Automated inspection of PCB components using a genetic algorithm template-matching approach

Abstract

Automated inspection of surface mount PCB boards is a requirement to assure quality and to reduce manufacturing scrap costs and rework. This paper investigates methodologies for locating and identifying multiple objects in images used for surface mount device inspection. One of the main challenges for surface mount device inspection is component placement inspection. Component placement errors such as missing, misaligned or incorrectly rotated components are a major cause of defects and need to be detected before and after the solder reflow process. This paper focuses on automated object-recognition techniques for locating multiple objects using grey-model fitting for producing a generalised template for a set of components. The work uses the normalised cross correlation (NCC) template-matching approach and examines a method for constraining the search space to reduce computational calculations. The search for template positions has been performed exhaustively and by using a genetic algorithm. Experimental results using a typical PCB image are reported.