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

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Wadidie, A.
Studer, G. M.
Villez, K.

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University of Texas at Austin


Our 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.


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