بازسازی تصاویر حرارتی و به کارگیری روش خوشه بندی K جهت عیب یابی قطعات پلیمری ساخت افزایشی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 مرکز جوش و تست غیرمخرب دانشگاه تهران، دانشکده مکانیک، پردیس فنی، دانشگاه تهران

2 دانشجوی دکترا، مهندسی مکانیک، پردیس دانشکده های فنی، دانشگاه تهران

3 دانشیار دانشکده مهندسی مکانیک دانشگاه تهران_

4 _Associate Professor, University of Tehran

5 گروه مهندسی مکانیک، دانشکده فنی و مهندسی، دانشگاه اصفهان

10.30494/jndt.2021.274284.1053

چکیده

امروزه قطعات ساخته‌شده به روش تولید افزایشی با سرعت بالایی درحال‌توسعه هستند و کاربردهای فراوانی در صنایع مختلف دارند. ازاین‌رو بهره‌گیری از روش‌های سریع و دقیق برای بررسی سلامت و کیفیت این قطعات اهمیت دوچندانی پیداکرده است. در این پژوهش یک نمونه از جنس PLA ساخته‌شده به روش مدلسازی رسوب ذوب شده به روش ترموگرافی بازرسی شد. پس از بررسی تصاویر اولیه حرارتی بهترین فریم انتخاب گردید. به‌منظور بهبود تصاویر حرارتی اولیه، روش پردازش بازسازی پاسخ حرارتی (TSR) به داده‌های اعمال و بهترین تصاویر مشتقات اول تا سوم این روش تعیین گردید. تصاویر منتخب به‌منظور بررسی اثر روش پردازش در بهبود اختلاف شدت نور میان نواحی معیوب و سالم، به کمک پارامتر نسبت سیگنال به نویز (SNR) مقایسه شدند. بر اساس نتایج به‌دست‌آمده بالاترین مقدار میانگین SNR متعلق به تصویر مشتق اول پردازش، برابر با 15.68، بود. این میزان تقریبا 4 برابر مقدار میانگین SNR برای بهترین فریم اولیه بود. همچنین باهدف تشخیص عیوب به‌صورت خودکار روش دسته‌بندی k-means به تصاویر منتخب اعمال شد. بعد از بررسی تصاویر باینری نهایی مشخص شد که 100% عیوب در تصاویر مشتق اول و دوم TSR قابل‌شناسایی هستند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Thermal signal reconstruction and employment of K clustering method for inspection of additive manufactured polymer parts

نویسندگان [English]

  • pouria meshkizadeh 1
  • Mojtaba Rezaee Hajideh 2
  • Mohammadreza Farahani 3 4
  • Mohammad Heidari-Rarani 5
1 School of mechanical engineering, Tehran university, Tehran, Iran
2 School of Mechanical Engineering, College of Engineering, University of Tehran
3 Associate Professor, School of Mechanical Engineering, College of Engineering, University of Tehran
4 Associate Professor, School of Mechanical Engineering, College of Engineering, University of Tehran
5 Faculty of Engineering, University of Isfahan, Hezar Jarib st, Isfahan
چکیده [English]

Due to the increase in the application of additive manufactured components in the industry, developing fast and accurate methods for defect evaluation of these products has become vitally important. In this study, a PLA sample was inspected by thermography. Several artificial defects varying in size and depth were produced in the specimen. A projector with 2 KW in power was utilized to heat the sample for the 15s. The infrared camera recorded the sample’s temperature during the heating period and 30s after shutting down the source. Afterward, the best frame of raw data was selected. The contrast of defective and sound regions improved with applying the well-known Thermal Signal Reconstruction (TSR) image processing method to enhance the automatic detectability of defects. The contrast enhancement was studied quantitatively via adopting signal to noise ratio (SNR) parameter. According to the acquired results, TSR’s 1st derivative image had the highest average of SNR. This amount was approximately four times higher than that of the best frame of raw data. Ultimately, to identify the defects automatically, k-means clustering was adopted. By comparing the segmented images, it was proved that the adopted process was successful in improving automatic defect detection. While the proportion of detectable defects through segmented image concluded from the best frame of raw data was only 70 percent, the figure for segmented images concluded form 1st and 2nd derivative of TSR was substantially higher at 100 percent.

کلیدواژه‌ها [English]

  • Nondestructive Tests
  • Thermography
  • thermal image processing
  • Automatic defect detection
  • Additive Manufacturing
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