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Finite element simulation study on rail flaw detection using laser induced ultrasonic guided waves

With the need of increased tonnage transportation through heavy haul railways, ensuring safety and reliability is always of the highest priority. Early detection of rail flaws ensures transportation safety and avoids unplanned downtime owing to derailments. Several conventional non-destructive techniques have been proposed for rail flaw detection including eddy current, infrared imaging, and vibration detection, but these are all slow speed applications. Laser ultrasonic guided wave based detection stands out as a potential non-contact high speed detection method. In this technique, the rail surface is subjected to laser pulses which results in propagation of guided waves in the rail. These waves get scattered when they meet cracks or deformities. The rail flaws are then detected by sensing the scattered ultrasound waves using air coupled sensors. These waves also trigger surface rubbing at rail cracks generating heat in the vicinity, which enhances thermal imaging of flaws. This paper presents results of finite element simulations for crack detection using ultrasonic guided waves at different excitation frequencies and sensor positions. These results are used to identify the best suited frequency and sensor position for defect identification. The outcome of this work will lead the way for a high speed rail flaw detection methodology based on laser induced guided wave principles.

Funding

Category 3 - Industry and Other Research Income

History

Editor

Gräbe PJ; Fröhling RD

Start Page

267

End Page

274

Number of Pages

8

Start Date

02/09/2017

Finish Date

06/09/2017

ISBN-13

9780911382662

Location

Cape Town, South Africa

Publisher

International Heavy Haul Association (IHHA)

Place of Publication

Virginia Beach, VA.

Peer Reviewed

Yes

Open Access

No

Author Research Institute

Centre for Railway Engineering

Era Eligible

Yes

Name of Conference

11th International Heavy Haul Association Conference (IHHA 2017)