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Classification of incorrectly picked components using Convolutional Neural Networks
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
2018 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Printed circuit boards used in most ordinary electrical devices are usually equipped through an assembly line. Pick and place machines as part of those lines require accurate detection of incorrectly picked components, and this is commonly performed via image analysis. The goal of this project is to investigate if we can achieve state-of-the-art performance in an industrial quality assurance task through the application of artificial neural networks. Experiments regarding different network architectures and data modifications are conducted to achieve precise image classification. Although the classification rates do not surpass or equal the rates of the existing vision-based detection system, there remains great potential in the deployment of a machine-learning-based algorithm into pick and place machines.

Abstract [sv]

Tryckta kretskort som används i de flesta vanliga elektroniska produkter är vanligtvis monterade i monteringslinjer. Ytmonteringsmaskinerna i dessa monteringslinjer kräver exakt detektering av felaktigt plockade komponenter, vilket ofta genomförs med hjälp av bildanalys. Målet med detta projekt är att undersöka om vi kan uppnå framstående resultat i en industriell kvalitetssäkringsuppgift genom användandet av artificiella neuronnätverk. Experiment utförs med olika nätverksarkitekturer och datamodifikationer för att uppnå exakt bildklassificering.  Även om klassificeringsgraderna inte uppnår klassificeringsgraderna hos existerande synbaserade detekteringssystem, finns en stor potential för användandet av maskininlärningsbaserade algoritmer i ytmonteringsmaskiner.

Place, publisher, year, edition, pages
2018.
Series
TRITA-EECS-EX ; 2018:316
Keywords [en]
Convolutional Neural Network, Machine Learning, Neural Network, Pick and place machine, Assembly line
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-230732OAI: oai:DiVA.org:kth-230732DiVA, id: diva2:1221560
External cooperation
Mycronic AB
Educational program
Master of Science - Systems, Control and Robotics
Presentation
2018-06-13, 09:30 (English)
Supervisors
Examiners
Available from: 2018-08-24 Created: 2018-06-20 Last updated: 2018-08-24Bibliographically approved

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