Automated layer identification in large area additive manufacturing (LAAM): A comparison of image thresholding and edge detection techniques

dc.creatorWadidie, A.
dc.creatorStuder, G. M.
dc.creatorVillez, K.
dc.date.accessioned2024-03-27T03:18:46Z
dc.date.available2024-03-27T03:18:46Z
dc.date.issued2023
dc.description.abstractOur study aims to develop an automated method for identifying layers on images of 3Dprinted walls from a LAAM printer, as manual identification is subjective and can be timeconsuming. We applied three different image processing methods to identify edges between layers: simple thresholding, Otsu thresholding, and Canny edge detection. Otsu thresholding was found to be the most accurate and required minimal manual intervention. From our study, we propose a new approach by going through essential steps for greater accuracy. This research demonstrates the feasibility of using computer-based methods to automatically identify layers in 3D printing, reducing manual time and effort and improving the strength and quality of 3D-printed parts.
dc.description.departmentMechanical Engineering
dc.identifier.urihttps://hdl.handle.net/2152/124461
dc.identifier.urihttps://doi.org/10.26153/tsw/51069
dc.language.isoen_US
dc.publisherUniversity of Texas at Austin
dc.relation.ispartof2023 International Solid Freeform Fabrication Symposium
dc.rights.restrictionOpen
dc.subjectlarge area additive manufacturing
dc.subject3D printing
dc.subjectthresholding
dc.subjectedge detection
dc.titleAutomated layer identification in large area additive manufacturing (LAAM): A comparison of image thresholding and edge detection techniques
dc.typeConference paper

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