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Title: Machine-vision-based identification of broken inserts in edge profile milling heads
Authors: Fernandez-Robles, Laura
Azzopardi, George
Alegre, Enrique
Petkov, Nicolai
Keywords: Computer vision
Computer vision -- Evaluation
Milling cutters
Issue Date: 2017
Publisher: Elsevier
Citation: Fernandez-Robles, L., Azzopardi, G., Alegre, E., & Petkov, N. (2017). Machine-vision-based identification of broken inserts in edge profile milling heads. Robotics and Computer-Integrated Manufacturing, 44, 276-283.
Abstract: This paper presents a reliable machine vision system to automatically detect inserts and determine if they are broken. Unlike the machining operations studied in the literature, we are dealing with edge milling head tools for aggressive machining of thick plates (up to 12 centimetres) in a single pass. The studied cutting head tool is characterised by its relatively high number of inserts (up to 30) which makes the localisation of inserts a key aspect. The identification of broken inserts is critical for a proper tool monitoring system. In the method that we propose, we first localise the screws of the inserts and then we determine the expected position and orientation of the cutting edge by applying some geometrical operations. We compute the deviations from the expected cutting edge to the real edge of the inserts to determine if an insert is broken. We evaluated the proposed method on a new dataset that we acquired and made public. The obtained result (a harmonic mean of precision and recall 91.43%) shows that the machine vision system that we present is effective and suitable for the identification of broken inserts in machining head tools and ready to be installed in an on-line system.
Description: We thank the company TECOI for providing us an edge profile milling head tool and the inserts to create our dataset and evaluate the proposed system.
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