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

Department of System Design Engineering

Research output: Contribution to conference › Paper › peer-review

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.

Original language	English
Pages	477-482
Number of pages	6
Publication status	Published - 2017
Event	20th International Symposium on Advances in Abrasive Technology, ISAAT 2017 - Okinawa, Japan Duration: 2017 Dec 3 → 2017 Dec 6

Conference

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

Keywords

Collision avoidance
Convolutional neural network
Description
Feature detection
Matching
Tools identification
Turning-milling machine

ASJC Scopus subject areas

Mechanics of Materials
Industrial and Manufacturing Engineering
Mechanical Engineering
Materials Science(all)

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Cite this

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title = "Identification of cutting tool for avoiding collision in turning-milling machine",

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",

note = "Publisher Copyright: {\textcopyright} ISAAT 2017.All right reserved. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 20th International Symposium on Advances in Abrasive Technology, ISAAT 2017 ; Conference date: 03-12-2017 Through 06-12-2017",

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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.

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KW - Matching

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