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oai:dspace.mit.edu:1721.1/1502202023-04-01T04:06:17Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

Anthony, Brian W. Sardet, Maëlle J. Massachusetts Institute of Technology. Department of Mechanical Engineering 2023-03-31T14:40:29Z 2023-03-31T14:40:29Z 2023-02 2023-03-01T20:04:08.153Z https://hdl.handle.net/1721.1/150220 This thesis presents an in-depth investigation on characterization of optical fiber preform core manufacturing and the identification of underlying trends in measured production data. While walking through the different operations involved in the process, we explained the challenges associated with insuring refractive index profile precision and glass purity. Starting with unsupervised learning, process by process, we applied linear and non linear dimensionality reduction algorithms (PCA and t-sne) to features matrices created from time series data and have been able to connect data clusters with context information like machines or month of the year. Then considering the core fabrication process as a whole, we studied the propagation of trends in the data sets up to quality measurements using Dice’s statistic to gauge similarities between samples sets. Finally, we developed some data-driven regression models in order to predict the refractive index measured at the end using data from all processes. As a result, Kernel algorithms performed the best and almost as well on raw statistics from all processes as on encoded information about machine sequences and dates. This supervised approach demonstrated some great potential for the development of prediction tools which could help design the optimized production line. An underlying objective is to support Sterlite Technologies Limited in using data-driven approach applied to process control for its plant in Waluj and Shendra starting by implementing good practices for variables measurement, logging and tracking. M.Eng. Massachusetts Institute of Technology In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ A Data-Driven Approach to Improve Optical Fiber Manufacturing: Focus on Core Deposition Thesis application/pdf Master Master of Engineering in Advanced Manufacturing and Design Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="053c6735-31a8-41c5-9892-8e2172753ef2"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>A Data-Driven Approach to Improve Optical Fiber Manufacturing: Focus on Core Deposition</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2023-02</PublicationDate> <Authors> <Author> <DisplayName>Sardet, Maëlle J.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://rightsstatements.org/page/InC-EDU/1.0/</License> <Abstract>This thesis presents an in-depth investigation on characterization of optical fiber preform core manufacturing and the identification of underlying trends in measured production data. While walking through the different operations involved in the process, we explained the challenges associated with insuring refractive index profile precision and glass purity. Starting with unsupervised learning, process by process, we applied linear and non linear dimensionality reduction algorithms (PCA and t-sne) to features matrices created from time series data and have been able to connect data clusters with context information like machines or month of the year. Then considering the core fabrication process as a whole, we studied the propagation of trends in the data sets up to quality measurements using Dice’s statistic to gauge similarities between samples sets. Finally, we developed some data-driven regression models in order to predict the refractive index measured at the end using data from all processes. As a result, Kernel algorithms performed the best and almost as well on raw statistics from all processes as on encoded information about machine sequences and dates. This supervised approach demonstrated some great potential for the development of prediction tools which could help design the optimized production line. An underlying objective is to support Sterlite Technologies Limited in using data-driven approach applied to process control for its plant in Waluj and Shendra starting by implementing good practices for variables measurement, logging and tracking.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>