Enhancement of 3D Object Detection Using Deep Learning
Clicks: 1
ID: 312708
2025
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
0.0
/100
1 views
0 readers
AI Quality Assessment
Not analyzed
Readership in this journal
Ranked #298 of 705 articles by views in Journal of Computing & Biomedical Informatics
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
3D printing, an advanced form of additive manufacturing, has revolutionized production by enabling the creation of complex, customized objects used across industries like aerospace, healthcare, and automotive. Despite its benefits, 3D printing faces challenges such as defects (cracks, warping, surface imperfections) that compromise the structural integrity of printed objects, especially in high-precision applications. Traditional defect detection methods rely on manual inspection or image processing, which are time-consuming and error-prone. To address these issues, deep learning has been applied for automated defect detection. The proposed model, DenseNet201, is a pre-trained convolutional neural network (CNN), fine-tuned on a 3D printing defect dataset to classify defects and non-defects. Enhanced techniques, such as data augmentation, dropout regularization, and optimizer tuning (using the Adam optimizer), are implemented to optimize the model's performance. These methods contribute to the enhancement of 3D printing quality by improving defect detection accuracy. The trained model achieved maximum accuracy 93%, it also shows balanced performance across all classes with a score of 0.92 for average precision, recall, and F1 score; while showing the best performance on key defect types with F1 scores of 0.98 and 0.93 displaying strong defect detection which ultimately enhances manufacturing efficiency, reduces waste, and minimizes costs associated with traditional inspection methods. This approach aligns with the goal of utilizing deep learning to significantly improve the quality control process in 3D object detection.
| Reference Key |
imported_1777056172_69ebb9aca272f
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Seyed Ebrahim Hosseini |
| Journal | Journal of Computing & Biomedical Informatics |
| Year | 2025 |
| DOI |
DOI not found
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.