TY - GEN
T1 - DEFECT SEGMENTATION FROM X-RAY COMPUTED TOMOGRAPHY OF LASER POWDER BED FUSION PARTS
T2 - ASME 2023 18th International Manufacturing Science and Engineering Conference, MSEC 2023
AU - Ouidadi, Hasnaa
AU - Xu, Boyang
AU - Guo, Shenghan
N1 - Publisher Copyright:
Copyright © 2023 by ASME.
PY - 2023
Y1 - 2023
N2 - Internal defects, e.g., lack of fusion and porosity, are major quality concerns in Laser Powder Bed Fusion (L-PBF). In post-process part inspection, X-ray Computed Tomography (XCT) is used to scan the part to reveal the defective regions inside. 2-dimensional XCT images are obtained showing the part’s cross-section at different heights (layers). Segmenting the defects from raw XCT images is necessary to locate the defected regions, evaluate the part’s quality, and enable root cause analysis. This study proposes two methods for defect segmentation, one is based on deep learning (DL) and the other based on classic machine learning (ML), and compares them with statistical image thresholding approaches (i.e., K-means, Bernsen’s, Otsu’s Thresholding). A discussion about the method-level difference among these methods is provided, revealing the merits of the proposed DL and ML methods in fast defect segmentation and transfer learning across printing conditions. A Case study is done by applying the DL, ML, and statistical image thresholding methods on real XCT images of L-PBF specimens. The defect segmentation accuracy and efficiency of the proposed DL and ML methods are evaluated. A guideline is developed for automatic defect segmentation from XCT images of L-PBF-ed parts by combining statistical image thresholding and DL/ML methods.
AB - Internal defects, e.g., lack of fusion and porosity, are major quality concerns in Laser Powder Bed Fusion (L-PBF). In post-process part inspection, X-ray Computed Tomography (XCT) is used to scan the part to reveal the defective regions inside. 2-dimensional XCT images are obtained showing the part’s cross-section at different heights (layers). Segmenting the defects from raw XCT images is necessary to locate the defected regions, evaluate the part’s quality, and enable root cause analysis. This study proposes two methods for defect segmentation, one is based on deep learning (DL) and the other based on classic machine learning (ML), and compares them with statistical image thresholding approaches (i.e., K-means, Bernsen’s, Otsu’s Thresholding). A discussion about the method-level difference among these methods is provided, revealing the merits of the proposed DL and ML methods in fast defect segmentation and transfer learning across printing conditions. A Case study is done by applying the DL, ML, and statistical image thresholding methods on real XCT images of L-PBF specimens. The defect segmentation accuracy and efficiency of the proposed DL and ML methods are evaluated. A guideline is developed for automatic defect segmentation from XCT images of L-PBF-ed parts by combining statistical image thresholding and DL/ML methods.
KW - Laser Powder Bed Fusion
KW - deep learning
KW - image segmentation
KW - image thresholding
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85176741039&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85176741039&partnerID=8YFLogxK
U2 - 10.1115/msec2023-104387
DO - 10.1115/msec2023-104387
M3 - Conference contribution
AN - SCOPUS:85176741039
T3 - Proceedings of ASME 2023 18th International Manufacturing Science and Engineering Conference, MSEC 2023
BT - Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering
PB - American Society of Mechanical Engineers
Y2 - 12 June 2023 through 16 June 2023
ER -