scholarly journals Detection of Transverse Defects in Rails Using Noncontact Laser Ultrasound

Proceedings ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 43
Author(s):  
Hajar Benzeroual ◽  
Abdellatif Khamlichi ◽  
Alia Zakriti
Keyword(s):  
Defect Detection ◽  
Defect Size ◽  
Ultrasound Wave ◽  
Laser Ultrasound ◽  
Laser Vibrometry ◽  
Transverse Fissure ◽  
Railway Infrastructure ◽  
Laser Sources ◽  
Transverse Defect ◽  
Ablative Laser

Rail inspections are required and used to ensure safety and preserve the availability of railway infrastructure. According to the statistics published by railroad administrations worldwide, the transverse fissure appearing in railhead is the principal cause of rail accidents. These particular defects are initiated inside the railhead. Detection of these cracks has always been challenging because a defect signature remains mostly small until the defect size reaches a significant value. The present work deals with the theoretical analysis of an integrated contact-less system for rail diagnosis, which is based on ultrasounds. The generation of these waves was performed through non-ablative laser sources. Rotational laser vibrometry was used to achieve the reception of the echoes. Detection of flaws in the rail was monitored by considering special ultrasound wave signal based indicators. Finite element modeling of the rail system was performed, and transverse defect detection of the rail was analyzed.

Defect Detection of Flip Chip Solder Bumps Through Local Temporal Coherence Analysis of Laser Ultrasound Signals

2007 ◽  
Author(s):  
Jin Yang ◽  
Charles Ume
Keyword(s):  
Defect Detection ◽  
Flip Chip ◽  
Solder Bump ◽  
Temporal Coherence ◽  
Coherence Analysis ◽  
Laser Ultrasound ◽  
Surface Mount ◽  
Flip Chips ◽  
Solder Bumps ◽  
Ultrasound Signals

Microelectronics packaging technology has evolved from through-hole and bulk configuration to surface-mount and small-profile ones. In surface mount packaging, such as flip chips, chip scale packages (CSP), and ball grid arrays (BGA), chips/packages are attached to the substrates or printed wiring boards (PWB) using solder bump interconnections. Solder bumps, which are hidden between the device and the substrate/board, are no longer visible for inspection. A novel solder bump inspection system has been developed using laser ultrasound and interferometric techniques. This system has been successfully applied to detect solder bump defects including missing, misaligned, open, and cracked solder bumps in flip chips, and chip scale packages. This system uses a pulsed Nd:YAG laser to induce ultrasound in the thermoelastic regime and the transient out-of-plane displacement response on the device surface is measured using the interferometric technique. In this paper, local temporal coherence (LTC) analysis of laser ultrasound signals is presented and compared to previous signal processing methods, including Error Ratio and Correlation Coefficient. The results show that local temporal coherence analysis increases measurement sensitivity for inspecting solder bumps in packaged electronic devices. Laser ultrasound inspection results are also compared with X-ray and C-mode Scanning Acoustic Microscopy (CSAM) results. In particular, this paper discusses defect detection for a 6.35mm×6.35mm×0.6mm PB18 flip chip and a flip chip (SiMAF) with 24 lead-free solder bumps. These two flip chip specimens are both non-underfilled.

scholarly journals Research on the Influence of Noise to Laser Ultrasound SAFT for Lead Defect Detection

2020 ◽  
Vol 1622 ◽  
pp. 012015
Author(s):  
Ting Yang ◽  
Wen He ◽  
Jianzong He ◽  
Haohui He ◽  
Jinjing Yuan ◽  
...  
Keyword(s):  
Defect Detection ◽  
Laser Ultrasound ◽  
Influence Of Noise

Ultrasound wave excitation in thermal NDE for defect detection

2018 ◽  
Vol 100 ◽  
pp. 153-165 ◽  
Author(s):  
Qiuye Yu ◽  
Omar Obeidat ◽  
Xiaoyan Han
Keyword(s):  
Defect Detection ◽  
Ultrasound Wave ◽  
Wave Excitation

Bag of tricks for fabric defect detection based on Cascade R-CNN

2020 ◽  
pp. 004051752095522
Author(s):  
Feng Li ◽  
Feng Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Defect Detection ◽  
State Of The Art ◽  
Detection Algorithm ◽  
Input Image ◽  
Defect Size ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Single Input

In this paper, a bag of tricks is proposed to improve the precision of fabric defect detection. Although the general state-of-the-art convolutional neural network detection algorithm can achieve a better detection effect, in fact, the detection precision still has enough room to improve on fabric defect detection. Therefore, we propose three tricks to further improve the precision. Firstly, we use multiscale training, which scales the single input image into a number of images of different resolutions for training, so as to be able to adapt to the box distribution of different scales. Secondly, we use the dimension clusters method. By observing the distribution of the width and the height of the defect size in the fabric dataset, we find that the distribution of the defect size in the dataset is extremely unbalanced and the size span is large. We believe that the training results of the default prior boxes setting might not be optimal, so we conduct dimensional clustering for the width and height of the defect size of the dataset, so as to make the network model easier to learn. Thirdly, we use soft non-maximum suppression instead of traditional non-maximum suppression to avoid the situation that the same kinds of defect category in the dataset are overlapped and eliminated as repeated detection. With this bag of tricks, we effectively improve the precision of fabric defect detection by 8.9% mAP on the basis of the baseline of state-of-the-art convolutional neural network detection algorithm.

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