{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/147574"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/147574","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Correlating Displacement Sensors and In-Situ Optical Imaging for the Layer Management in a Laser Powder Bed Fusion Process","abstract":"Additive manufacturing (AM) allows for the creation of complex geometries that cannot be created with traditional manufacturing methods. The strategy of this project is to utilize various sensors in tandem with the camera available within the machine to manage the pertinent powder layer. This study was carried out by a group of four students in collaboration with the partner company. For this study, the on-machine camera has been used for in-process monitoring purposes. This involves capturing the data in the form of images and post-processing them. Two different methods - Mean Intensity and Machine Learning based - were explored. Gauge Repeatability and Reproducibility (GR&R) studies were conducted for the mean intensity-based method and the results showed that the process is repeatable across different setups. The laser triangulation sensor was used to correlate with the camera images. The second method, (Convolutional Neural Network (CNN) based machine learning model), classified the images with 94% accuracy. Both of these methods were deployed on the machine by creating a user-friendly interface.","abstract_html":"Additive manufacturing (AM) allows for the creation of complex geometries that cannot be created with traditional manufacturing methods. The strategy of this project is to utilize various sensors in tandem with the camera available within the machine to manage the pertinent powder layer. This study was carried out by a group of four students in collaboration with the partner company. For this study, the on-machine camera has been used for in-process monitoring purposes. This involves capturing the data in the form of images and post-processing them. Two different methods - Mean Intensity and Machine Learning based - were explored. Gauge Repeatability and Reproducibility (GR&R) studies were conducted for the mean intensity-based method and the results showed that the process is repeatable across different setups. The laser triangulation sensor was used to correlate with the camera images. The second method, (Convolutional Neural Network (CNN) based machine learning model), classified the images with 94% accuracy. Both of these methods were deployed on the machine by creating a user-friendly interface.","abstract_has_math":false,"creators":["Sabarad, Satvik Irappa"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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Both of these methods were deployed on the machine by creating a user-friendly interface."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Correlating Displacement Sensors and In-Situ Optical Imaging for the Layer Management in a Laser Powder Bed Fusion Process"]}]}],"canonical_facts":{"dc:contributor.advisor":["Hardt, David E."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Mechanical Engineering"],"dc:creator":["Sabarad, Satvik Irappa"],"dc:date.accessioned":["2023-01-19T19:59:36Z"],"dc:date.available":["2023-01-19T19:59:36Z"],"dc:date.issued":["2022-09"],"dc:description.abstract":["Additive manufacturing (AM) allows for the creation of complex geometries that cannot be created with traditional manufacturing methods. The strategy of this project is to utilize various sensors in tandem with the camera available within the machine to manage the pertinent powder layer. This study was carried out by a group of four students in collaboration with the partner company. For this study, the on-machine camera has been used for in-process monitoring purposes. This involves capturing the data in the form of images and post-processing them. Two different methods - Mean Intensity and Machine Learning based - were explored. Gauge Repeatability and Reproducibility (GR&R) studies were conducted for the mean intensity-based method and the results showed that the process is repeatable across different setups. The laser triangulation sensor was used to correlate with the camera images. The second method, (Convolutional Neural Network (CNN) based machine learning model), classified the images with 94% accuracy. 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