Pattern classification of solder joint images using a correlation neural network

“…Reflections of the specular solder joint may appear or disappear even with small changes in viewing direction [5]. Moreover, specular surfaces rarely exhibit smooth shading that matches its shape, which makes it complex to recognise a specular surface from visual information.…”

Section: Illumination Designmentioning

confidence: 99%

“…Automated solder joint inspection requires the extraction of information from the solder joint surface [5]. However, the specularity of the solder joint surface causes difficulties in the automated extraction of relevant information.…”

Section: Introductionmentioning

confidence: 99%

Design and development of automatic visual inspection system for PCB manufacturing

Mar

Yarlagadda

Fookes

2011

Robotics and Computer-Integrated Manufacturing

View full text Add to dashboard Cite

a b s t r a c tInspection of solder joints has been a critical process in the electronic manufacturing industry to reduce manufacturing cost, improve yield, and ensure product quality and reliability. This paper proposes two inspection modules for an automatic solder joint classification system. The ''front-end'' inspection system includes illumination normalisation, localisation and segmentation. The ''back-end'' inspection involves the classification of solder joints using the Log-Gabor filter and classifier fusion. Five different levels of solder quality with respect to the amount of solder paste have been defined. The Log-Gabor filter has been demonstrated to achieve high recognition rates and is resistant to misalignment. This proposed system does not need any special illumination system, and the images are acquired by an ordinary digital camera. This system could contribute to the development of automated non-contact, non-destructive and low cost solder joint quality inspection systems.

show abstract

“…Normalized cross-correlation has long been a common and powerful similarity measure in computer vision, which has been extensively used for applications such as object recognition [1,2], OCR [3,4] and defect inspection [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%