Automated 3D burr detection in cast manufacturing using sparse convolutional neural networks

Ahmed Mohammed (), Johannes Kvam, Ingrid Fjordheim Onstein, Marianne Bakken and Helene Schulerud
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Ahmed Mohammed: SINTEF Digital
Johannes Kvam: SINTEF Digital
Ingrid Fjordheim Onstein: Norwegian University of Science and Technology
Marianne Bakken: SINTEF Digital
Helene Schulerud: SINTEF Digital

Journal of Intelligent Manufacturing, 2023, vol. 34, issue 1, No 14, 303-314

Abstract: Abstract For automating deburring of cast parts, this paper proposes a general method for estimating burr height using 3D vision sensor that is robust to missing data in the scans and sensor noise. Specifically, we present a novel data-driven method that learns features that can be used to align clean CAD models from a workpiece database to the noisy and incomplete geometry of a RGBD scan. Using the learned features with Random sample consensus (RANSAC) for CAD to scan registration, learned features improve registration result as compared to traditional approaches by (translation error ( $$\Delta $$ Δ 18.47 mm) and rotation error( $$\Delta 43 ^\circ $$ Δ 43 ∘ )) and accuracy(35%) respectively. Furthermore, a 3D-vision based automatic burr detection and height estimation technique is presented. The estimated burr heights were verified and compared with measurements from a high resolution industrial CT scanning machine. Together with registration, our burr height estimation approach is able to estimate burr height similar to high resolution CT scans with Z-statistic value ( $$z=0.279$$ z = 0.279 ).

Keywords: Burr detection; Burr height; Deep learning; Convolutional neural network; Registration (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10845-022-02036-6

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