Engineering driven cause-effect modeling and statistical analysis for multi-operational machining process diagnosis

Jian Liu, Jing Li, Jianjun Shi

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

6 Scopus citations

Abstract

Process fault identification for product quality improvement is a critical issue in both design and manufacturing, especially for multistage manufacturing processes. In this paper, an integrated approach is proposed to develop cause-effect models from engineering knowledge and to conduct associated statistical analysis of the measurement data. First, a cause-effect diagram and predicted symptom vectors (PSV) are formulated to recognize the cause-effect relationship between process variables and product qualities. Then factor analysis and factor rotating technique are employed to extract the symptoms reflected from measurement data. Finally the potential process faults are identified by comparing predicted symptoms and extracted symptoms. A case study is conducted to demonstrate the effectiveness of the proposed methodology.

Original languageEnglish (US)
Title of host publicationTransactions of the North American Manufacturing Research Institute of SME
Pages65-72
Number of pages8
Volume33
Publication statusPublished - 2005
Externally publishedYes
EventNorth American Manufacturing Research Conference, NAMRC 33 - New York, NY, United States
Duration: May 24 2005May 27 2005

Other

OtherNorth American Manufacturing Research Conference, NAMRC 33
CountryUnited States
CityNew York, NY
Period5/24/055/27/05

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Keywords

  • Cause-effect relationship
  • Multistage manufacturing process
  • Predicted symptom vector
  • Root cause diagnosis

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Liu, J., Li, J., & Shi, J. (2005). Engineering driven cause-effect modeling and statistical analysis for multi-operational machining process diagnosis. In Transactions of the North American Manufacturing Research Institute of SME (Vol. 33, pp. 65-72)