شناسایی عیوب ظاهری جوش با استفاده از بینایی ماشین براساس یادگیری عمیق

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکده مهندسی عمران، دانشگاه تربیت دبیر شهید رجایی

2 دانشکده مهندسی عمران، دانشگاه تربیت دبیر شهید رجایی.

3 دانشگاه آزاد تهران غرب

چکیده

یکی از کنترلهای کیفیت جوش، کنترل عیوب ظاهری جوش مانند ترک سطحی، جرقه و پاشش، سر رفتن جوش بر روی فلز و ذوب ناقص است. در حال حاضر بر اساس ضوابط آیین‌نامه‌ها، کیفیت ظاهری جوش توسط یک بازرس به صورت چشمی (تست چشمی) کنترل می‌شود. میزان دقت کار در این روش به میزان مهارت شخص بازرس بستگی دارد. عدم استفاده از تجهیزات و فناوری باعث می‌شود تا خطای شناسایی عیوب ظاهری بالا باشد. در این تحقیق، روشی پیشنهاد می‌گردد تا به کمک تصاویر حاصل از جوش و استفاده از بینایی ماشین بر اساس یادگیری عمیق بتوان با دقت و سرعت مناسب عیوب ظاهری جوش را شناسایی کرد. در یادگیری عمیق از شبکه کونولوشنال برای استخراج ویژگی از تصویر استفاده می‌شود. برای اطمینان از دقت روش پیشنهادی، تصاویر جدیدی از جوش معیوب که قبلاً وضعیت آنها توسط بازرسان مجرب تعیین شده بود انتخاب گردید و وضعیت سلامت آنها به کمک ماشین مورد ارزیابی قرار گرفت. نتایج نشان می‌دهد روش پیشنهادی می‌تواند با دقت قابل قبول (بالای 85 درصد)، عیوب ظاهری جوش را شناسایی کند. همچنین نتایج نشان می‌دهد، با استفاده از روش پیشنهادی، عیوب ظاهری جوش در مقایسه با روش سنتی با سرعت بیشتری مورد ارزیابی قرار می‌گیرد.

کلیدواژه‌ها


عنوان مقاله [English]

Identification of Apparent Welding Defects Using Computer Vision Based On Deep Learning

نویسندگان [English]

  • Musa Mahmoudi 1
  • soroush ghaderi 2
  • faezeh mahmoudi 3
1 Department of Structure and Earthquake Engineering, Shahid Rajaee Teacher Training University
2 Department of Structure and Earthquake Engineering, Shahid Rajaee Teacher Training University
3 Department of Artificial intelligence, Azad University, Tehran Gharb Branch.
چکیده [English]

One of the welding controls in health monitoring of structures is to visually control the appearance of welding defects (cracks, Spatter, Overlap, Lack of Fusion). Currently, according to regulations, the appearance quality of welding is controlled by an inspector visually. The accuracy of work in this method depends on the skill level of the inspector. Non using of equipment and technology leads to a high error in identifying visual defects. In this research, a method is proposed to be able to more accurately identify the appearance of welding defects with the help of imaging using machine vision based on deep learning. Convolutional network is used for deep learning to extract features from the image. The results show that the proposed method can identify welding defects with an acceptable accuracy (over 85%). Also, the results show that by using the proposed method, welding defects are evaluated more quickly compared to the traditional method.

کلیدواژه‌ها [English]

  • appearance welding defects
  • Convolutional Neural Network
  • image processing
  • deep learning
  • computer vision
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