Sensorless Tool Collision Detection for Multi-axis Machine Tools by Integration of Disturbance Information

Tetsuya Shigematsu, Ryo Koike, Yasuhiro Kakinuma, Tojiro Aoyama, Kouhei Ohnishi

研究成果: Conference article査読

4 被引用数 (Scopus)

抄録

Tool collision must be detected with high response and reliability to reduce damage on machine-tool components. Several researches have proposed tool collision detection methods by monitoring a fluctuation in acceleration or force in collision direction with additional sensors. However, the installation of additional sensors is not desirable due to cost, machine-tool stiffness and failure rate. Moreover, these approaches do not focus on practical use of information in the other directions, although the collision-induced fluctuation can be observed not only in collision direction. This paper presents a tool collision detection method by applying disturbance observer theory and integrating the estimated collision force information in two axes. Furthermore, hotelling's T2 statistic is employed as a multivariate statistical process to the estimated collision force in the two directions. The proposed method successfully detects tool collision with higher response and reliability than that using only information in collision direction.

本文言語English
ページ(範囲)658-663
ページ数6
ジャーナルProcedia CIRP
57
DOI
出版ステータスPublished - 2016
イベント49th CIRP Conference on Manufacturing Systems, CIRP-CMS 2016 - Stuttgart, Germany
継続期間: 2016 5 252016 5 27

ASJC Scopus subject areas

  • 制御およびシステム工学
  • 産業および生産工学

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