Fastowicz |
| collection | DOAJ |
| description | Additive manufacturing is one of the continuously developing areas of technology that still requires reliable monitoring and quality assessment of obtained products. Considering the relatively long time necessary for manufacturing larger products, one of the most desired solutions is video quality monitoring of the manufactured object’s surface. This makes it possible to stop the printing process if the quality is unacceptable. It helps to save the filament, energy, and time, preventing the production of items with poor aesthetic quality. In the paper, several approaches to image-based surface quality assessment are discussed and combined towards a high correlation with the subjective perception of typical quality degradations of the 3D printed surfaces, exceeding 0.9. Although one of the most significant limitations of using full-reference image quality-assessment metrics might be the lack of reference images, it can be overcome by using mutual similarity calculated for image regions. For the created dataset containing 107 samples with subjective aesthetic quality scores, it is shown that the combination of even two metrics using their weighted sum and product significantly outperforms any elementary metric or feature when considering correlations with subjective quality scores. |
| format | Article |
| id | doaj-art-3d0e40dd07c94d759ad1ba4e6f478a41 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-3d0e40dd07c94d759ad1ba4e6f478a412025-08-20T03:52:57ZengMDPI AGApplied Sciences2076-34172025-04-01159485210.3390/app15094852A Multi-Feature Automatic Evaluation of the Aesthetics of 3D Printed SurfacesJarosław Fastowicz0Mateusz Tecław1Krzysztof Okarma2Department of Signal Processing and Multimedia Engineering, West Pomeranian University of Technology in Szczecin, 70-313 Szczecin, PolandDepartment of Signal Processing and Multimedia Engineering, West Pomeranian University of Technology in Szczecin, 70-313 Szczecin, PolandDepartment of Signal Processing and Multimedia Engineering, West Pomeranian University of Technology in Szczecin, 70-313 Szczecin, PolandAdditive manufacturing is one of the continuously developing areas of technology that still requires reliable monitoring and quality assessment of obtained products. Considering the relatively long time necessary for manufacturing larger products, one of the most desired solutions is video quality monitoring of the manufactured object’s surface. This makes it possible to stop the printing process if the quality is unacceptable. It helps to save the filament, energy, and time, preventing the production of items with poor aesthetic quality. In the paper, several approaches to image-based surface quality assessment are discussed and combined towards a high correlation with the subjective perception of typical quality degradations of the 3D printed surfaces, exceeding 0.9. Although one of the most significant limitations of using full-reference image quality-assessment metrics might be the lack of reference images, it can be overcome by using mutual similarity calculated for image regions. For the created dataset containing 107 samples with subjective aesthetic quality scores, it is shown that the combination of even two metrics using their weighted sum and product significantly outperforms any elementary metric or feature when considering correlations with subjective quality scores.https://www.mdpi.com/2076-3417/15/9/48523D printsimage analysisimage quality assessmentmulti-metric fusioncombined metricsadditive manufacturing |
| spellingShingle | Jarosław Fastowicz Mateusz Tecław Krzysztof Okarma A Multi-Feature Automatic Evaluation of the Aesthetics of 3D Printed Surfaces Applied Sciences 3D prints image analysis image quality assessment multi-metric fusion combined metrics additive manufacturing |
| title | A Multi-Feature Automatic Evaluation of the Aesthetics of 3D Printed Surfaces |
| title_full | A Multi-Feature Automatic Evaluation of the Aesthetics of 3D Printed Surfaces |
| title_fullStr | A Multi-Feature Automatic Evaluation of the Aesthetics of 3D Printed Surfaces |
| title_full_unstemmed | A Multi-Feature Automatic Evaluation of the Aesthetics of 3D Printed Surfaces |
| title_short | A Multi-Feature Automatic Evaluation of the Aesthetics of 3D Printed Surfaces |
| title_sort | multi feature automatic evaluation of the aesthetics of 3d printed surfaces |
| topic | 3D prints image analysis image quality assessment multi-metric fusion combined metrics additive manufacturing |
| url | https://www.mdpi.com/2076-3417/15/9/4852 |
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