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Proceedings Article

Information integration and diagnosis analysis of equipment status and production quality for machining process

[+] Author Affiliations
Tao Zan, Min Wang, Jianzhong Hu

Beijing Univ. of Technology (China)

Proc. SPIE 7997, Fourth International Seminar on Modern Cutting and Measurement Engineering, 79973G (May 26, 2011); doi:10.1117/12.888551
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From Conference Volume 7997

  • Fourth International Seminar on Modern Cutting and Measurement Engineering
  • Jiezhi Xin; Lianqing Zhu; Zhongyu Wang
  • Beijing, China | December 10, 2010

abstract

Machining status monitoring technique by multi-sensors can acquire and analyze the machining process information to implement abnormity diagnosis and fault warning. Statistical quality control technique is normally used to distinguish abnormal fluctuations from normal fluctuations through statistical method. In this paper by comparing the advantages and disadvantages of the two methods, the necessity and feasibility of integration and fusion is introduced. Then an approach that integrates multi-sensors status monitoring and statistical process control based on artificial intelligent technique, internet technique and database technique is brought forward. Based on virtual instrument technique the author developed the machining quality assurance system - MoniSysOnline, which has been used to monitoring the grinding machining process. By analyzing the quality data and AE signal information of wheel dressing process the reason of machining quality fluctuation has been obtained. The experiment result indicates that the approach is suitable for the status monitoring and analyzing of machining process.

© (2010) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

Tao Zan ; Min Wang and Jianzhong Hu
"Information integration and diagnosis analysis of equipment status and production quality for machining process", Proc. SPIE 7997, Fourth International Seminar on Modern Cutting and Measurement Engineering, 79973G (May 26, 2011); doi:10.1117/12.888551; http://dx.doi.org/10.1117/12.888551


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