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DTSTART;TZID=America/Denver:20231112T115000
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UID:submissions.supercomputing.org_SC23_sess415_ws_esp102@linklings.com
SUMMARY:Uncertainty Quantification of Metal Additive Manufacturing Process
 ing Conditions Through the Use of Exascale Computing
DESCRIPTION:Robert Carson (Lawrence Livermore National Laboratory (LLNL)),
  Matt Rolchigo and John Coleman (Oak Ridge National Laboratory (ORNL)), Mi
 khail Titov (Brookhaven National Laboratory), Jim Belak (Lawrence Livermor
 e National Laboratory (LLNL)), and Matt Bement (Oak Ridge National Laborat
 ory (ORNL))\n\nMetal additive manufacturing is a disruptive manufacturing 
 technology that opens the design space for parts outside those possible fr
 om traditional manufacturing methods. In order to accelerate industry and 
 R&D needs to certify AM parts, the ExaAM project has developed a suite of 
 exascale-ready computational tools to model the process-to-structure-to-pr
 operties relationship for additively manufactured metal components. One to
 ol is a UQ pipeline to quantify the effect uncertainty in processing condi
 tions has on local mechanical responses. We present an overview of this pi
 peline and its codes. Using ORNL’s exascale computer, Frontier, we utilize
  this pipeline to cross multiple length and time scales to predict local m
 echanical response of a location within a complex AM bridge part, AMB2018-
 01 produced by NIST as part of their 2018 AM-Bench test series. Our result
 s are then compared to experimental mechanical tests of parts from the NIS
 T build to quantify the error in the ExaAM UQ workflow.\n\nTag: Performanc
 e Optimization\n\nRegistration Category: Workshop Reg Pass\n\nSession Chai
 rs: Tiernan Casey (Sandia National Laboratories) and Antigoni Georgiadou (
 Oak Ridge National Laboratory (ORNL))\n\n
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