Born Qualified Grand Challenge LDRD Final Report

Technical Report · Mon Oct 01 00:00:00 EDT 2018

DOI:https://doi.org/10.2172/1481619· OSTI ID:1481619

Roach, Robert Allen ^[1]; Argibay, Nicolas ^[1]; Allen, Kyle ^[1]; Balch, Dorian K. ^[1]; Beghini, Lauren L. ^[1]; Bishop, Joseph E. ^[1]; Boyce, Brad ^[1]; Brown, Judith Alice ^[1]; Burchard, Ross L. ^[1]; Chandross, Michael E. ^[1]; Cook, Adam ^[1]; Diantonio, Christopher ^[1]; Dressler, Amber Dawn ^[1]; Forrest, Eric Christopher ^[1]; Ford, Kurtis Ross ^[1]; Ivanoff, Thomas ^[1]; Jared, Bradley Howell ^[1]; Johnson, Kyle ^[1]; Kammler, Daniel ^[1]; Koepke, Joshua Robert ^[1] ; Kustas, Andrew ^[1]; Lavin, Judith Maria ^[1]; Leathe, Nicholas ^[1]; Lester, Brian T. ^[1]; Madison, Jonathan D ^[1]; Mani, Seethambal S. ^[1]; Martinez, Mario J. ^[1]; Moser, Daniel ^[1]; Rodgers, Theron ^[1]; Seidl, Daniel Thomas ^[1]; Brown-Shaklee, Harlan James ^[1]; Stanford, Joshua ^[1]; Stender, Michael ^[1]; Sugar, Joshua Daniel ^[1]; Swiler, Laura Painton ^[1]; Taylor, Samantha ^[1]; Trembacki, Bradley ^[1]

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1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

This SAND report fulfills the final report requirement for the Born Qualified Grand Challenge LDRD. Born Qualified was funded from FY16-FY18 with a total budget of ~$13M over the 3 years of funding. Overall 70+ staff, Post Docs, and students supported this project over its lifetime. The driver for Born Qualified was using Additive Manufacturing (AM) to change the qualification paradigm for low volume, high value, high consequence, complex parts that are common in high-risk industries such as ND, defense, energy, aerospace, and medical. AM offers the opportunity to transform design, manufacturing, and qualification with its unique capabilities. AM is a disruptive technology, allowing the capability to simultaneously create part and material while tightly controlling and monitoring the manufacturing process at the voxel level, with the inherent flexibility and agility in printing layer-by-layer. AM enables the possibility of measuring critical material and part parameters during manufacturing, thus changing the way we collect data, assess performance, and accept or qualify parts. It provides an opportunity to shift from the current iterative design-build-test qualification paradigm using traditional manufacturing processes to design-by-predictivity where requirements are addressed concurrently and rapidly. The new qualification paradigm driven by AM provides the opportunity to predict performance probabilistically, to optimally control the manufacturing process, and to implement accelerated cycles of learning. Exploiting these capabilities to realize a new uncertainty quantification-driven qualification that is rapid, flexible, and practical is the focus of this effort.

• Research Organization: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 1481619
• Report Number(s): SAND-2018-11276; 669545
• Country of Publication: United States
• Language: English