Linking pyrometry to porosity in additively manufactured metals

doi:10.1016/j.addma.2019.100946

This content will become publicly available on November 17, 2020

Title: Linking pyrometry to porosity in additively manufactured metals

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Abstract

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.

Authors:

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

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

Publication Date:: 2019-11-17

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

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:: 1595013

Report Number(s):: SAND-2019-14425J
Journal ID: ISSN 2214-8604; 682624

Grant/Contract Number:: AC04-94AL85000; NA0003525

Resource Type:: Accepted Manuscript

Journal Name:: Additive Manufacturing

Additional Journal Information:: Journal Volume: 31; Journal Issue: C; Journal ID: ISSN 2214-8604

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 36 MATERIALS SCIENCE; Laser powder bed fusion; Insitu monitoring; Pyrometry; Porosity; Data analytics

Citation Formats


                    Mitchell, John A., Ivanoff, Thomas A., Dagel, Daryl, Madison, Jonathan D., and Jared, Bradley. Linking pyrometry to porosity in additively manufactured metals.  United States: N. p., 2019. 
        Web.  doi:10.1016/j.addma.2019.100946.

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                    Mitchell, John A., Ivanoff, Thomas A., Dagel, Daryl, Madison, Jonathan D., & Jared, Bradley. Linking pyrometry to porosity in additively manufactured metals.  United States.  doi:10.1016/j.addma.2019.100946.

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                    Mitchell, John A., Ivanoff, Thomas A., Dagel, Daryl, Madison, Jonathan D., and Jared, Bradley. Sun .  
        "Linking pyrometry to porosity in additively manufactured metals".  United States.  doi:10.1016/j.addma.2019.100946.

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@article{osti_1595013,

  title        = {Linking pyrometry to porosity in additively manufactured metals},

  author       = {Mitchell, John A. and Ivanoff, Thomas A. and Dagel, Daryl and Madison, Jonathan D. and Jared, Bradley},

  abstractNote = {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.},

  doi          = {10.1016/j.addma.2019.100946},

  journal      = {Additive Manufacturing},

  number       = C,

  volume       = 31,

  place        = {United States},

  year         = {2019},

  month        = {11}

}

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