دوره 22، شماره 4 - ( مجله مهندسی برق و الکترونیک ایران - جلد 22 شماره 4 1404 )
جلد 22 شماره 4 صفحات 56-44 |
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Hosseini H, Mavaddati S. Application and Evaluation of Deep Learning Models for Defect Detection in Printed Circuit Boards. Journal of Iranian Association of Electrical and Electronics Engineers 2025; 22 (4) :44-56
URL: http://jiaeee.com/article-1-1765-fa.html
URL: http://jiaeee.com/article-1-1765-fa.html
حسینی حامی، مودّتی سمیرا. بکارگیری و ارزیابی مدلهای یادگیری عمیق به منظور تشخیص نقصها در بردهای مدار چاپی. نشریه مهندسی برق و الکترونیک ایران. 1404; 22 (4) :44-56
دانشکده مهندسی و فناوری- دانشگاه مازندران
چکیده: (607 مشاهده)
نقصها در بردهای مدار چاپی میتوانند منجر به اختلالات جدی در عملکرد دستگاههای الکترونیکی شوند و شناسایی دقیق و سریع آنها برای تضمین کیفیت تولید، امری ضروری است. در این مقاله، کارایی شش مدل یادگیری عمیق شامل Inception-v3، VGG16، رزنت18، رزنت50، رزنت101 و یولوv5 در تشخیص خودکار نقصهای بردهای مدار چاپی بررسی شده است. این نقصها که میتوانند باعث اختلالات جدی در عملکرد دستگاههای الکترونیکی شوند، نیازمند شناسایی سریع و دقیق برای تضمین کیفیت تولید هستند. هر یک از مدلها با استفاده از مجموعه دادهای متشکل از تصاویر نقصهای مختلف بردهای مدار چاپی آموزش دیده و با معیارهای دقت، حساسیت، اختصاصیت و سرعت پردازش ارزیابی شدهاند. نتایج نشان میدهد که مدل یولوv5 با دقت بالاتر و زمان پردازش کمتر، بهترین عملکرد را در تشخیص انواع نقصهای سطحی و ساختاری ارائه میدهد. این پژوهش تأکید میکند که انتخاب معماری مناسب شبکه عصبی عمیق میتواند تأثیر قابل توجهی بر بهبود دقت و کارایی فرآیند بازرسی بردهای مدار چاپی داشته باشد و جایگزینی مؤثر برای روشهای سنتی باشد.
فهرست منابع
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