Enabling rapid X-ray CT characterisation for additive manufacturing using CAD models and deep learning-based reconstruction

Ziabari, Amirkoushyar; Venkatakrishnan, Singanallur V.; Snow, Zackary; Lisovich, Aleksander; Sprayberry, Michael; Brackman, Paul; Frederick, Curtis; Bhattad, Pradeep; Graham, Sarah; Bingham, Philip; Dehoff, Ryan; Plotkowski, Alex; Paquit, Vincent

doi:10.1038/s41524-023-01032-5

Enabling rapid X-ray CT characterisation for additive manufacturing using CAD models and deep learning-based reconstruction

Journal Article · Tue May 30 00:00:00 EDT 2023 · npj Computational Materials

DOI:https://doi.org/10.1038/s41524-023-01032-5· OSTI ID:1986242

^[1]; ^[1]; ^[1]; Lisovich, Aleksander ^[2]; ^[1]; Brackman, Paul ^[2]; Frederick, Curtis ^[2]; Bhattad, Pradeep ^[2]; Graham, Sarah ^[1]; ^[1]; ^[1]; ^[1]; ^[1]

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
ZEISS Industrial Metrology LLC, Maple Grove, MN (United States)

Metal additive manufacturing (AM) offers flexibility and cost-effectiveness for printing complex parts but is limited to few alloys. Qualifying new alloys requires process parameter optimisation to produce consistent, high-quality components. High-resolution X-ray computed tomography (XCT) has not been effective for this task due to artifacts, slow scan speed, and costs. We propose a deep learning-based approach for rapid XCT acquisition and reconstruction of metal AM parts, leveraging computer-aided design models and physics-based simulations of nonlinear interactions between X-ray radiation and metals. This significantly reduces beam hardening and common XCT artifacts. We demonstrate high-throughput characterisation of over a hundred AlCe alloy components, quantifying improvements in characterisation time and quality compared to high-resolution microscopy and pycnometry. Our approach facilitates investigating the impact of process parameters and their geometry dependence in metal AM.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: Shanghai Institute of Ceramics of the Chinese Academy of Sciences; USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Materials & Manufacturing Office (AMMTO)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1986242

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 9; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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