Objective
The European rail infrastructure employs high manganese, high strength steel components at safety critical locations such as crossings. Restricted access to these assets limits the range of NDT techniques available for in-service inspection to those capable of inspection from the top surface. Conventional ultrasonic inspection of high manganese steel is ineffective due to the inherent coarse grain structure and the highly anisotropic and non-homogeneous material properties; low material penetration, high scattering and beam divergence contribute to low signal-to-noise ratio of reflected signals. Conventional dual phased array techniques (with transmitter/receiver pair) have been applied in conjunction with processing features such as spatial averaging and dynamic depth focussing. However, this approach is highly inefficient and therefore inappropriate for practical deployment.
The SAFTInspector proposal will enable efficient in-service inspection of manganese rail assets by applying Phased Array UT full matrix capture in combination with a novel post processing technique called Synthetic Aperture Focusing Technique (SAFT). This technique increases the available angular range of inspection by synthesising a large aperture transducer; therefore spatially averaging noise contributions from grain structure, allowing an increase in Signal-to-Noise ratio (SNR) and increasing the potential signal response from a defect.
The aim is to design a novel array transducer working in a full matrix capture (FMC) mode. This novel design will enable efficient acquisition of data for SAFT processing. SAFT post processing will generate 2D and 3D reconstructions of the ultrasonic volumetric image to produce a simple pass or fail indication for the user. This will dramatically reduce the time required at the asset and effectively remove the need for additional interpretation by highly skilled operators.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- agricultural sciencesagriculture, forestry, and fisheriesagriculturegrains and oilseeds
- natural scienceschemical sciencesinorganic chemistrytransition metals
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Topic(s)
Call for proposal
FP7-SME-2012
See other projects for this call
Funding Scheme
BSG-SME - Research for SMEsCoordinator
CB21 6AL Cambridge
United Kingdom