Impedance model force control using neural networks for a desktop NC machine tool

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In manufacturing industries of metallics 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 an ability to control the polishing force acting between an abrasive tool and workpiece. The force control method is called impedance model force control. The most important gain is the desired damping of the impedance model. Ideally, the desired damping is calculated from the critical damping condition in consideration of the effective stiffness in force control system. However, one of the serious problems is that the effective stiffness of the NC machine tool has undesirable nonlinearity. The nonlinearity gives bad influences to the force control stability. In this paper, a fine tuning method of the desired damping is considered by using neural networks. The neural networks acquire the nonlinearity of effective stiffness. It has been observed that the desired damping generated from the learned neural networks allows the NC machine tool to achieve a stable finishing result.

Original languageEnglish
Title of host publicationIEEE International Symposium on Industrial Electronics
Pages1428-1433
Number of pages6
DOIs
Publication statusPublished - 2009
Externally publishedYes
EventIEEE International Symposium on Industrial Electronics, IEEE ISIE 2009 - Seoul, Korea, Republic of
Duration: Jul 5 2009Jul 8 2009

Other

OtherIEEE International Symposium on Industrial Electronics, IEEE ISIE 2009
CountryKorea, Republic of
CitySeoul
Period7/5/097/8/09

    Fingerprint

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering

Cite this

Nagata, F., Mizobuchi, T., Tani, S., Hase, T., Haga, Z., Watanabe, K., & Habib, M. K. (2009). Impedance model force control using neural networks for a desktop NC machine tool. In IEEE International Symposium on Industrial Electronics (pp. 1428-1433). [5219916] https://doi.org/10.1109/ISIE.2009.5219916