Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/160966
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dc.contributor.authorZhang, Hainingen_US
dc.contributor.authorChoi, Joon Philen_US
dc.contributor.authorMoon, Seung Kien_US
dc.contributor.authorNgo, Teck Huien_US
dc.date.accessioned2022-08-10T00:46:22Z-
dc.date.available2022-08-10T00:46:22Z-
dc.date.issued2020-
dc.identifier.citationZhang, H., Choi, J. P., Moon, S. K. & Ngo, T. H. (2020). A multi-objective optimization framework for aerosol jet customized line width printing via small data set and prediction uncertainty. Journal of Materials Processing Technology, 285, 116779-. https://dx.doi.org/10.1016/j.jmatprotec.2020.116779en_US
dc.identifier.issn0924-0136en_US
dc.identifier.urihttps://hdl.handle.net/10356/160966-
dc.description.abstractAerosol jet printing (AJP) is a promising non-contact writing technology to fabricate customized and conformal microelectronics devices on flexible substrates. However, in recent years, the printed line quality is highlighted as a limitation in the applications of AJP technology. According to previous researches, a line printed with high edge roughness and low cross-sectional area will reduce the resistance homogeneity and current carrying capacity, respectively. Despite a high line thickness is beneficial to increase the cross-sectional area, it will be in contradiction with a customized line width under a certain mass flow rate, and may lead to an increase in the line edge roughness. Therefore, it is necessary to minimize the inherent contradictions between different printed line features in a design space. In this research, a multi-objective optimization framework is proposed to optimize the overall printing quality of customized line width. In the proposed framework, Latin hyper sampling is utilized for initial experimental design as it could maximize uniformity in a design space with small dataset. Gaussian process regression (GPR) is then adopted for rapid modeling of the printed line morphology due to its capability of providing prediction uncertainty. Following that, GPR models are driven with an efficient multi-objective genetic algorithm to minimize the inherent contradictions of the AJP process. Thus, the optimal process parameters for customized line width printing can be identified systematically and cost-efficiently in a design space. Experimental results indicate the validity of the proposed framework for customized line width printing. Till now, there are few systematic researches on the optimization of printed line morphology, which is an essential component for AJP. This research attempts to contribute to enriching the body of knowledge on printing process optimization.en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relation.ispartofJournal of Materials Processing Technologyen_US
dc.rights© 2020 Elsevier B.V. All rights reserved.en_US
dc.subjectEngineering::Mechanical engineeringen_US
dc.titleA multi-objective optimization framework for aerosol jet customized line width printing via small data set and prediction uncertaintyen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.identifier.doi10.1016/j.jmatprotec.2020.116779-
dc.identifier.scopus2-s2.0-85086010270-
dc.identifier.volume285en_US
dc.identifier.spage116779en_US
dc.subject.keywordsAerosol Jet Printingen_US
dc.subject.keywordsLine Morphologyen_US
dc.description.acknowledgementThis research work was conducted in the SMRT-NTU Smart Urban Rail Corporate Laboratory with funding support from the National Research Foundation (NRF), SMRT and Nanyang Technological University; under the Corp Lab@University Scheme.en_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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