شناسایی عیب در اتصالات چسبی با استفاده از روش انتشار امواج فراصوت مبتنی بر هوش مصنوعی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجو،دانشکده مهندسی مکانیک،دانشگاه علم و صنعت ایران

2 گروه مهندسی مکانیک، دانشگاه علم و صنعت ایران

3 مهندسی مکانیک، دانشکده مهندسی مکانیک، دانشگاه علم و صنعت ایران

چکیده

وجود عیب در اتصالات چسبی یک مسئله مهم در ساخت سازه‌های فضایی می‌باشد. در این مقاله با استفاده از امواج لمب، ویژگی‌های مناسب جهت شناسایی اندازه و موقعیت عیوب اتصالات چسبی به‌دست‌آمده‌ است. با استفاده از شبیه‌سازی المان محدود به بررسی اثر عیب بر انتشار امواج لمب پرداخته شده است. شبیه‌سازی برای سه ضخامت متفاوت چسب، سه سایز متفاوت عیب دایره‌ای در ۹ موقعیت مختلف صورت‌گرفته است و تأثیر هر یک از آنها بر موج عبوری از اتصال بررسی شده است. سیگنال‌های به‌دست‌آمده از اتصالات معیوب با سیگنال حاصل از اتصال سالم مقایسه گردیده و ناحیه موردنظر جهت تحلیل‌های بعدی از کل سیگنال دریافتی جدا شد. تفکیک مناسب و صحیح عیوب نیازمند یافتن مشخصه‌هایی مناسب برای آن است به همین جهت 34 ویژگی جهت ایجاد تمایز و تفکیک عیوب بررسی گردید. در ادامه با فراهم آمدن پایه‌های ایجاد الگوهایی مناسب برای تفکیک عیوب، از شبکه عصبی استفاده شد. درصد تشخیص صحیح شبکه عصبی برای تفکیک ضخامت چسب 93/8 درصد، برای تفکیک مساحت عیوب از منظر اندازه ۱۰۰ درصد و برای تفکیک موقعیت عیب در دو محور افقی و عمودی به ترتیب 96/1 و 95/1 درصد به دست آمد. نتایج به‌دست‌آمده نشان‌دهنده کارایی روش تکامل فاصله بهبودیافته و ویژگی‌های انتخاب شده جهت تفکیک عیوب این‌گونه از اتصالات است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Use of Artificial Intelligence to Identify Adhesive Joints Defects by Using Ultrasonic

نویسندگان [English]

  • Mahshad Rastegarmoghaddam 1
  • majid rajabi 2
  • seyed davoud nikkhouy tanha 3
1 Student, Mechanical Engineering, IUST
2 Mechanical Engineering, IUST
3 Mechanical Engineering, IUST
چکیده [English]

Defects in adhesive joints are an important issue in the construction of space structures. In this paper, using lamp waves, suitable properties have been obtained to identify the size and position of the defects of the adhesive joints. Using finite element simulations, the effect of the defect on the propagation of the lamp waves has been investigated. Simulations have been performed for three different adhesive thicknesses, three different sizes of circular defects in 9 different positions, and the effect of each of them on the wave passing through the joint has been investigated. The signals obtained from the faulty connections were compared with the signal obtained from the healthy connection and the desired area was isolated from the total received signal for further analysis. The proper and correct separation of defects requires finding suitable characteristics for it. Therefore, 34 features were examined to differentiate and separate defects. Then, the neural network was used to provide the basis for creating appropriate patterns for the separation of defects. The percentage of correct detection of neural network for adhesive thickness separation was 93.8%, for defect area separation in terms of size 100% and for defect position separation in X and Y axes were 96.1 and 95.1%, respectively. The obtained results show the efficiency of the improved distance evolution method and the features selected to distinguish the defects of such connections.

کلیدواژه‌ها [English]

  • Non-destructive evaluation
  • Limb wave
  • Adhesive bonding
  • Status monitoring
  • Neural network
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