In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning

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dc.contributor.authorGaikwad, Aniruddhaen
dc.contributor.authorChang, Tammyen
dc.contributor.authorGiera, Brianen
dc.contributor.authorWatkins, Nicholasen
dc.contributor.authorMukherjee, Saptarshien
dc.contributor.authorPascall, Andrewen
dc.contributor.authorStobbe, Daviden
dc.contributor.authorRao, Prahaladaen
dc.date.accessioned2022-11-14T17:59:11Zen
dc.date.available2022-11-14T17:59:11Zen
dc.date.issued2022-10en
dc.description.abstractIn droplet-on-demand liquid metal jetting (DoD-LMJ) additive manufacturing, complex physical interactions govern the droplet characteristics, such as size, velocity, and shape. These droplet characteristics, in turn, determine the functional quality of the printed parts. Hence, to ensure repeatable and reliable part quality it is necessary to monitor and control the droplet characteristics. Existing approaches for in-situ monitoring of droplet behavior in DoD-LMJ rely on high-speed imaging sensors. The resulting high volume of droplet images acquired is computationally demanding to analyze and hinders real-time control of the process. To overcome this challenge, the objective of this work is to use time series data acquired from an in-process millimeter-wave sensor for predicting the size, velocity, and shape characteristics of droplets in DoD-LMJ process. As opposed to high-speed imaging, this sensor produces data-efficient time series signatures that allows rapid, real-time process monitoring. We devise machine learning models that use the millimeter-wave sensor data to predict the droplet characteristics. Specifically, we developed multilayer perceptron-based non-linear autoregressive models to predict the size and velocity of droplets. Likewise, a supervised machine learning model was trained to classify the droplet shape using the frequency spectrum information contained in the millimeter-wave sensor signatures. High-speed imaging data served as ground truth for model training and validation. These models captured the droplet characteristics with a statistical fidelity exceeding 90%, and vastly outperformed conventional statistical modeling approaches. Thus, this work achieves a practically viable sensing approach for real-time quality monitoring of the DoD-LMJ process, in lieu of the existing data-intensive image-based techniques.en
dc.description.notesThis work was performed in part under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory (LLNL) under contract DE-AC52-07-NA27344 and supported by the LLNL-LDRD Program under Project Nos. 19-ERD-008 and 18-SI001. The document release number is LLNL-JRNL-826615. Prahalada Rao thanks the National Science Foundation (NSF) and Department of Energy (DoE) for funding his work under awards OIA-1929172, CMMI-1920245, CMMI-1739696, ECCS-2020246, PFI-TT 2044710, CMMI-1752069, CMMI-1719388, and DE-SC0021136. Using sensor data and artificial intelligence to improve part quality in metal additive manufacturing was the major aspect of CMMI-1752069 (Program Officer: Kevin Chou). The use of artificial intelligence in the broader manufacturing context was funded through ECCS-2020246 (Program office: Donald Wunsch). Supplemental funding for CMMI-1752069 was obtained through the NSF INTERN program (Program Officer: Prakash Balan) and CMMI Data Science Activities (Program Officer: Martha Dodson) is greatly appreciated. Both supplements funded Aniruddha Gaikwad's research with LLNL.en
dc.description.sponsorshipU.S. Department of Energy by Lawrence Livermore National Laboratory (LLNL) [DE-AC52-07-NA27344]; LLNL-LDRD Program [19-ERD-008, 18-SI001]; National Science Foundation (NSF); Department of Energy (DoE) [OIA-1929172, CMMI-1920245, CMMI-1739696, ECCS-2020246, PFI-TT 2044710, CMMI-1752069, CMMI-1719388, DE-SC0021136]; Program office: Donald Wunsch [ECCS-2020246]; NSF INTERN program [CMMI-1752069]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1007/s10845-022-01977-2en
dc.identifier.eissn1572-8145en
dc.identifier.issn0956-5515en
dc.identifier.issue7en
dc.identifier.urihttp://hdl.handle.net/10919/112587en
dc.identifier.volume33en
dc.language.isoenen
dc.publisherSpringeren
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectDroplet-on-demand liquid metal jetting (DoD-LMJ)en
dc.subjectIn-process sensing and monitoringen
dc.subjectHigh-speed imagingen
dc.subjectMillimeter-wave sensingen
dc.subjectMachine learningen
dc.titleIn-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learningen
dc.title.serialJournal of Intelligent Manufacturingen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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