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A reliable solder joint inspection method based on a light-weight point cloud network and modulated loss

Neurocomputing, ISSN: 0925-2312, Vol: 488, Page: 315-327
2022
  • 10
    Citations
  • 0
    Usage
  • 12
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    10
    • Citation Indexes
      10
  • Captures
    12

Article Description

High-speed and high-reliability automated quality inspection is widely demanded in the electronics industry. Recently, deep learning theory combined with many non-destructive technologies shows superior performance for inspecting failed soldering. In this paper, a reliable point cloud learning based method is implemented for high-speed solder joint shape defect detection. First, a light-weight neural network named Solder PointNet (SPNet) is proposed. With local group attention mechanisms, SPNet avoids adverse effects of outliers in the scanning point cloud and finds favorable critical point feature adaptively. Then, a modulated loss is designed to ensure reliable low false detection rate. By adjusting the weights of cross-entropy loss, the predictive distribution of defective samples is guided to a smaller range, thereby setting an appropriate threshold to efficiently separate the predictive distribution. Furthermore, the proposed method is further trained and evaluated on the self built solder joint dataset DHU-PAD1000 of point cloud data. Comparison experiments are carried out on the built dataset, and the results show that SPNet achieves higher accuracy with fewer parameters, considerable speed advantage, and more reliable automated detection.

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