An Automated Feature Selection Method for Visual Inspection Systems

Automated visual inspection (AVI) systems these days are considered essential 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 assembly lines. The constant introduction of new products has caused AVI systems to become rapidly obsolete. The general goal of this research centers on developing self-training AVI systems for the inspection of SMD components. The premise is that these systems would be less prone to obsolescence. In this paper, the authors describe the methodology being used for automatically selecting the features to inspect new components. In particular, this paper explores the use of multivariate stepwise discriminant analysis techniques, such as Wilks' Lambda, in order to automate the feature selection process. All of these techniques are applied to a case study of the inspection of SMD components. Note to Practitioners—In this paper, we present a methodology that would allow the automation of the tedious task of exploring and selecting features to develop algorithms for automated visual inspection systems. In particular, the proposed method selects a subset of features among all of the known features. The chosen subset seeks to minimize inspection errors while keeping the algorithmic development time to a minimum. This is particularly useful for adapting pre-existing systems to inspect new components, especially when the characteristics of the new components are similar to those of components already inspected by the inspection system. We applied this methodology to a case of study of the inspection of surface-mounted devices.