Li, Wei;
Lambert-Garcia, Rubén;
Getley, Anna CM;
Kim, Kwan;
Bhagavath, Shishira;
Majkut, Marta;
Rack, Alexander;
... Leung, Chu Lun Alex; + view all
(2024)
AM-SegNet for additive manufacturing in situ X-ray image segmentation and
feature quantification.
Virtual and Physical Prototyping
, 19
(1)
10.1080/17452759.2024.2325572.
Preview |
Text
AM-SegNet for additive manufacturing in situ X-ray image segmentation and feature quantification.pdf - Published Version Download (4MB) | Preview |
Abstract
Synchrotron X-ray imaging has been utilised to detect the dynamic behaviour of molten pools during the metal additive manufacturing (AM) process, where a substantial amount of imaging data is generated. Here, we develop an efficient and robust deep learning model, AM-SegNet, for segmenting and quantifying high-resolution X-ray images and prepare a large-scale database consisting of over 10,000 pixel-labelled images for model training and testing. AM-SegNet incorporates a lightweight convolution block and a customised attention mechanism, capable of performing semantic segmentation with high accuracy (∼96%) and processing speed (< 4 ms per frame). The segmentation results can be used for quantification and multi-modal correlation analysis of critical features (e.g. keyholes and pores). Additionally, the application of AM-SegNet to other advanced manufacturing processes is demonstrated. The proposed method will enable end-users in the manufacturing and imaging domains to accelerate data processing from collection to analytics, and provide insights into the processes’ governing physics.
| Type: | Article |
|---|---|
| Title: | AM-SegNet for additive manufacturing in situ X-ray image segmentation and feature quantification |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1080/17452759.2024.2325572 |
| Publisher version: | http://dx.doi.org/10.1080/17452759.2024.2325572 |
| Language: | English |
| Additional information: | © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. |
| Keywords: | Additive manufacturing (AM); synchrotron imaging; semantic segmentation; deep learning; laser powder bed fusion (LPBF); directed energy deposition (DED); machine learning; artificial intelligence |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10189088 |
Archive Staff Only
 |
View Item |
