Abstract
The electronics assembly industry has faced the problem of rapid introduction and retirement of electronic products. Therefore, a system is required that automatically and significantly shortens the time to develop inspection algorithms for new components. The general goal of this research is to develop a self-training classifier for the inspection of SMD components. During the training of the classifier, feature selection is necessary to reduce the computational cost. In particular, this paper explores the use of linear regression as a way to measure the behavior of the features in order to expedite the feature selection process.
| Original language | English (US) |
|---|---|
| Title of host publication | IIE Annual Conference and Exhibition 2004 |
| Pages | 1421-1426 |
| Number of pages | 6 |
| State | Published - 2004 |
| Event | IIE Annual Conference and Exhibition 2004 - Houston, TX, United States Duration: May 15 2004 → May 19 2004 |
Other
| Other | IIE Annual Conference and Exhibition 2004 |
|---|---|
| Country/Territory | United States |
| City | Houston, TX |
| Period | 5/15/04 → 5/19/04 |
Keywords
- Automated visual inspection (AVI)
- Feature selection
- Linear regression
- Statistical process control (SPC)
- Vector classifier
ASJC Scopus subject areas
- Engineering(all)