A PHM Approach to Additive Manufacturing Equipment Health Monitoring, Fault Diagnosis, and Quality Control

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Jae Yoon David He Brandon Van Hecke

Abstract

Fabrication of three-dimensional (3D) objects through direct deposition of functional materials using 3D printing equipment is called additive manufacturing (AM). Benefits of AM include producing goods quickly and on-demand, with greater customization and complexity and less material waste. While the use of AM has been growing, a number of challenges continue to impede its more widespread adoption, particularly in the areas of non-destructive evaluation/non- destructive testing (NDE/NDT) techniques for AM equipment health monitoring and measurement. In this paper, a prognostics and health management (PHM) approach to AM equipment health monitoring, fault diagnosis and quality control is presented and illustrated with a case study. The presented PHM approach is developed using two types of NDE/NDT sensors: acoustic emission (AE) sensor and piezoelectric strain sensor. A seeded driving belt fault on a fused filament fabrication desktop 3D printer is used to validate the feasibility of the PHM approach in the case study. The case study results have shown the effectiveness of the presented method for AM equipment fault diagnosis and quality control.

How to Cite

Yoon, J. ., He, D. ., & Van Hecke, B. . (2014). A PHM Approach to Additive Manufacturing Equipment Health Monitoring, Fault Diagnosis, and Quality Control. Annual Conference of the PHM Society, 6(1). https://doi.org/10.36001/phmconf.2014.v6i1.2338
Abstract 39565 | PDF Downloads 7182

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Keywords

fault detection, acoustic emission sensors, Additive manufacturing equipment, 3D printer, PE strain sensor

References
Antoni, J. (2007). Fast computation of the kurtogram for the detection of transient faults, Mechanical Systems and Signal Processing, Vol. 32, No. 1, pp. 108 - 124.

Banaszak, D. (2001). Comparison of piezoelectric strain sensors with strain gages, Proceedings of the Annual Meeting of the American Statistical Association, August 5 – 9, Atlanta, GA.

Bechhoefer, E., Qu, Y., Zhu, J., & He, D. (2013). Signal processing technique to improve an acoustic emission sensors, Proceedings of the Annual Conference of the Prognostics and
Health Management Society 2013, October 14 – 17, New Orleans, LA.

Energetics Incorporated. (2013). Workshop summary report: measurement science roadmap for metal-based additive manufacturing, National Institute for Standards and Technology (NIST), U.S. Department of Commerce, May 13.

Feng, Z. & Zuo, M. J. (2013). Fault diagnosis of planetary gearboxes via torsional vibration signal analysis, Mechanical Systems and Signal Processing, Vol. 36, No.2, pp. 401 - 421.

Fessenden, R. A. (1913). Electric Signaling Apparatus, US Patent 1050441A, January 14.

Lee, C. K. & O’Sullivan, T. (1991). Piezoelectric strain rate gages,” Journal of the Acoustical Society of America, Vol. 90, No.2, pp.945 - 953.

Kiddy, J. S., Samuel, P. D., Lewicki, D. G., LaBerge, K. E., Ehinger, R. T., & Fetty, J. (2011). Fiber optic strain sensor for planetary gear diagnostics, NASA Technical Report: NASA/TM-2011-217123, NASA Glenn Research Center, Cleveland, OH.

MakerBot Industries, LLC. (2014). Replicator2 Desktop 3D Printer User Manual, Brooklyn, NY.

Mathews, J. R. (1983). Acoustic Emission, Gordon and Breach Science Publishers Inc., New York, NY, USA.

Office of the Press Secretary. (2013). Remarks by the President in the State of the Union Address, February 12, 2013. http://www.whitehouse.gov/the-press- office/2013/02/12/remarks-president-state-union- address

Scheer, C., Reimche, W., & Bach, F.W. (2007). Early fault detection at gear units by acoustic emission and wavelet analysis, Journal of Acoustic Emission, Vol. 25, No. 1, pp. 331 – 340.

Sirohi, J. & Chopra, I. (2000). Fundamental understanding of piezoelectric strain sensors, Proceedings of Journal of Intelligent Material Systems and Structures, Vol. 11, No. 4, pp. 246 - 257.

Tandon, N. & Mata, S. (1999). Detection of defects in gears by acoustic emission measurements, Journal of Acoustic Emission, Vol. 17, No. 1-2, pp. 23 - 27.

Tandon, N. & Nakra, B. C. (2000). Comparison of vibration and acoustic measurement techniques for the condition monitoring of rolling element bearings, Tribology International, Vol. 25, No. 3, pp. 205 - 212, 1992.

Teager, H. M., & Teager S. M. (1992). Evidence for nonlinear sound production mechanisms in the vocal tract, , Speech Production and Speech Modeling Symposium, Time Frequency and Time-Scale Analysis, edited by Hardcastle, W. J. and Marchal, A., Springer, Amsterdam, Netherlands.

Qu, Y., He, D., Bechhoefer, E., & Zhu, J. (2013). A new acoustic emission sensor based gear fault detection approach, International Journal of Prognostics and Health Management, Vol. 4, Special Issue on Wind Turbine PHM, pp. 1 - 14.

Qu, Y., He, D., Yoon, J., VanHecke, B., Bechhoefer, E., & Zhu, J. (2014). Gearbox tooth cut fault diagnostics using acoustic emission and vibration sensors - a comparative study, Sensors, Vol. 14, No. 1, pp. 1372 - 1393.

Yoshioka, T., & Fujiwara, T. (1984). Application of acoustic emission technique to detection of rolling bearing failure, American Society of Mechanical Engineers, Vol. 14, No. 1, pp. 55 - 76.
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