Enhanced detection of surface deformations in LPBF using deep convolutional neural networks and transfer learning from a porosity model
Abstract Our previous research papers have shown the potential of deep-learning models for real-time detection and control of porosity defects in 3D printing, specifically in the laser powder bed fusion (LPBF) process. Extending these models to identify other defects like surface deformation poses a...
Saved in:
| Main Authors: | Muhammad Ayub Ansari, Andrew Crampton, Samer Mohammed Jaber Mubarak |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-11-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-76445-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Comparative study of LPBF Ta–Ti alloy: microstructural evolution and deformation behavior
by: Zhenyu Yang, et al.
Published: (2025-09-01) -
Enhancing geometric modeling in convolutional neural networks: limit deformable convolution
by: Wei Wang, et al.
Published: (2025-03-01) -
Influence of LPBF Process Parameters on the Surface Quality of Stainless Steel on the Adhesion Properties with Thermoplastics
by: Florian Lehmann, et al.
Published: (2025-05-01) -
Improving Face Presentation Attack Detection Through Deformable Convolution and Transfer Learning
by: Shakeel Muhammad Ibrahim, et al.
Published: (2025-01-01) -
Bangladeshi Vehicle Classification and Detection Using Deep Convolutional Neural Networks With Transfer Learning
by: Farid, et al.
Published: (2025-01-01)