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The development of a micro-pattern manufacturing method using rotating active tools with compensation of estimated errors and an LMS algorithm

Author

Listed:
  • Soo-Bong Cho

    (Korea Institute of Machinery and Materials
    Korea University of Science and Technology(UST))

  • Seung-Kook Ro

    (Korea Institute of Machinery and Materials)

  • Byung-Sub Kim

    (Korea Institute of Machinery and Materials)

  • Sung-Cheul Lee

    (Korea Institute of Machinery and Materials)

  • Jong-Kweon Park

    (Korea Institute of Machinery and Materials
    Korea University of Science and Technology(UST))

Abstract

In this paper, techniques for machining and micro-structuring dimples and grooves on the interior of cylinders using an active rotating tool are discussed. Microscopic dimples and grooves patterned on the inner surface of a cylinder act as lubrication and reduce friction. The active rotating tool presented here is equipped with a gap sensor that can measure the distance between the tool, workpiece, and machining tip so that micron-scale dimples and grooves can be patterned and connected to piezoelectric actuators. Electronic control and power connections are made to the external controller via a slip ring. Accurate measurements of the distance between the tool and workpiece were used to increase the lubrication effect by machining patterns with uniform size and depth. It is difficult to accurately measure errors in cylinders of various shapes using a single gap sensor; thus, we employed two gap sensors to ensure accurate assessment of cylinder shape, and a least mean square algorithm was implemented to compensate for the measured runout errors, which were tracked and compensated using the gap sensor. The method presented here reduces errors on the inner face of a cylinder, and produces a uniform pattern.

Suggested Citation

  • Soo-Bong Cho & Seung-Kook Ro & Byung-Sub Kim & Sung-Cheul Lee & Jong-Kweon Park, 2021. "The development of a micro-pattern manufacturing method using rotating active tools with compensation of estimated errors and an LMS algorithm," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 51-59, January.
  • Handle: RePEc:spr:joinma:v:32:y:2021:i:1:d:10.1007_s10845-020-01558-1
    DOI: 10.1007/s10845-020-01558-1
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    References listed on IDEAS

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    1. Emel Kuram & Babur Ozcelik, 2016. "Micro-milling performance of AISI 304 stainless steel using Taguchi method and fuzzy logic modelling," Journal of Intelligent Manufacturing, Springer, vol. 27(4), pages 817-830, August.
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