کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5132224 1491516 2017 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Tensor-based ultrasonic data analysis for defect detection in fiber reinforced polymer (FRP) composites
موضوعات مرتبط
مهندسی و علوم پایه شیمی شیمی آنالیزی یا شیمی تجزیه
پیش نمایش صفحه اول مقاله
Tensor-based ultrasonic data analysis for defect detection in fiber reinforced polymer (FRP) composites
چکیده انگلیسی


- A Tucker3-based ultrasonic data analysis method is proposed for defect detection.
- Leverages are used to summarize the defect information from the Tucker3 factors.
- Candidate defective regions can be identified from the second and third modes.
- Defect locations and shapes can be determined by clustering analysis.
- Defect depths are estimated from the peaks in the leverages in the first mode.

Non-destructive testing (NDT) is an important tool for defect detection in composite materials. Compared to other NDT methods, ultrasonic testing (UT) has the principal advantages of high penetrating power and high detection sensitivity. To better identify the locations and depths of defective regions, various ultrasonic signal processing methods have been adopted to enhance defect signals. However, most of the existing methods cannot deal with the entire third-order tensor of UT data in an efficient manner. In order to solve this problem, a tensor-based ultrasonic data analysis method is proposed based on Tucker3 decomposition. After decomposition, the defect information is extracted by a small number of factors, which is further summarized by three leverage vectors. The candidate defective regions are then identified from the leverages in the second and third modes, facilitating the following clustering step for finding the locations and the shapes of defects. Moreover, the defect depths are estimated from the peaks in the leverages in the first mode. The proposed method was applied to detecting defects in fiber reinforced polymer (FRP) composites. The experimental results illustrated the feasibility of the proposed method.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Chemometrics and Intelligent Laboratory Systems - Volume 163, 15 April 2017, Pages 24-30
نویسندگان
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