Metal additive manufacturing simulation across length, time, and computing scales

Plotkowski, Alex; Rolchigo, Matt; Wagner, Gregory; Reeve, Samuel Temple; Coleman, John; Knapp, Gerry; Levine, Lyle; To, Albert; DeWitt, Stephen; Dugast, Florian; Mahadevan, Sankaran; Newman, Christopher Kyle; Stump, Benjamin; Bement, Matt; Turner, John

doi:10.1177/09506608251394155

Metal additive manufacturing simulation across length, time, and computing scales

Journal Article · Thu Dec 11 00:00:00 EST 2025 · International Materials Reviews

DOI:https://doi.org/10.1177/09506608251394155· OSTI ID:3013504

^[1]; ^[1]; Wagner, Gregory ^[2]; ^[1]; ^[1]; ^[1]; Levine, Lyle ^[3]; To, Albert ^[4]; DeWitt, Stephen ^[1]; ^[4]; Mahadevan, Sankaran ^[5]; Newman, Christopher Kyle ^[6]; ^[1]; Bement, Matt ^[1]; ^[1]

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Northwestern Univ., Evanston, IL (United States)
National Institute of Standards and Technology
Univ. of Pittsburgh, PA (United States)
Vanderbilt Univ., Nashville, TN (United States)
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Metal additive manufacturing (AM) offers a unique opportunity for production of advanced materials and complex geometries. However, variability in microstructure and properties challenges conventional approaches to design, process optimization, qualification, and materials selection. Modeling and simulation can improve understanding of AM processing and materials, but also poses major challenges for existing computational methods. Simultaneously, modern scientific computing hardware has become increasingly complex, most notably with the adoption of hybrid architectures such as Graphical Processing Units (GPUs). If appropriately utilized, emerging computational capabilities provide an opportunity to reveal new insight into AM processing and the resulting material structure and properties. In this review we describe the computational AM landscape, identify critical gaps, and highlight opportunities to impact the development and application of AM. First, the requirements and challenges of representative AM problem statements will be defined. Here, these problems range from scientific studies to industrial applications and are designed to capture the breadth of challenges facing the AM community. Next, the current state of AM modeling and simulation is evaluated, broken down by enabling hardware and software, process simulation, microstructure simulation, and property simulation. Each section describes the diversity of simulation approaches and associated trade-offs in physical fidelity and computational expense. Each area is then assessed based on their suitability and readiness for current and developing computational architectures. Lastly, the greatest opportunities for future research and application are highlighted, including gaps in modeling capabilities, opportunities for near-term application, and key scientific challenges.

View Accepted Manuscript (DOE)

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

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Materials & Manufacturing Technologies Office (AMMTO); USDOE Office of Nuclear Energy (NE); USDOE Office of Science (SC)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 3013504

Journal Information:: International Materials Reviews, Journal Name: International Materials Reviews; ISSN 1743-2804; ISSN 0950-6608

Publisher:: Taylor & FrancisCopyright Statement

Country of Publication:: United States

Language:: English

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