Integration of an Ultrasonic Sensor within a Robotic End Effector for Application within Railway Track Flaw Detection

Author:

Cilia Luke1,Griffiths Christian Andrew1ORCID,Rees Andrew2ORCID,Thompson Jennifer1

Affiliation:

1. Department of Mechanical Engineering, Swansea University, Swansea SA1 8EN, UK

2. Wolfson School, Loughborough University, Loughborough LE11 3TU, UK

Abstract

The rail industry is constantly facing challenges related to safety with regard to the detection of surface cracks and internal defects within rail tracks. Significant focus has been placed on developing sensor technologies that would facilitate the detection of flaws that compromise rail safety. In parallel, robot automation has demonstrated significant advancements in the integration of sensor technologies within end effectors. This study investigates the novel integration of an ultrasonic sensor within a robotic platform specifically for the application of detecting surface cracks and internal defects within rail tracks. The performance of the robotic sensor system was assessed on a rail track specimen containing sacrificial surface cracks and internal defects and then compared against a manual detection system. The investigation concludes that the robotic sensor system successfully identified internal defects in the web region of the rail track when utilising a 60° and 70° wedged probe, with a frequency range between 4 MHz and 5 MHz. However, the surface crack investigation proved that the transducer was insensitive to the detection of cracks, possibly due to the inadequate angle of the wedged probe. The overall outcome of the study highlights the potential that robotic sensor systems have in the detection of internal defects and characterises the limitations of surface crack identification to assist in enhancing rail safety.

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference34 articles.

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2. Bombarda, D., Vitetta, G.M., and Ferrante, G. (2021). Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview. Appl. Sci., 11.

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