TY - JOUR
T1 - Effect of contaminations on the acoustic emissions during wire and arc additive manufacturing of 316L stainless steel
AU - Ramalho, André
AU - Santos, Telmo Gomes
AU - Bevans, Ben
AU - Smoqi, Ziyad
AU - Rao, Prahalad
AU - Oliveira, João Pedro
N1 - Funding Information:
info:eu-repo/grantAgreement/FCT/OE/UI%2FBD%2F151018%2F2021/PT#
AR acknowledges Funda??o para a Ci?ncia e a Tecnologia (FCT-MCTES) for funding the Ph.D. Grant UI/BD/151018/2021. AR, TGS and JPO acknowledge Funda??o para a Ci?ncia e a Tecnologia (FCT-MCTES) for its financial support via the project UID/00667/2020 (UNIDEMI). PR acknowledges funding from the Department of Energy (DOE), Office of Science, under Grant number DE-SC0021136, and the National Science Foundation (NSF) [Grant numbers CMMI-1719388, CMMI-1920245, CMMI-1739696, CMMI-1752069, PFI-TT 2044710, ECCS 2020246] for funding his research program. This work espousing the concept of online process monitoring in WAAM was funded through the foregoing DOE Grant (Program Officer: Timothy Fitzsimmons), which supported the doctoral graduate work of Mr. Benjamin Bevans.
Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/3
Y1 - 2022/3
N2 - Additive Manufacturing (AM) processes allow the creation of complex parts with near net shapes. Wire and arc additive manufacturing (WAAM) is an AM process that can produce large metallic components with low material waste and high production rates. Typically, WAAM enables over 10-times the volumetric deposition rates of powder-based AM processes. However, the high depositions rates of WAAM require high heat input to melt the large volume of material, which in turn results in potential flaws such as pores, cracks, distortion, loss of mechanical properties and low dimensional accuracy. Hence, for practical implementation of the WAAM process in an industrial environment it is necessary to ensure flaw-free production. Accordingly, to guarantee the production-level scalability of WAAM it is fundamental to monitor and detect flaw formation during the process. The objective of this work is to characterize the effects of different contaminations on the acoustic spectrum of WAAM and lay the foundations for a microphone-based acoustic sensing approach for monitoring the quality of WAAM-fabricated parts. To realize this objective, WAAM parts were processed with deliberately introduced flaws, such as material contamination, and the acoustic signals were analyzed using the time and frequency domain techniques, namely, Power Spectral Density, and Short Time Fourier Transform. The signatures obtained were used to pinpoint the location of flaw formation. The results obtained in this study show that the effects of contamination in WAAM can be identified through the analysis of the acoustic spectrum of the process.
AB - Additive Manufacturing (AM) processes allow the creation of complex parts with near net shapes. Wire and arc additive manufacturing (WAAM) is an AM process that can produce large metallic components with low material waste and high production rates. Typically, WAAM enables over 10-times the volumetric deposition rates of powder-based AM processes. However, the high depositions rates of WAAM require high heat input to melt the large volume of material, which in turn results in potential flaws such as pores, cracks, distortion, loss of mechanical properties and low dimensional accuracy. Hence, for practical implementation of the WAAM process in an industrial environment it is necessary to ensure flaw-free production. Accordingly, to guarantee the production-level scalability of WAAM it is fundamental to monitor and detect flaw formation during the process. The objective of this work is to characterize the effects of different contaminations on the acoustic spectrum of WAAM and lay the foundations for a microphone-based acoustic sensing approach for monitoring the quality of WAAM-fabricated parts. To realize this objective, WAAM parts were processed with deliberately introduced flaws, such as material contamination, and the acoustic signals were analyzed using the time and frequency domain techniques, namely, Power Spectral Density, and Short Time Fourier Transform. The signatures obtained were used to pinpoint the location of flaw formation. The results obtained in this study show that the effects of contamination in WAAM can be identified through the analysis of the acoustic spectrum of the process.
KW - Acoustic spectrum
KW - Additive manufacturing
KW - Contamination
KW - Fast Fourier Transform
KW - Wire and arc additive manufacturing
UR - http://www.scopus.com/inward/record.url?scp=85122244014&partnerID=8YFLogxK
U2 - 10.1016/j.addma.2021.102585
DO - 10.1016/j.addma.2021.102585
M3 - Article
AN - SCOPUS:85122244014
SN - 2214-8604
VL - 51
JO - Additive Manufacturing
JF - Additive Manufacturing
M1 - 102585
ER -