Automatic Volumetric Segmentation of Additive Manufacturing Defects with 3D U-Net

TL;DR

This paper introduces a novel application of 3D U-Net neural networks for automatic volumetric segmentation of additive manufacturing defects in XCT images, achieving high accuracy and advancing machine learning use in AM quality control.

Contribution

It is the first to demonstrate 3D volumetric segmentation of AM defects using deep learning, adapting medical imaging techniques to additive manufacturing defect detection.

Findings

Achieved a mean IOU of 88.4% on AM defect segmentation

Demonstrated the effectiveness of 3D U-Net variants for AM defect detection

Contributed to the integration of machine learning in AM quality assurance

Abstract

Segmentation of additive manufacturing (AM) defects in X-ray Computed Tomography (XCT) images is challenging, due to the poor contrast, small sizes and variation in appearance of defects. Automatic segmentation can, however, provide quality control for additive manufacturing. Over recent years, three-dimensional convolutional neural networks (3D CNNs) have performed well in the volumetric segmentation of medical images. In this work, we leverage techniques from the medical imaging domain and propose training a 3D U-Net model to automatically segment defects in XCT images of AM samples. This work not only contributes to the use of machine learning for AM defect detection but also demonstrates for the first time 3D volumetric segmentation in AM. We train and test with three variants of the 3D U-Net on an AM dataset, achieving a mean intersection of union (IOU) value of 88.4%.