Abstract
Automated Visual Inspection (AVI) systems are nowadays considered an integral part in the assembly of Surface Mounted Devices (SMDs) in the electronics industry. This industry has faced the problem of rapid introduction and retirement of SMD based products with the consequent obsolescence of the inspection systems already in the production line. The general goal of this research is to develop a self-training system for the inspection of SMD components. In this paper we describe the general methodology being followed to develop such a system and in particular in the methodology being used for the first part of the process, i.e., automated feature selection. The objective of the feature selection process is to reduce the computational time and cost of the inspection systems such that the set of features selected minimizes the inspection errors of the systems in the inspection phase.
Original language | English (US) |
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Title of host publication | IIE Annual Conference and Exposition 2005 |
State | Published - 2005 |
Event | IIE Annual Conference and Exposition 2005 - Atlanta, GA, United States Duration: May 14 2005 → May 18 2005 |
Other
Other | IIE Annual Conference and Exposition 2005 |
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Country/Territory | United States |
City | Atlanta, GA |
Period | 5/14/05 → 5/18/05 |
Keywords
- Automated Visual Inspection (AVI)
- Feature Selection
- Quadratic Vector Classifier
- Self-Training Classifier
ASJC Scopus subject areas
- Engineering(all)