کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8129865 | 1523180 | 2018 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
High-frequency ultrasonic methods for determining corrosion layer thickness of hollow metallic components
ترجمه فارسی عنوان
روشهای اولتراسونیک فرکانس بالا برای تعیین ضخامت لایه خوردگی اجزای توخالی فلزی
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
فیزیک و نجوم
آکوستیک و فرا صوت
چکیده انگلیسی
Corrosion in internal cavity is one of the most common problems occurs in many hollow metallic components, such as pipes containing corrosive fluids and high temperature turbines in aircraft. It is highly demanded to non-destructively detect the corrosion inside hollow components and determine the corrosion extent from the external side. In this work, we present two high-frequency ultrasonic non-destructive testing (NDT) technologies, including piezoelectric pulse-echo and laser-ultrasonic methods, for detecting corrosion of Ni superalloy from the opposite side. The determination of corrosion layer thickness below â¼100â¯Âµm has been demonstrated by both methods, in comparison with X-CT and SEM. With electron microscopic examination, it is found that with multilayer corrosion structure formed over a prolonged corrosion time, the ultrasonic NDT methods can only reliably reveal outer corrosion layer thickness because of the resulting acoustic contrast among the multiple layers due to their respective different mechanical parameters. A time-frequency signal analysis algorithm is employed to effectively enhance the high frequency ultrasonic signal contrast for the piezoelectric pulse-echo method. Finally, a blind test on a Ni superalloy turbine blade with internal corrosion is conducted with the high frequency piezoelectric pulser-receiver method.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Ultrasonics - Volume 89, September 2018, Pages 166-172
Journal: Ultrasonics - Volume 89, September 2018, Pages 166-172
نویسندگان
Hongwei Liu, Lei Zhang, Hong Fei Liu, Shuting Chen, Shihua Wang, Zheng Zheng Wong, Kui Yao,