Additive manufacturing (AM) is a novel fabrication technique capable of producing highly complex parts. Nevertheless, a major challenge is the quality assurance of the AM fabricated parts. While there are several ways of approaching this problem, how to develop informative process signatures to detect part anomalies for quality control is still an open question. The objective of this study is to build a new layer-wise process signature model to characterize the thermal-defect relationship. Based on melt pool images, we propose novel layer-wise key process signatures, which are calculated using multilinear principal component analysis (MPCA) and are directly correlated with the layer-wise quality of the part. The resultant layer-wise quality features can be used to predict the overall defect distribution of a fabricated layer during the build. The proposed model is validated through a case study based on a direct laser deposition experiment, where the layer-wise quality of the part is predicted on the fly. The accuracy of prediction is calculated using three measures (i.e., recall, precision, and F-score), showing reasonable success of the proposed methodology in predicting layer-wise quality. The proposed quality prediction methodology enables online process correction to eliminate anomalies and to ultimately improve the quality of the fabricated parts.
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August 2019
Research-Article
Layer-Wise Modeling and Anomaly Detection for Laser-Based Additive Manufacturing
Seyyed Hadi Seifi,
Seyyed Hadi Seifi
Department of Industrial and Systems Engineering,
Starkville, MS 39762
e-mail: ss4350@msstate.edu
Mississippi State University
,Starkville, MS 39762
e-mail: ss4350@msstate.edu
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Wenmeng Tian,
Wenmeng Tian
Department of Industrial and Systems Engineering,
Starkville, MS 39762
e-mail: tian@ise.msstate.edu
Mississippi State University
,Starkville, MS 39762
e-mail: tian@ise.msstate.edu
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Haley Doude,
Haley Doude
Center for Advanced Vehicular Systems,
Starkville, MS 39762
e-mail: haley@cavs.msstate.edu
Mississippi State University
,Starkville, MS 39762
e-mail: haley@cavs.msstate.edu
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Linkan Bian
Linkan Bian
1
Department of Industrial and Systems Engineering,
Center for Advanced Vehicular Systems,
Starkville, MS 39762
e-mail: bian@ise.msstate.edu
Center for Advanced Vehicular Systems,
Mississippi State University
,Starkville, MS 39762
e-mail: bian@ise.msstate.edu
1Corresponding author.
Search for other works by this author on:
Seyyed Hadi Seifi
Department of Industrial and Systems Engineering,
Starkville, MS 39762
e-mail: ss4350@msstate.edu
Mississippi State University
,Starkville, MS 39762
e-mail: ss4350@msstate.edu
Wenmeng Tian
Department of Industrial and Systems Engineering,
Starkville, MS 39762
e-mail: tian@ise.msstate.edu
Mississippi State University
,Starkville, MS 39762
e-mail: tian@ise.msstate.edu
Haley Doude
Center for Advanced Vehicular Systems,
Starkville, MS 39762
e-mail: haley@cavs.msstate.edu
Mississippi State University
,Starkville, MS 39762
e-mail: haley@cavs.msstate.edu
Mark A. Tschopp
Linkan Bian
Department of Industrial and Systems Engineering,
Center for Advanced Vehicular Systems,
Starkville, MS 39762
e-mail: bian@ise.msstate.edu
Center for Advanced Vehicular Systems,
Mississippi State University
,Starkville, MS 39762
e-mail: bian@ise.msstate.edu
1Corresponding author.
Manuscript received August 4, 2018; final manuscript received May 23, 2019; published online June 21, 2019. Assoc. Editor: Qiang Huang. This work is in part a work of the U.S. Government. ASME disclaims all interest in the U.S. Government’s contributions.
J. Manuf. Sci. Eng. Aug 2019, 141(8): 081013 (12 pages)
Published Online: June 21, 2019
Article history
Received:
August 4, 2018
Revision Received:
May 23, 2019
Accepted:
May 26, 2019
Citation
Seifi, S. H., Tian, W., Doude, H., Tschopp, M. A., and Bian, L. (June 21, 2019). "Layer-Wise Modeling and Anomaly Detection for Laser-Based Additive Manufacturing." ASME. J. Manuf. Sci. Eng. August 2019; 141(8): 081013. https://doi.org/10.1115/1.4043898
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