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ORIGINAL RESEARCH article

Front. Met. Alloy
Sec. Physical Metallurgy
Volume 3 - 2024 | doi: 10.3389/ftmal.2024.1397636

Utilizing Computational Materials Modeling and Big Data to Develop Printable High Gamma Prime Superalloys for Additive Manufacturing

Provisionally accepted
Jonathon Bracci Jonathon Bracci 1*Kevin Kaufmann Kevin Kaufmann 1,2Jesse Schlatter Jesse Schlatter 1James Vecchio James Vecchio 1Naixie Zhou Naixie Zhou 1Sicong Jiang Sicong Jiang 1Kenneth S. Vecchio Kenneth S. Vecchio 1,2Justin Cheney Justin Cheney 1
  • 1 Oerlikon Metco (US) Inc., San Diego, United States
  • 2 University of California, San Diego, La Jolla, California, United States

The final, formatted version of the article will be published soon.

    Metal-based additive manufacturing offers potential to disrupt the manufacturing process across multiple industries. However, the vast majority of modern alloys are incompatible with the complex thermal histories of additive manufacturing. For example, the high gamma prime forming nickel-based superalloys are of considerable commercial interest owing to their properties; however, their gamma prime content renders them non-weldable and prone to cracking during additive manufacturing. Computational materials modeling and big data analytics is becoming an increasingly valuable tool for developing new alloys for additive manufacturing. This work reports the use of such tools toward the design of a high gamma prime superalloy with reduced cracking susceptibility while maintaining similar hardness to CM247. Experimental fabrication and characterization of the candidate alloys is performed. Results show the candidate alloys have improved printability, up to 41x reduction in crack density (mm/mm 2 ) compared with CM247, and good agreement with the modeled predictions.

    Keywords: Additive manufacturing, Nickel-based superalloys, calculation of phase diagrams, computational materials engineering, Gamma prime

    Received: 07 Mar 2024; Accepted: 26 Jun 2024.

    Copyright: © 2024 Bracci, Kaufmann, Schlatter, Vecchio, Zhou, Jiang, Vecchio and Cheney. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Jonathon Bracci, Oerlikon Metco (US) Inc., San Diego, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.