Predicting laser weld reliability with stochastic reduced-order models. Predicting laser weld reliability

Emery, John M.; Field, Richard V.; Foulk, James W.; Karlson, Kyle N.; Grigoriu, Mircea D.

doi:10.1002/nme.4935

Predicting laser weld reliability with stochastic reduced-order models. Predicting laser weld reliability

Journal Article · Tue May 26 00:00:00 EDT 2015 · International Journal for Numerical Methods in Engineering

DOI:https://doi.org/10.1002/nme.4935· OSTI ID:1140327

Emery, John M. ^[1]; Field, Richard V. ^[1]; Foulk, James W. ^[2]; Karlson, Kyle N. ^[2]; Grigoriu, Mircea D. ^[3]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Cornell Univ., Ithaca, NY (United States)

Laser welds are prevalent in complex engineering systems and they frequently govern failure. The weld process often results in partial penetration of the base metals, leaving sharp crack-like features with a high degree of variability in the geometry and material properties of the welded structure. Furthermore, accurate finite element predictions of the structural reliability of components containing laser welds requires the analysis of a large number of finite element meshes with very fine spatial resolution, where each mesh has different geometry and/or material properties in the welded region to address variability. We found that traditional modeling approaches could not be efficiently employed. Consequently, a method is presented for constructing a surrogate model, based on stochastic reduced-order models, and is proposed to represent the laser welds within the component. Here, the uncertainty in weld microstructure and geometry is captured by calibrating plasticity parameters to experimental observations of necking as, because of the ductility of the welds, necking – and thus peak load – plays the pivotal role in structural failure. The proposed method is exercised for a simplified verification problem and compared with the traditional Monte Carlo simulation with rather remarkable results.

Research Organization:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC04-94AL85000

OSTI ID:: 1140327

Alternate ID(s):: OSTI ID: 1400915

Report Number(s):: SAND--2014-1043J; 499015

Journal Information:: International Journal for Numerical Methods in Engineering, Journal Name: International Journal for Numerical Methods in Engineering Journal Issue: 12 Vol. 103; ISSN 0029-5981

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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Cited By (2)

The third Sandia fracture challenge: predictions of ductile fracture in additively manufactured metal Kramer, Sharlotte L. B.; Jones, Amanda; Mostafa, Ahmed International Journal of Fracture, Vol. 218, Issue 1-2 https://doi.org/10.1007/s10704-019-00361-1	journal	July 2019
Predicting the reliability of an additively-manufactured metal part for the third Sandia fracture challenge by accounting for random material defects Johnson, Kyle L.; Emery, John M.; Hammetter, Chris I. International Journal of Fracture https://doi.org/10.1007/s10704-019-00368-8	journal	July 2019

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36 MATERIALS SCIENCE
42 ENGINEERING
laser welds
monte carlo simulation
stochastic reduced-order models
structural reliability

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