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May 8, 2026Next MaterialsOpen Access

Defect localization using region of interest and histogram-based enhancement approaches in fused filament fabrication additive manufacturing

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Authors

MAMd Manjurul AhsanUniversity of OklahomaYLYingtao LiuUniversity of OklahomaSRShivakumar RamanUniversity of Oklahoma

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Implication

Randomized trial demonstrates enhanced defect localization in additive manufacturing, indicating improved quality control.

Key Points

  • This study aims to enhance defect detection and localization in 3D-printed objects using novel pre-processing techniques and deep learning models.
  • Utilized Region of Interest (ROI) selection, Histogram Equalization (HE), and Details Enhancement (DE) with a modified VGG16 model.
  • Achieved defect classification across five types using class-weighted training on an imbalanced FDM dataset.
  • Applied LIME and Grad-CAM for interpretable visualizations of model decision-making.
  • Achieved perfect accuracy of 1.00 and F1-score of 1.00 on the test set using modified VGG16 model.
  • Demonstrated computational efficiency with 30,713M FLOPs and 15M parameters, the lowest among compared models.
  • Utilized custom separable convolution layer for efficient and accurate defect localization.

Cite This Study

Ahsan et al. (2026) studied this question.

synapsesocial.com/papers/69fd7d94bfa21ec5bbf05e75https://doi.org/10.1016/j.nxmate.2026.102174
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