Vision-based inspection of PCB soldering defects

Zailah W, and Gan, B.Y.J. and Leong, H.Y. and Norsuzlin Mohd Sahar, and Mohammad Tariqul Islam, (2022) Vision-based inspection of PCB soldering defects. Jurnal Kejuruteraan, 34 (5). pp. 807-817. ISSN 0128-0198


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Vision-based inspection of printed circuit board (PCB) soldering defects was studied for preparing feature data and classifying the overall PCB soldering defects on a PCB prototype into different classes. The image data of overall PCB soldering defects on a PCB prototype was developed using an image sensor camera. Image data augmentation was conducted to enhance the dataset volume. Image pre-processing included image resizing, image colour conversion, and image denoising. Watershed-based image segmentation was performed in the image post-processing to segmented images; then, feature extraction was conducted using curvelet transform to prepare image feature data. The feature data as the statistical data include kurtosis, contrast, energy, homogeneity, and variance. These data were analysed, and the percentage difference of mean values of statistical data between image classes was calculated. Kurtosis had the highest percentage difference among the statistical data. In the comparison of the mean values, kurtosis obtained 4.97% difference for the class of good and medium condition; 17.02% difference for the good and bad condition; and 12.08% difference for the bad and medium condition. Through this analysis, kurtosis is considered more reliable data for the machine-learning based classification in this project. The extracted data can be applied in future studies to classify overall solder joint defects on a PCB prototype by artificial neural network in machine learning classification.

Item Type:Article
Keywords:Solder joints defect; Watershed transform; Curvelet transform; Statistical data
Journal:Jurnal Kejuruteraan
ID Code:20582
Deposited By: ms aida -
Deposited On:23 Nov 2022 01:59
Last Modified:23 Nov 2022 02:00

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