Robust strategies for diagnosing manufacturing defects

Nancy E. Reed, Maria Gini, Paul E. Johnson

Research output: Contribution to journalArticlepeer-review

Abstract

We describe several strategies used by expert troubleshooters performing a manufacturing screening task, the diagnosis of defects on a computer board. These strategies use "inexact models" of the components and connections on the board. A prototype expert system has been implemented that uses the strategies and models. The strategies and models are robust because they are applicable to a wide range of problems, including problems not previously encountered. The system saves useful data acquired during problem solving to assist in future problems. We also describe how the above strategies and models can be used in a sensorbased system that acquires information about the board through a vision camera and other sensing devices. This will further increase the productivity of human troubleshooters.

Original languageEnglish (US)
Pages (from-to)387-406
Number of pages20
JournalApplied Artificial Intelligence
Volume10
Issue number5
DOIs
StatePublished - Oct 1 1996

Bibliographical note

Funding Information:
This work was supported by IBM and the University of Minnesota’ s Microelectronic and Information Sciences Center (MEIS). Thanks to the expert troubleshooters at IBM who allowed us to observe them. Address correspondence to Nancy E. Reed, Department of Computer Science, University of California, 2063 Engineering II, Davis, CA 95616-8562, USA. E-mail: nereed@ucdavis.edu

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