In Situ Active Contour-Based Segmentation and Dimensional Analysis of Part Features in Additive Manufacturing
The evaluation of the geometric conformity of in-layer features in Additive Manufacturing (AM) remains a challenge due to low contrast between the features and the background, textural variations, imaging artifacts, and lighting conditions. This research presents a novel in situ vision-based framewo...
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MDPI AG
2025-03-01
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| Series: | Journal of Manufacturing and Materials Processing |
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| Online Access: | https://www.mdpi.com/2504-4494/9/3/102 |
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| author | Tushar Saini Panos S. Shiakolas |
| author_facet | Tushar Saini Panos S. Shiakolas |
| author_sort | Tushar Saini |
| collection | DOAJ |
| description | The evaluation of the geometric conformity of in-layer features in Additive Manufacturing (AM) remains a challenge due to low contrast between the features and the background, textural variations, imaging artifacts, and lighting conditions. This research presents a novel in situ vision-based framework for AM to identify in real-time in-layer features and estimate their shape and printed dimensions and then compare them with the as-processed layer features to evaluate geometrical differences. The framework employs a composite approach to segment features by combining simple thresholding for external features with the Chan–Vese (C–V) active contour model to identify low-contrast internal features. The effect of varying C–V parameters on the segmentation output is also evaluated. The framework was evaluated on a 20.000 mm × 20.000 mm multilayer part with internal features (two circles and a rectangle) printed using Fused Deposition Modeling (FDM). The segmentation performance of the composite method was compared with traditional methods with the results showing the composite method scoring higher in most metrics, including a maximum Jaccard index of 78.34%, effectively segmenting high- and low-contrast features. The improved segmentation enabled the identification of feature geometric differences ranging from 1 to 10 pixels (0.025 mm to 0.250 mm) after printing each layer in situ and in real time. This performance verifies the ability of the framework to detect differences at the pixel level on the evaluation platform. The results demonstrate the potential of the framework to segment features under different contrast and texture conditions, ensure geometric conformity and make decisions on any differences in feature geometry and shape. |
| format | Article |
| id | doaj-art-0264407b82a24aed95374cb560f16ef5 |
| institution | Kabale University |
| issn | 2504-4494 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Manufacturing and Materials Processing |
| spelling | doaj-art-0264407b82a24aed95374cb560f16ef52025-08-20T03:43:34ZengMDPI AGJournal of Manufacturing and Materials Processing2504-44942025-03-019310210.3390/jmmp9030102In Situ Active Contour-Based Segmentation and Dimensional Analysis of Part Features in Additive ManufacturingTushar Saini0Panos S. Shiakolas1Mechanical and Aerospace Engineering Department, The University of Texas at Arlington, Arlington, TX 76019, USAMechanical and Aerospace Engineering Department, The University of Texas at Arlington, Arlington, TX 76019, USAThe evaluation of the geometric conformity of in-layer features in Additive Manufacturing (AM) remains a challenge due to low contrast between the features and the background, textural variations, imaging artifacts, and lighting conditions. This research presents a novel in situ vision-based framework for AM to identify in real-time in-layer features and estimate their shape and printed dimensions and then compare them with the as-processed layer features to evaluate geometrical differences. The framework employs a composite approach to segment features by combining simple thresholding for external features with the Chan–Vese (C–V) active contour model to identify low-contrast internal features. The effect of varying C–V parameters on the segmentation output is also evaluated. The framework was evaluated on a 20.000 mm × 20.000 mm multilayer part with internal features (two circles and a rectangle) printed using Fused Deposition Modeling (FDM). The segmentation performance of the composite method was compared with traditional methods with the results showing the composite method scoring higher in most metrics, including a maximum Jaccard index of 78.34%, effectively segmenting high- and low-contrast features. The improved segmentation enabled the identification of feature geometric differences ranging from 1 to 10 pixels (0.025 mm to 0.250 mm) after printing each layer in situ and in real time. This performance verifies the ability of the framework to detect differences at the pixel level on the evaluation platform. The results demonstrate the potential of the framework to segment features under different contrast and texture conditions, ensure geometric conformity and make decisions on any differences in feature geometry and shape.https://www.mdpi.com/2504-4494/9/3/102additive manufacturingimage segmentationquality controlactive contoursChan–Veseprocess monitoring |
| spellingShingle | Tushar Saini Panos S. Shiakolas In Situ Active Contour-Based Segmentation and Dimensional Analysis of Part Features in Additive Manufacturing Journal of Manufacturing and Materials Processing additive manufacturing image segmentation quality control active contours Chan–Vese process monitoring |
| title | In Situ Active Contour-Based Segmentation and Dimensional Analysis of Part Features in Additive Manufacturing |
| title_full | In Situ Active Contour-Based Segmentation and Dimensional Analysis of Part Features in Additive Manufacturing |
| title_fullStr | In Situ Active Contour-Based Segmentation and Dimensional Analysis of Part Features in Additive Manufacturing |
| title_full_unstemmed | In Situ Active Contour-Based Segmentation and Dimensional Analysis of Part Features in Additive Manufacturing |
| title_short | In Situ Active Contour-Based Segmentation and Dimensional Analysis of Part Features in Additive Manufacturing |
| title_sort | in situ active contour based segmentation and dimensional analysis of part features in additive manufacturing |
| topic | additive manufacturing image segmentation quality control active contours Chan–Vese process monitoring |
| url | https://www.mdpi.com/2504-4494/9/3/102 |
| work_keys_str_mv | AT tusharsaini insituactivecontourbasedsegmentationanddimensionalanalysisofpartfeaturesinadditivemanufacturing AT panossshiakolas insituactivecontourbasedsegmentationanddimensionalanalysisofpartfeaturesinadditivemanufacturing |