Identification of cutting tool for avoiding collision in turning-milling machine

Achmad Pratama Rifai; Hideki Aoyama

Identification of cutting tool for avoiding collision in turning-milling machine

Achmad Pratama Rifai, Hideki Aoyama

システムデザイン工学科

研究成果: Paper › 査読

抄録

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.

本文言語	English
ページ	477-482
ページ数	6
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

Conference	20th International Symposium on Advances in Abrasive Technology, ISAAT 2017
国/地域	Japan
City	Okinawa
Period	17/12/3 → 17/12/6

ASJC Scopus subject areas

材料力学
産業および生産工学
機械工学
材料科学（全般）

他のファイルとリンク

引用スタイル

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abstract = "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.",

keywords = "Collision avoidance, Convolutional neural network, Description, Feature detection, Matching, Tools identification, Turning-milling machine",

author = "Rifai, {Achmad Pratama} and Hideki Aoyama",

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AU - Aoyama, Hideki

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N2 - 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.

AB - 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.

KW - Collision avoidance

KW - Convolutional neural network

KW - Description

KW - Feature detection

KW - Matching

KW - Tools identification

KW - Turning-milling machine

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T2 - 20th International Symposium on Advances in Abrasive Technology, ISAAT 2017

Y2 - 3 December 2017 through 6 December 2017

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Identification of cutting tool for avoiding collision in turning-milling machine

抄録

Conference

ASJC Scopus subject areas

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