Digital 3D defect maps: detecting localised porosity with high-speed melt pool imaging data in LPBF
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Adoption of metal additive manufacturing for critical applications is hindered by the costs of post-build quality inspection. In-process monitoring offers a promising alternative by enabling parallel construction of digital 3Ddefect maps for every component manufactured. In this work, we present a system to detect local regions of porosity, containing both keyhole and lack-of-fusion defects, in laser powder bed fusion parts. A coaxial high-speed melt pool imaging setup operating at 20 kHz acquires feature-rich data, capturing images approximately every 37.5 μm along scan tracks and records over 30 million melt pool images per hour of build time. Using these data, a gradient-boosted decision tree model is trained to classify porosity levels in localised 2 mm voxel
bins. The system achieves a state-of-the-art detection threshold of 0.11% porosity, defined by the standard
non-destructive evaluation criterion of 90% probability of detection at 95% confidence. By training on datasets
containing realistic, organically generated porosity and demonstrating the most accurate localised porosity
detection yet reported, this work represents a significant advance towards practical, industrially relevant in-process defect detection for additive manufacturing.
bins. The system achieves a state-of-the-art detection threshold of 0.11% porosity, defined by the standard
non-destructive evaluation criterion of 90% probability of detection at 95% confidence. By training on datasets
containing realistic, organically generated porosity and demonstrating the most accurate localised porosity
detection yet reported, this work represents a significant advance towards practical, industrially relevant in-process defect detection for additive manufacturing.
Date Issued
2025-08-05
Date Acceptance
2025-10-01
Citation
Additive Manufacturing, 2025, 111
ISSN
2214-8604
Publisher
Elsevier BV
Journal / Book Title
Additive Manufacturing
Volume
111
Copyright Statement
© 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Publication Status
Published
Article Number
104982
Date Publish Online
2025-10-09