Monitoring and flaw detection during wire-based directed energy deposition using in-situ acoustic sensing and wavelet graph signal analysis

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dc.contributor.authorBevans, Benjaminen
dc.contributor.authorRamalho, Andreen
dc.contributor.authorSmoqi, Ziyaden
dc.contributor.authorGaikwad, Aniruddhaen
dc.contributor.authorSantos, Telmo G.en
dc.contributor.authorRao, Prahaladen
dc.contributor.authorOliveira, J. P.en
dc.date.accessioned2023-04-13T19:35:09Zen
dc.date.available2023-04-13T19:35:09Zen
dc.date.issued2023-01en
dc.description.abstractThe goal of this work is to detect flaw formation in the wire-based directed energy deposition (W-DED) process using in-situ sensor data. The W-DED studied in this work is analogous to metal inert gas electric arc welding. The adoption of W-DED in industry is limited because the process is susceptible to stochastic and environmental disturbances that cause instabilities in the electric arc, eventually leading to flaw for-mation, such as porosity and suboptimal geometric integrity. Moreover, due to the large size of W-DED parts, it is difficult to detect flaws post-process using non-destructive techniques, such as X-ray com-puted tomography. Accordingly, the objective of this work is to detect flaw formation in W-DED parts using data acquired from an acoustic (sound) sensor installed near the electric arc. To realize this objec-tive, we develop and apply a novel wavelet integrated graph theory approach. The approach extracts a single feature called graph Laplacian Fiedler number from the noise-contaminated acoustic sensor data, which is subsequently tracked in a statistical control chart. Using this approach, the onset of various types of flaws are detected with a false alarm rate less-than 2%. This work demonstrates the potential of using advanced data analytics for in-situ monitoring of W-DED.(c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).en
dc.description.notesAndre Ramalho acknowledges Fundacao para a Ciencia e a Tecnologia (FCT-MCTES) for funding the Ph.D. Grant UI/BD/151018/2021. Andre Ramalho, Telmo G. Santos and J.P. Oliveira acknowledge Fundacao para a Ciencia e a Tecnologia (FCT-MCTES) for its financial support via the project UID/00667/2020 (UNIDEMI). J. P. Oliveira acknowledges funding by national funds from FCT-Fundacao para a Ciencia e a Tecnologia, I.P., in the scope of the projects LA/P/0037/2020, UIDP/50025/2020 and UIDB/50025/2020 of the Associate Laboratory Institute of Nanostructures, Nanomodelling and Nanofabrication - i3N. This activity has received funding from the European Institute of Innovation and Technology (EIT) - Project Smart WAAM: Microstructural Engineering and Integrated Non-Destructive Testing. This body of the European Union receives support from the European Union's Horizon 2020 research and innovation program.<br />Prahalada Rao 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 partially supported the doctoral graduate work of Mr. Benjamin Bevans at University of Nebraska-Lincoln Benjamin, Aniruddha, and Ziyad Smoqi were further supported by the NSF grants CMMI 1752069 (CAREER) and ECCS 2020246. Detecting flaw formation in metal AM using in-situ sensing and graph theory-based algorithms was a major component of CMMI 1752069 (program office: Kevin Chou). Developing machine learning alogirthms for advanced man-ufacturing applications was the goal of ECCS 2020246 (Program officer: Donald Wunsch). The XCT work was performed at the Nebraska Nanoscale Facility: National Nanotechnology Coordinated Infrastructure under award no. ECCS: 2025298, and with support from the Nebraska Research Initiative through the Nebraska Center for Materials and Nanoscience and the Nanoengineering Research Core Facility at the University of Nebraska-Lincoln. The acquisition of the XCT scanner at University of Nebraska was funded through CMMI 1920245 (Program officer: Wendy Crone).en
dc.description.sponsorshipFundacao para a Ciencia e a Tecnologia (FCT-MCTES) [UI/BD/151018/2021, UID/00667/2020]; FCT-Fundacao para a Ciencia e a Tecnologia, I.P. [LA/P/0037/2020, UIDP/50025/2020, UIDB/50025/2020]; European Institute of Innovation and Technology (EIT) - Project Smart WAAM: Microstructural Engineering and Integrated Non-Destructive Testing; European Union; Department of Energy (DOE), Office of Science [DE-SC0021136]; National Science Foundation (NSF) [CMMI-1719388, CMMI-1920245, CMMI-1739696, CMMI-1752069, PFI-TT 2044710, ECCS 2020246]; DOE; NSF [ECCS 2020246, CMMI 1752069]; [CMMI 1920245]; [ECCS: 2025298]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1016/j.matdes.2022.111480en
dc.identifier.eissn1873-4197en
dc.identifier.other111480en
dc.identifier.urihttp://hdl.handle.net/10919/114502en
dc.identifier.volume225en
dc.language.isoenen
dc.publisherElsevieren
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectWire-based directed energy depositionen
dc.subjectProcess flaw monitoringen
dc.subjectAcoustic sensoren
dc.subjectWavelet filteringen
dc.subjectGraph theoryen
dc.titleMonitoring and flaw detection during wire-based directed energy deposition using in-situ acoustic sensing and wavelet graph signal analysisen
dc.title.serialMaterials & Designen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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