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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Main Authors: Jarosław Fastowicz, Mateusz Tecław, Krzysztof Okarma
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Applied Sciences
Subjects:
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.
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id doaj-art-3d0e40dd07c94d759ad1ba4e6f478a41
institution Kabale University
issn 2076-3417
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publishDate 2025-04-01
publisher MDPI AG
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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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