Ziad, E.
, Ju, F.
, Yang, Z.
and Lu, Y.
(2024),
Pyramid Learning Based Part-to-Part Consistency Analysis in Laser Powder Bed Fusion, ASME 2024 19th International Manufacturing Science and Engineering Conference, Knoxville, TN, US, [online], https://doi.org/10.1115/MSEC2024-124538, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=957216
(Accessed April 30, 2025)
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Pyramid Learning Based Part-to-Part Consistency Analysis in Laser Powder Bed Fusion
Published
Author(s)
Erfan Ziad, Feng Ju, ,
Abstract
With the rapid growth of the manufacturing industry, laserbased metal additive manufacturing, such as laser powder bed fusion, has the potential to usher in a revolution. However, its widespread adoption is contingent on the resolution of severa lchallenges. A significant challenge is the uncertainty associated with part consistency when standardized materials are used in additive manufacturing processes. To ensure the quality and reproducibility of AM parts, it is essential to ensure that consistency is maintained. This study delves into an assessment of part-to-part consistency, leveraging a Pyramid learning-based technique that utilizes X-ray computed tomography (XCT) images for four nominally identical parts. Employing machine learning, this approach adopts a hierarchical feature system to enhance model performance. Pyramid Learning not only improves the accuracy of part-to-part consistency scanning but also reduces noise, bolstering overall robustness. The findings showcased the efficacy of pyramid learning in enhancing metric performance when sufficient detail is present. It also provides guidance on locating defects and deformations for the AM parts.Proceedings Title
ASME 2024 19th International Manufacturing Science and Engineering Conference
Volume
1
Conference Dates
June 17-20, 2024
Conference Location
Knoxville, TN, US
Conference Title
ASME MSEC 2024
Pub Type
Conferences
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Keywords
Pyramid Learning, Machine Learning, Computer Vision, Additive Manufacturing, X-Ray Computed Tomography, Laser Powder Bed Fusion
Citation
Issues
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Created August 20, 2024, Updated March 3, 2025