CAD/CAM-based force controller using a neural network-based effective stiffness estimator

Fusaomi Nagata, Takanori Mizobuchi, Tetsuo Hase, Zenku Haga, Keigo Watanabe, Maki K. Habib

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

In industries manufacturing metallic molds, various NC machine tools are used. We have already proposed a desktop NC machine tool with compliance control capability to automatically cope with the finishing process of LED lens molds. The NC machine tool has the ability to control the polishing force acting between an abrasive tool and a work piece. The force control method is called impedance model force control. The most effective gain is the desired damping of the impedance model. Ideally, the desired damping is calculated from the critical damping condition after considering the effective stiffness in the force control system. However, there is a problem in that the effective stiffness of the NC machine tool has undesirable nonlinearity. The nonlinearity has a bad influence on the force control stability. In this article, a fine tuning method of the desired damping is considered using neural networks. The neural networks acquire the nonlinearity of effective stiffness. The promise is evaluated through an experiment.

Original languageEnglish
Pages (from-to)101-105
Number of pages5
JournalArtificial Life and Robotics
Volume15
Issue number1
DOIs
Publication statusPublished - 2010

Keywords

  • Force control
  • NC machine tool
  • Neural network
  • Nonlinear effective stiffness

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'CAD/CAM-based force controller using a neural network-based effective stiffness estimator'. Together they form a unique fingerprint.

Cite this