Robotics & Machine Learning Daily News2024,Issue(Feb.16) :50-50.DOI:10.3390/app14031164

Swansea University Researcher Provides New Insights into Robotics (Integration of an Ultrasonic Sensor within a Robotic End Effector for Application within Railway Track Flaw Detection)

Robotics & Machine Learning Daily News2024,Issue(Feb.16) :50-50.DOI:10.3390/app14031164

Swansea University Researcher Provides New Insights into Robotics (Integration of an Ultrasonic Sensor within a Robotic End Effector for Application within Railway Track Flaw Detection)

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Abstract

A new study on robotics is now available. According to news reporting originating from Swansea, United Kingdom, by NewsRx correspondents, research stated, "The rail industry is constantly facing challenges related to safety with regard to the detection of surface cracks and internal defects within rail tracks." The news correspondents obtained a quote from the research from Swansea University: "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."

Key words

Swansea University/Swansea/United Kingdom/Europe/Emerging Technologies/Machine Learning/Robotics/Robots/Technology

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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参考文献量34
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