Predictive models for fatigue property of laser powder bed fusion stainless steel 316L

Meng Zhang, Chen-Nan Sun, Xiang Zhang, Jun Wei, David Hardacre, Hua Li

Research output: Contribution to journalArticle

20 Citations (Scopus)
41 Downloads (Pure)

Abstract

The selection of appropriate processing parameters is crucial for producing parts with target properties via the laser powder bed fusion (L-PBF) process. In this work, the fatigue properties of L-PBF stainless steel 316L under controlled changes in laser power and scan speed were studied by employing the statistical response surface method. Processing regions corresponding to different fatigue failure mechanisms were identified. The optimum fatigue properties are associated with crack initiation from microstructure defect, which, by acting as the weakest link, creates enhanced porosity-tolerance at applied stress approaching the fatigue limit. Deviations from the optimum processing condition lead to strength degradation and porosity-driven cracking. Based on the observed relations between microstructural features and failure behaviour, a processing-independent fatigue prediction model was proposed. The microstructure-driven failure was modelled by a reference S-N curve where the intrinsic effect of microstructure inhomogeneity was accounted for by applying a reduction factor on fatigue life. For the porosity-driven failure, high cycle fatigue life follows an inverse-square-root relation with porosity fraction. This relation was incorporated into the Basquin equation for predicting the fatigue strength parameters.
Original languageEnglish
Pages (from-to)42-54
Number of pages13
JournalMaterials & Design
Volume145
Early online date19 Feb 2018
DOIs
Publication statusPublished - 5 May 2018

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Materials & Design. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Materials & Design, [145, (2018)] DOI: 10.1016/j.matdes.2018.02.054

© 2018, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Keywords

  • fatigue
  • selective laser melting
  • stainless steel 316L
  • Basquin equation
  • predictive model
  • porosity

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