Turning-milling machine contains a large number of tools, where wrong disposition of tools possesses collision risks. Operator mistakes and lack of proper communication feedback of the machine technology may lead to error that harms the machines. The aim of this study is developing fast, precise, and reliable automatic machining tool recognition system in turning-milling machines. The approach consists of two steps, predicting the class of the tool using convolutional neural network, and identifying the exact type of the tools by performing feature detection, description, and matching.
|出版ステータス||Published - 2017|
|イベント||20th International Symposium on Advances in Abrasive Technology, ISAAT 2017 - Okinawa, Japan|
継続期間: 2017 12月 3 → 2017 12月 6
|Conference||20th International Symposium on Advances in Abrasive Technology, ISAAT 2017|
|Period||17/12/3 → 17/12/6|
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