Validation of materials-informed digital twin: Mapping residual strains in HSLA steel weldment using high energy X-rays

Charles R. Fisher, Kelly E. Nygren, Armand J. Beaudoin

Research output: Contribution to journalArticlepeer-review


The shipbuilding industry has placed an emphasis on establishing digital twin prototypes to advance computational lifecycle analysis capabilities. However, material processing data is not currently included in simulations for structural performance, which could lead to inaccuracies. Integrated Computational Materials Engineering (ICME) techniques yield a path to solving this problem through linking digital information across multiple length scales in order to minimize the time and cost of physical testing needed for validation. This paper details the initial work of a larger materials-informed digital twin effort seeking to validate the need for fabrication inclusion in lifecycle analysis. First, the selection and fabrication of the “sister sample” specimens used for physical validation from a single plate of HSLA-100 steel. Second, the finite-element analysis (FEA)-based welding simulations of the reinforced dog-bone weldment. Third, comparison of predicted strain data from FEA simulations to two-dimensional (2D) strain maps using energy dispersive diffraction (EDD) techniques at the Cornell High-Energy Synchrotron Source (CHESS). The results showed high correlation between the measured EDD data and the FEA predictions, particularly for the high strain (>0.0015) fields of first principle strain vectors measured parallel to each welding pass. This computational validation is the first step towards validating the materials-informed digital twin method for increasing accuracy of structural analysis and fatigue lifecycle evaluation.

Original languageEnglish (US)
Pages (from-to)75-87
Number of pages13
JournalJournal of Manufacturing Processes
StatePublished - Feb 2022
Externally publishedYes


  • FEA
  • Residual stress
  • Synchrotron diffraction
  • Welding

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering


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