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IJAT Vol.5 No.3 pp. 362-368
doi: 10.20965/ijat.2011.p0362
(2011)

Paper:

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A Proposed Ultraprecision Machining Process Monitoring Method Using Causal Network Model of Air Spindle System

Hiroshi Sawano, Ryosuke Kobayashi, Hayato Yoshioka,
and Hidenori Shinno

Precision and Intelligence Laboratory, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama, Kanagawa 226-8503, Japan

Received:
January 14, 2011
Accepted:
February 27, 2011
Published:
May 5, 2011
Creative Commons License
Keywords:
process monitoring, ultraprecision machining, air spindle system, micro machining, causal network
Abstract
Future ultraprecision machining systems require inprocess monitoring and intelligent machining control functions. This paper presents a newly developed machining process monitoring method. The method proposed aims at monitoring the ultraprecision machining process using a causal network model of an air spindle system. The results of actual machining experiments confirm that the proposed method can estimate the dynamic and thermal behaviors at the cutting point during machining. In consequence, the process monitoring method proposed can systematically predict the tool wear, the contact condition between the tool and the workpiece, the abnormal machining conditions, and so on.
Cite this article as:
H. Sawano, R. Kobayashi, H. Yoshioka, and H. Shinno, “A Proposed Ultraprecision Machining Process Monitoring Method Using Causal Network Model of Air Spindle System,” Int. J. Automation Technol., Vol.5 No.3, pp. 362-368, 2011.
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References
  1. [1] T. Masuzawa, “State of the art of micromachining,” CIRP Annals, 49-2, pp. 473-489, 2000.
  2. [2] Y. Takeuchi, Y. Yoneyama, T. Ishida, and T. Kawai, “6-axis control ultraprecision micro grooving on sculptured surfaces with nonrotational cutting tool,” CIRP Annals, 58-1, pp. 53-56, 2009.
  3. [3] H. Yoshioka, H. Hashizume, and H. Shinno, “In-process microsensor for ultraprecision machining,” IEE Proc-Sci. Meas. Technol., 151-2, pp. 121-125, 2004.
  4. [4] H. Yoshioka, M. Hayashi, and H. Shinno, “Status monitoring on ultraprecision machining using micro thermo sensor and AE sensor,” Int. J. of Automation Technology, 3-4, pp. 422-427, 2009.
  5. [5] C. Aliustaoglu, H. M. Ertunc, and H. Ocak, “Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system,” Mechanical Systems and Signal Processing, 23, pp. 539-546, 2009.
  6. [6] N. Ghosh, Y. B. Ravi, A. Patra, S. Mukhopadhyay, S. Paul, A. R. Mohanty, and A. B. Chattopadhyay, “Estimation of tool wear during CNC milling using neural network-based sensor fusion,” Mechanical Systems and Signal Processing, 21, pp. 466-479, 2007.
  7. [7] H. Shinno and H. Hashizume “Structured method for identifying success factors in new product development of machine tools,” CIRP Annals, 51-1, pp. 281-284, 2002.

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