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The broader integration of AI technologies into smart manufacturing has transformed quality control systems across industries. Examples include automated defect detection in semiconductors, surface inspection in automotive parts, and pattern analysis in textiles.
Within this context, PCB inspection represents a critical application area. Incorporating unsupervised learning-based methods into quality inspection processes not only improves detection accuracy but also enables adaptive quality assurance, contributing to the realization of smart factories where AI-driven inspection systems continuously optimize manufacturing processes through real-time feedback and data-driven decision-making. |
IEEE/ICACT20260190 Slide.08
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