Technical Brief

Identifying Uncertainty in Laser Powder Bed Fusion Additive Manufacturing Models

[+] Author and Article Information
Felipe Lopez

Department of Mechanical Engineering,
University of Texas at Austin,
Austin, TX 78712
e-mail: felipelopez@utexas.edu

Paul Witherell

Systems Integration Division,
National Institute of Standards and Technology,
Gaithersburg, MD 20899

Brandon Lane

Intelligent Systems Division,
National Institute of Standards and Technology,
Gaithersburg, MD 20899

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received March 28, 2016; final manuscript received July 6, 2016; published online September 12, 2016. Assoc. Editor: Samy Missoum.The United States Government retains, and by accepting the paper for publication, the publisher acknowledges that the United States Government retains, a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for United States government purposes.

J. Mech. Des 138(11), 114502 (Sep 12, 2016) (4 pages) Paper No: MD-16-1247; doi: 10.1115/1.4034103 History: Received March 28, 2016; Revised July 06, 2016

As additive manufacturing (AM) matures, models are beginning to take a more prominent stage in design and process planning. A limitation frequently encountered in AM models is a lack of indication about their precision and accuracy. Often overlooked, model uncertainty is required for validation of AM models, qualification of AM-produced parts, and uncertainty management. This paper presents a discussion on the origin and propagation of uncertainty in laser powder bed fusion (L-PBF) models. Four sources of uncertainty are identified: modeling assumptions, unknown simulation parameters, numerical approximations, and measurement error in calibration data. Techniques to quantify uncertainty in each source are presented briefly, along with estimation algorithms to diminish prediction uncertainty with the incorporation of online measurements. The methods are illustrated with a case study based on a thermal model designed for melt pool width predictions. Model uncertainty is quantified for single track experiments, and the effect of online estimation in overhanging structures is studied via simulation.

Copyright © 2016 by ASME
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Grahic Jump Location
Fig. 1

Cascade of sources of error in computer models of AM

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Fig. 2

V&V and UQ in computational models as suggested in ASME V&V 20

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Fig. 3

Online estimation in predictive models

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Fig. 4

Melt pool width predictions (continuous line) and measurements (points) for single track scans with alloy 625

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Fig. 5

Estimated diffusion efficiency and melt pool width. Predictions are plotted as continuous lines and 95% confidence intervals are given in dashed lines.



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