Abstract
Laser metal deposition (LMD) is an advanced additive manufacturing (AM) process used to build or repair metal parts layer by layer for a range of different applications. Any presence of deposition defects in the part produced causes change in the mechanical properties and might cause failure to the part. Corrective remedies to fix these defects will increase the machining time and costs. In this work, a novel defects monitoring system was proposed to detect and classify defects in real time using an acoustic emission (AE) sensor and an unsupervised pattern recognition analysis (K-means clustering) in conjunction with a principal component analysis (PCA). A time domain and frequency domain relevant descriptors were used in the classification process to improve the characterization of the defects sources. The methodology was found to be efficient in distinguishing two types of signals that represent two kinds of defects, which are cracks and porosities. A cluster analysis of AE data is achieved and the resulting clusters correlated with the defects sources during laser metal deposition. It was found that cracks and pores that occur during LMD can be detected using an AE sensor. Pores produce acoustic emission events with high energy, shorter decay time, and less amplitude when compared to cracks. Specifically, the signal energy is a crucial feature in identifying the AE defect source mechanisms. The frequency is not significant; it has a little contribution to the classification solution.
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References
Wang L, Felicelli SD, Craig JE (2009) Experimental and numerical study of the LENS rapid fabrication process. ASME J Manuf Sci Eng 131(4):041019–041019-8. doi:10.1115/1.3173952
V. Weerasinghe, W. Steen (1983) Laser cladding by powder injection. In: Proc. 1st Int. Conf. Lasers Manuf., p 125–132
Weerasinghe V, Steen W (1987) Laser cladding with blown powder. Met Constr 19:581–585
Sears J (1999) Direct laser powder deposition—‘State of the Art’, No. KAPL-P-000311; K99089, Knolls At. Power Lab., Nis. NY
McLean M (1997) Laser direct casting high nickel alloy components. Adv Powder Metall Part Mater 3:21
Mazumder J, Choi J, Nagarathnam J, Koch K, Hetzner D (1997) The direct metal deposition of H13 tool steel for 3D components. JOM 49:55–60
Lewis G, Nemec R, Milewski J, Thoma D (1994) Directed light fabrication, No. LAUR-94-2845; CONF-9410189-2, Los Alamos Natl. Lab., NM, USA
Milewski J, Lewis G, Thoma D (1998) Directed light fabrication of a solid metal hemisphere using 5-axis powder deposition. J Mater Process Technol 75:165–172
Wu X, Liang J, Mei J, Mitchell C, Goodwin PS, Voice W (2004) Microstructures of laser-deposited Ti-6Al-4V. Mater Des 25:137–144
Arcella F, Froes F (2000) Producing titanium aerospace components from powder using laser forming. JOM 52:28–30
J. Fessler, R. Merz, Laser deposition of metals for shape deposition manufacturing, In: Proc Solid Free Fabr Symp, Univ Texas, Austin, 1996, pp. 117–124.
Keicher DM, Miller WD (1998) LENS moves beyond RP to direct fabrication. Met Powder Rep 53:26–28
Griffith M, Schlienger M, Harwell L (1998) Thermal behavior in the LENS process, No. SAND-98-1850C; CONF-980826-, Sandia Natl. Labs, Albuquerque, NM, USA
Xue L, Islam M (1998) Free-form laser consolidation for producing functional metallic components. Laser Inst. Am. Laser Mater Process 84
Xue L, Islam M (2000) Free-form laser consolidation for producing metallurgically sound and functional components. J Laser Appl 12:160–165
Ma Z, Sun G, Liu D, Xing X (2016) Dissipativity analysis for discrete-time fuzzy neural networks with leakage and time-varying delays. Neurocomputing 175 :579–584Part A
Liao Z, Axinte DA (2016) On monitoring chip formation, penetration depth and cutting malfunctions in bone micro-drilling via acoustic emission. J Mater Process Technol 229:82–93
Gaja H, Liou F (2016) Automatic detection of depth of cut during end milling operation using acoustic emission sensor. Int J Adv Manuf Technol
Gutkin R, Green CJ, Vangrattanachai S, Pinho ST, Robinson P, Curtis PT (2011) On acoustic emission for failure investigation in CFRP: pattern recognition and peak frequency analyses. Mech Syst Signal Process 25(4):1393–1407
Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65
Barua S et al. ((2014)) Vision-based defect detection in laser metal deposition process. Rapid Prototyp J 20(1):77–85
Lu SP et al. (2003) Acoustic emission monitoring and microscopic investigation of cracks in ERCuNi cladding. J Mater Sci Technol 19(3):201–205
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Gaja, H., Liou, F. Defects monitoring of laser metal deposition using acoustic emission sensor. Int J Adv Manuf Technol 90, 561–574 (2017). https://doi.org/10.1007/s00170-016-9366-x
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DOI: https://doi.org/10.1007/s00170-016-9366-x