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Training an artificial neural network for an effective PCB defect detection

Author

Listed:
  • Blanka Bártová
  • Vladislav Bína

Abstract

The printed circuit boards (PCBs) are crucial components of most electronic devices. In the last decades, the PCBs' manufacturing process was significantly improved, mainly by surface mounted technology (SMT) and automatic optical inspection (AOI) implementation. The real data as an output from the AOI device used for our analysis have been composed in a real manufacturing company. The currently used AOI solution achieves an accuracy of 95.82%. The goal of our study was to train an artificial neural network (ANN) to detect the defect PCBs with the highest possible accuracy. Different approaches have been used for ANN training, such as the experimental approach, regression, and Taguchi method. The resulted PCA-ANN model combines principal components analysis (PCA) method for data dimensionality reduction and ANN for low quality products detection. Our proposed model increases the AOI accuracy rate by 3.95%.

Suggested Citation

  • Blanka Bártová & Vladislav Bína, 2025. "Training an artificial neural network for an effective PCB defect detection," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 17(2), pages 200-216.
    • Handle: RePEc:ids:ijdmmm:v:17:y:2025:i:2:p:200-216
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