Deep-Learning-Based Segmentation of Keyhole in In-Situ X-ray Imaging of Laser Powder Bed Fusion

• Published in: Materials Vol. 17; no. 2; p. 510
• Main Authors: Dong, William, Lian, Jason, Yan, Chengpo, Zhong, Yiran, Karnati, Sumanth, Guo, Qilin, Chen, Lianyi, Morgan, Dane
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2024
• Subjects: Beds (process engineering); Computer vision; Datasets; Deep learning; Defects; Experiments; Image segmentation; Imaging techniques; keyhole; Keyholes; Labels; laser powder bed fusion; Lasers; Machine learning; Machine vision; Morphology; Powder beds; Powders; Radiography; Semantics; X ray imagery; deep learning; keyhole; image segmentation; laser powder bed fusion
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma17020510

In laser powder bed fusion processes, keyholes are the gaseous cavities formed where laser interacts with metal, and their morphologies play an important role in defect formation and the final product quality. The in-situ X-ray imaging technique can monitor the keyhole dynamics from the side and capture keyhole shapes in the X-ray image stream. Keyhole shapes in X-ray images are then often labeled by humans for analysis, which increasingly involves attempting to correlate keyhole shapes with defects using machine learning. However, such labeling is tedious, time-consuming, error-prone, and cannot be scaled to large data sets. To use keyhole shapes more readily as the input to machine learning methods, an automatic tool to identify keyhole regions is desirable. In this paper, a deep-learning-based computer vision tool that can automatically segment keyhole shapes out of X-ray images is presented. The pipeline contains a filtering method and an implementation of the BASNet deep learning model to semantically segment the keyhole morphologies out of X-ray images. The presented tool shows promising average accuracy of 91.24% for keyhole area, and 92.81% for boundary shape, for a range of test dataset conditions in Al6061 (and one AliSi10Mg) alloys, with 300 training images/labels and 100 testing images for each trial. Prospective users may apply the presently trained tool or a retrained version following the approach used here to automatically label keyhole shapes in large image sets.