The objective of this work is to identify failure modes and detect the onset of process anomalies in additive manufacturing (AM) processes, specifically focusing on fused filament fabrication (FFF). We accomplish this objective using advanced Bayesian nonparametric analysis of in situ heterogeneous sensor data. Experiments are conducted on a desktop FFF machine instrumented with a heterogeneous sensor array including thermocouples, accelerometers, an infrared (IR) temperature sensor, and a real-time miniature video borescope. FFF process failures are detected online using the nonparametric Bayesian Dirichlet process (DP) mixture model and evidence theory (ET) based on the experimentally acquired sensor data. This sensor data-driven defect detection approach facilitates real-time identification and correction of FFF process drifts with an accuracy and precision approaching 85% (average F-score). In comparison, the F-score from existing approaches, such as probabilistic neural networks (PNN), naïve Bayesian clustering, support vector machines (SVM), and quadratic discriminant analysis (QDA), was in the range of 55–75%.
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December 2015
Research-Article
Online Real-Time Quality Monitoring in Additive Manufacturing Processes Using Heterogeneous Sensors
Prahalad K. Rao,
Prahalad K. Rao
Department of Systems Science and
Industrial Engineering,
State University of New York at Binghamton,
Binghamton, NY 13702
Industrial Engineering,
State University of New York at Binghamton,
Binghamton, NY 13702
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Jia (Peter) Liu,
Jia (Peter) Liu
Grado Department of Industrial and
Systems Engineering,
Virginia Polytechnic Institute and
State University,
Blacksburg, VA 24061
Systems Engineering,
Virginia Polytechnic Institute and
State University,
Blacksburg, VA 24061
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David Roberson,
David Roberson
Grado Department of Industrial and
Systems Engineering,
Virginia Polytechnic Institute and
State University,
Blacksburg, VA 24061
Systems Engineering,
Virginia Polytechnic Institute and
State University,
Blacksburg, VA 24061
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Zhenyu (James) Kong,
Zhenyu (James) Kong
Grado Department of Industrial and
Systems Engineering,
Virginia Polytechnic Institute and
State University,
Blacksburg, VA 24061
e-mail: zkong@vt.edu
Systems Engineering,
Virginia Polytechnic Institute and
State University,
Blacksburg, VA 24061
e-mail: zkong@vt.edu
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Christopher Williams
Christopher Williams
Department of Mechanical Engineering,
Virginia Polytechnic Institute and
State University,
Blacksburg, VA 24061
Virginia Polytechnic Institute and
State University,
Blacksburg, VA 24061
Search for other works by this author on:
Prahalad K. Rao
Department of Systems Science and
Industrial Engineering,
State University of New York at Binghamton,
Binghamton, NY 13702
Industrial Engineering,
State University of New York at Binghamton,
Binghamton, NY 13702
Jia (Peter) Liu
Grado Department of Industrial and
Systems Engineering,
Virginia Polytechnic Institute and
State University,
Blacksburg, VA 24061
Systems Engineering,
Virginia Polytechnic Institute and
State University,
Blacksburg, VA 24061
David Roberson
Grado Department of Industrial and
Systems Engineering,
Virginia Polytechnic Institute and
State University,
Blacksburg, VA 24061
Systems Engineering,
Virginia Polytechnic Institute and
State University,
Blacksburg, VA 24061
Zhenyu (James) Kong
Grado Department of Industrial and
Systems Engineering,
Virginia Polytechnic Institute and
State University,
Blacksburg, VA 24061
e-mail: zkong@vt.edu
Systems Engineering,
Virginia Polytechnic Institute and
State University,
Blacksburg, VA 24061
e-mail: zkong@vt.edu
Christopher Williams
Department of Mechanical Engineering,
Virginia Polytechnic Institute and
State University,
Blacksburg, VA 24061
Virginia Polytechnic Institute and
State University,
Blacksburg, VA 24061
1Corresponding author.
Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received September 17, 2014; final manuscript received February 6, 2015; published online September 9, 2015. Assoc. Editor: Robert Gao.
J. Manuf. Sci. Eng. Dec 2015, 137(6): 061007 (12 pages)
Published Online: September 9, 2015
Article history
Received:
September 17, 2014
Revision Received:
February 6, 2015
Citation
Rao, P. K., Liu, J. (., Roberson, D., Kong, Z. (., and Williams, C. (September 9, 2015). "Online Real-Time Quality Monitoring in Additive Manufacturing Processes Using Heterogeneous Sensors." ASME. J. Manuf. Sci. Eng. December 2015; 137(6): 061007. https://doi.org/10.1115/1.4029823
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