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Investigation on SMT Product Defect Recognition Based on Multi-Source and Multi-Dimensional Data Reconstruction.

Jiantao Chang1, Zixuan Qiao1, Qibin Wang1

  • 1The Key Laboratory of Electronic Equipment Structure Design, Ministry of Education, Xidian University, Xi'an 710071, China.

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|June 24, 2022
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Summary
This summary is machine-generated.

This study introduces a new defect recognition model for surface-mounted technology (SMT) production. The enhanced model achieves 96.97% accuracy, improving SMT quality control and reducing costs.

Keywords:
SMT production qualitydefect recognitionfeature reconstructionsolder paste printing

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Area of Science:

  • Manufacturing Engineering
  • Quality Control
  • Materials Science

Background:

  • Defect recognition in solder paste printing is crucial for surface-mounted technology (SMT) production quality.
  • Current machine inspection methods for SMT defects exhibit low accuracy, necessitating manual rechecks and increasing production costs.

Purpose of the Study:

  • To develop an advanced SMT product defect recognition model using multi-source and multi-dimensional data reconstruction.
  • To enhance the accuracy of defect identification in the solder paste printing process for improved SMT quality control.

Main Methods:

  • Feature engineering techniques including interaction, selection, and conversion were employed to strengthen feature-defect correlations.
  • A novel defect recognition model was constructed based on reconstructed features for the solder paste printing process.
  • The model's performance was validated using a SMT production dataset and compared against existing methodologies.

Main Results:

  • The proposed defect recognition model achieved a high accuracy rate of 96.97%.
  • Comparative analysis demonstrated superior accuracy of the developed model over four other benchmark methods.
  • The findings indicate a significant improvement in defect recognition capabilities for SMT production.

Conclusions:

  • The developed multi-source, multi-dimensional data reconstruction approach offers a robust solution for SMT defect recognition.
  • This new method provides a viable strategy for enhancing defect recognition rates and overall quality control in SMT manufacturing.
  • The study presents a novel approach to address the limitations of current automated inspection systems in SMT production.