0
Research Papers

Variation Source Identification in Manufacturing Processes Based on Relational Measurements of Key Product Characteristics

[+] Author and Article Information
Jean-Philippe Loose

Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706; and Warwick Digital Laboratory, University of Warwick, Coventry, CV4 7AL, United Kingdomjploose@wisc.edu

Shiyu Zhou

Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706; and Warwick Digital Laboratory, University of Warwick, Coventry, CV4 7AL, United Kingdomszhou@engr.wisc.edu

Dariusz Ceglarek

Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706; and Warwick Digital Laboratory, University of Warwick, Coventry, CV4 7AL, United KingdomD.J.Ceglarek@warwick.ac.uk

J. Manuf. Sci. Eng 130(3), 031007 (May 06, 2008) (11 pages) doi:10.1115/1.2844591 History: Received October 06, 2006; Revised January 02, 2008; Published May 06, 2008

Variation source identification for manufacturing processes is critical for product dimensional quality improvement, and various techniques have been developed in recent years. Most existing variation source identification techniques are based on a linear fault-quality model, in which the relationships between process faults and product dimensional quality measurements are linear. In practice, many dimensional measurements are actually nonlinearly related to the process faults: For example, relational dimension measurements such as the relative distance between features are used to monitor composite tolerances. This paper presents a variation source identification methodology in the presence of these relational dimension measurements. In the proposed methodology, the joint probability density of the measurements is determined as a function of the process parameters; then, series of statistical comparisons are performed to differentiate and identify the variation source. A case study is also presented to illustrate the effectiveness of the methodology.

FIGURES IN THIS ARTICLE
<>
Copyright © 2008 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Figure 1

Variation source in an assembly operation

Grahic Jump Location
Figure 2

Engineering drawing for a hinge illustrating relational quality characteristics

Grahic Jump Location
Figure 3

Illustration of the inefficiencies of PC as patterns for variation source identification

Grahic Jump Location
Figure 4

Distribution of the angle between the first PC and the x axis

Grahic Jump Location
Figure 5

Steps for the variation source identification including relational measurements

Grahic Jump Location
Figure 6

Partition of the space for the example in Sec. 2 when u1 is the variation source

Grahic Jump Location
Figure 7

Plots of the pdf, given different scenarios

Grahic Jump Location
Figure 8

Three step simplified hood assembly process

Grahic Jump Location
Figure 9

Process parameters and measurements on the part

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In