Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/60419
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dc.contributor.authorWei, Lai
dc.date.accessioned2014-05-27T04:23:01Z
dc.date.available2014-05-27T04:23:01Z
dc.date.copyright2014en_US
dc.date.issued2014
dc.identifier.urihttp://hdl.handle.net/10356/60419
dc.description.abstractEfficiency in modern manufacturing process is very crucial. Manufactures these days want to have more control over the performance of their machines. This Final Year Project aims to using machine learning techniques to predict the outcome of a certain manufacturing process in order to improve the manufacturing efficiency. This is a joint project together with other SIMTech scientists and staff. The manufacture process studied this project is a coating process of a thin plastic substrate. A coating machine was used with two controllable inputs namely the coating material flow rate and substrate rolling speed. Normally, the thickness of coating material is difficult to control due to the limitation of the machine. In this project, machine learning methods were studied and developed to predict the coating thickness. By using the historical coating thickness data, Matlab programs were developed to predict the coating thickness. Therefore, coating thickness can be controlled and the production efficiency can be improved. Besides, physical models were also developed to predict the coating thickness and give physical explanation at the same time.en_US
dc.format.extent47 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.titleA systematic approach using machine learning and optimization techniques to improve manufacturing process efficiencyen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorEr Meng Jooen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US
dc.contributor.organizationA*STAR SIMTechen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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