2026-09-19T15:59:29Zhttps://dspace.mit.edu/server/oai/request

oai:dspace.mit.edu:1721.1/1521222023-09-14T03:26:40Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

Hardt, David E. Modes, Jane Ellen Massachusetts Institute of Technology. Department of Mechanical Engineering 2023-09-13T18:06:27Z 2023-09-13T18:06:27Z 2023-02 2023-03-01T20:04:07.454Z https://hdl.handle.net/1721.1/152122 Additive manufacturing (AM) allows for the creation of complex geometries that cannot be created with traditional manufacturing methods and is widely used in many industries. With any additive manufacturing process, achieving a successful Flayer is critical to the quality of the final part. Currently, no machine can provide objective evidence of a proper layer setup with in-process monitoring equipment. The strategy of this project was to utilize various sensors in tandem with the camera available within the machine to distinguish between passing and failing layers in a quantifiable manner. This thesis aimed to test the 3D printer’s on-machine camera and several other off the shelf cameras, (Spectral Instruments RVT100, GoPro HERO7 Black, STPCTOU Wireless Digital Microscope, and iPhone 12 camera,), to determine which if any of them were suitable for quantifying a layer setup through powder area coverage. Several tests were performed to look at the camera repeatability across one or several locations by analyzing image intensity values in ImageJ. Another test was performed to determine if there was a linear correlation between layer thickness and image intensity. The cumulative results from all tests indicate that the on-machine camera is the best option of all cameras tested for this application. M.Eng. Massachusetts Institute of Technology In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ Optical In-Process Monitoring Tools for Laser Powder Bed Fusion: Verifying Powder Area Coverage of a Layer Setup 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="a12139ea-3f6f-42f2-99e6-4ae829c6f24c"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>Optical In-Process Monitoring Tools for Laser Powder Bed Fusion: Verifying Powder Area Coverage of a Layer Setup</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2023-02</PublicationDate> <Authors> <Author> <DisplayName>Modes, Jane Ellen</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>https://rightsstatements.org/page/InC-EDU/1.0/</License> <Abstract>Additive manufacturing (AM) allows for the creation of complex geometries that cannot be created with traditional manufacturing methods and is widely used in many industries. With any additive manufacturing process, achieving a successful Flayer is critical to the quality of the final part. Currently, no machine can provide objective evidence of a proper layer setup with in-process monitoring equipment. The strategy of this project was to utilize various sensors in tandem with the camera available within the machine to distinguish between passing and failing layers in a quantifiable manner. This thesis aimed to test the 3D printer’s on-machine camera and several other off the shelf cameras, (Spectral Instruments RVT100, GoPro HERO7 Black, STPCTOU Wireless Digital Microscope, and iPhone 12 camera,), to determine which if any of them were suitable for quantifying a layer setup through powder area coverage. Several tests were performed to look at the camera repeatability across one or several locations by analyzing image intensity values in ImageJ. Another test was performed to determine if there was a linear correlation between layer thickness and image intensity. The cumulative results from all tests indicate that the on-machine camera is the best option of all cameras tested for this application.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>