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Understanding and detection of process instabilities in wire arc directed energy deposition additive manufacturing using meltpool imaging and machine learning

2025-10Artigo científico Acesso aberto
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dc.contributor.authorRamalho, André
dc.contributor.authorAssad, Anis
dc.contributor.authorBevans, Benjamin
dc.contributor.authorDeschamps, Fernando
dc.contributor.authorSantos, Telmo G.
dc.contributor.authorOliveira, J. P.
dc.contributor.authorRao, Prahalada
dc.contributor.institutionDEMI - Departamento de Engenharia Mecânica e Industrial
dc.contributor.institutionUNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial
dc.contributor.institutionDCM - Departamento de Ciência dos Materiais
dc.contributor.institutionCENIMAT-i3N - Centro de Investigação de Materiais (Lab. Associado I3N)
dc.contributor.pblElsevier
dc.date.accessioned2025-09-03T21:45:26Z
dc.date.available2025-09-03T21:45:26Z
dc.date.issued2025-10
dc.descriptionFunding Information: AR, TGS and JPO acknowledge the Portuguese Fundação para a Ciência e a Tecnologia (FCT - MCTES) for its financial support via the project UIDB/00667/2020 and UIDP/00667/2020 (UNIDEMI). JPO acknowledges the funding by national funds from FCT - Fundação para a Ciência 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. AR acknowledges FCT - MCTES for funding the PhD grant UI/BD/151018/2021. This activity has received funding from the European Institute of Innovation and Technology (EIT) RawMaterials through the 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. Prahalada Rao gratefully acknowledges funding from the following US federal government agencies for nurturing his scholastic research in metal additive manufacturing and smart manufacturing over the last decade through the following awards. National Science Foundation (NSF) via Grant Nos. CMMI-2428305, CMMI-2336449, CMMI-2309483/1752069, OIA-1929172, PFI-TT 2322322/2044710, CMMI-1920245, ECCS-2020246, CMMI-1739696, CMMI-2336449, and CMMI-2428305; US Department of Navy, Naval Surface Warfare Center (NAVAIR, N6833524C0215) and Office of Naval Research (ONR, N00014-21-1-2781); and the National Institute of Standards and Technology (NIST, 70NANB23H029T). Understanding the causal influence of process parameters on part quality and detection of defect formation using in-situ sensing was the major aspect of CMMI-2309483/1752069 (Program Officer: Pranav Soman). The use of machine learning and analytics for process diagnosis in additive manufacturing was funded via ECCS-2020246 (program officer: Richard Nash). Benjamin Bevans was funded through CMMI-2309483/1752069 and PFI-TT 2322322/2044710. Anis Assad and Fernando Deschamps were funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001. The foregoing also funded a visiting student scholarship for Anis Assad to work at Virginia Tech under the supervision of Prahalada Rao. Publisher Copyright: © 2025 The Authors
dc.description.abstractThis work concerns the wire arc directed energy deposition (WA-DED) additive manufacturing process. The objectives were two-fold: (1) observe and understand, through in-operando high-speed meltpool imaging, the causal dynamics of two common WA-DED process instabilities, namely, humping and humping-induced porosity; and (2) leverage the high-speed meltpool imaging data within machine learning algorithms for real-time detection of process instabilities. Humping and humping-induced porosity are leading stochastic causes of poor WA-DED part quality that occur despite extensive optimization of processing conditions. It is therefore essential to understand, detect and control the causal meltpool phenomena linked to these instabilities. Accordingly, we used a high-speed camera to capture the meltpool dynamics of multi-layer depositions of ER90S-G steel parts and meltpool flow behavior related to process instabilities were demarcated and quantified. Next, physically intuitive meltpool morphology signatures were extracted from the imaging data. These signatures were used in a machine learning model trained to autonomously detect process instabilities. This novel process-aware machine learning approach classified onset of instabilities with ∼85 % accuracy (F1-score), outperforming black-box deep learning models (F1-score <66 %). These results pave the way for a physically intuitive process-aware machine learning strategy for monitoring and control of the WA-DED process.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent16
dc.format.extent16053735
dc.identifier.doi10.1016/j.matdes.2025.114598
dc.identifier.issn0264-1275
dc.identifier.otherPURE: 128561133
dc.identifier.otherPURE UUID: 924703f9-5b53-462a-b3ab-f59a28601985
dc.identifier.otherScopus: 105013796554
dc.identifier.otherORCID: /0000-0001-6906-1870/work/191132191
dc.identifier.urihttp://hdl.handle.net/10362/187499
dc.identifier.urlhttps://www.scopus.com/pages/publications/105013796554
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00667%2F2020/PT
dc.relationResearch and Development Unit for Mechanical and Industrial Engineering
dc.relationResearch and Development Unit for Mechanical and Industrial Engineering
dc.relationInstitute of Nanostructures, Nanomodelling and Nanofabrication
dc.relationInstitute of Nanostructures, Nanomodelling and Nanofabrication
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F50025%2F2020/PT
dc.relationinfo:eu-repo/grantAgreement/FCT/Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017%2F2018) - Financiamento Base/UIDB%2F50025%2F2020/PT
dc.relationinfo:eu-repo/grantAgreement/FCT/OE/UI%2FBD%2F151018%2F2021/PT
dc.subjectHumping
dc.subjectMeltpool imaging
dc.subjectPorosity
dc.subjectProcess-aware machine learning
dc.subjectWire arc additive manufacturing (WAAM)
dc.subjectWire arc directed energy deposition
dc.subjectGeneral Materials Science
dc.subjectMechanics of Materials
dc.subjectMechanical Engineering
dc.titleUnderstanding and detection of process instabilities in wire arc directed energy deposition additive manufacturing using meltpool imaging and machine learningen
dc.typejournal article
degois.publication.firstPage1
degois.publication.lastPage16
degois.publication.titleMaterials and Design
degois.publication.volume258
dspace.entity.typePublication
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oaire.awardNumberUI/BD/151018/2021
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oaire.awardTitleResearch and Development Unit for Mechanical and Industrial Engineering
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oaire.awardTitleInstitute of Nanostructures, Nanomodelling and Nanofabrication
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oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00667%2F2020/PT
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oaire.awardURIinfo:eu-repo/grantAgreement/FCT/Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017%2F2018) - Financiamento Base/UIDB%2F50025%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/OE/UI%2FBD%2F151018%2F2021/PT
oaire.fundingStream6817 - DCRRNI ID
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oaire.fundingStream6817 - DCRRNI ID
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oaire.fundingStreamConcurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) - Financiamento Base
oaire.fundingStreamOE
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