Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorAng, Ryan Wei Haoen_US
dc.identifier.citationAng, R. W. H. (2023). Anomaly detection in aerosol jet printing via computer vision & machine learning. Final Year Project (FYP), Nanyang Technological University, Singapore.
dc.description.abstractThe Aerosol Jet Printing (AJP) technique is a relatively new contactless direct write method that is being developed for the purpose of producing fine features on a diverse selection of surfaces. The technique was originally designed to make electronic circuits, but it has subsequently been tested for many uses, which include active and passive electrical components. While it is a great technique, AJP does have its limitations. For instance, there is still no concrete in-situ monitoring system in place for AJP so that anomalies occurring during the printing process can be detected early. Hence, this paper aims to develop, and more importantly, fine-tune, an in-situ monitoring algorithm that will be implemented into the Aerosol Jet Printing system with the help of an Object Detection Model, which hinges upon the principles of Deep Learning (DL), and Computer Vision (CV). This model was trained on a dataset comprising of six different classes, evaluated against several metrics, and finally, fined-tuned based on its hyperparameters to attain optimal performance.en_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Aeronautical engineeringen_US
dc.titleAnomaly detection in aerosol jet printing via computer vision & machine learningen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorYeong Wai Yeeen_US
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.description.degreeBachelor of Engineering (Aerospace Engineering)en_US
item.fulltextWith Fulltext-
Appears in Collections:MAE Student Reports (FYP/IA/PA/PI)
Files in This Item:
File Description SizeFormat 
Ryan_Ang-AJP-FYP-Final Report.pdf
  Restricted Access
3.31 MBAdobe PDFView/Open

Page view(s)

Updated on Jun 19, 2024


Updated on Jun 19, 2024

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.