Linking pyrometry to porosity in additively manufactured metals

Journal Article · 17 November 2019 · Additive Manufacturing

DOI: https://doi.org/10.1016/j.addma.2019.100946 · OSTI ID:1595013

Mitchell, John A. ^[1]; Ivanoff, Thomas A. ^[1]; Dagel, Daryl ^[1]; Madison, Jonathan D. ^[1]; Jared, Bradley ^[1]

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

Porosity in additively manufactured metals can reduce material strength which is generally considered undesirable. Although studies have shown relationships between process parameters and porosity, monitoring strategies for defect detection and pore formation are still needed. In this paper, instantaneous anomalous conditions are detected in-situ via pyrometry during laser powder bed fusion additive manufacturing and correlated with voids observed using post-build micro-computed tomography. Large two-color pyrometry data sets were used to estimate instantaneous temperatures, melt pool orientations and aspect ratios. Machine learning algorithms were then applied to processed pyrometry data to detect outlier images and conditions. It is shown that melt pool outliers are good predictors of voids observed post-build. With this approach, real time process monitoring can be incorporated into systems to detect defect and void formation. Alternatively, using the methodology presented here, pyrometry data can be post processed for porosity assessment.

• Research Organization: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: AC04-94AL85000; NA0003525
• OSTI ID: 1595013
• Report Number(s): SAND-2019-14425J; 682624
• Journal Information: Additive Manufacturing, Vol. 31, Issue C; ISSN 2214-8604
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English