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

Achmad Pratama Rifai, Hideki Aoyama

Research output: Contribution to conferencePaperpeer-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 languageEnglish
Pages477-482
Number of pages6
Publication statusPublished - 2017
Event20th International Symposium on Advances in Abrasive Technology, ISAAT 2017 - Okinawa, Japan
Duration: 2017 Dec 32017 Dec 6

Conference

Conference20th International Symposium on Advances in Abrasive Technology, ISAAT 2017
CountryJapan
CityOkinawa
Period17/12/317/12/6

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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