Performance of modern robotic manipulators has enabled research and development of fast automated non-destructive testing (NDT) systems for complex geometries. This paper presents recent outcomes of work aimed at removing the bottleneck due to data acquisition rates, to fully exploit the scanning speed of modern 6-DoF manipulators. State of the art ultrasonic instrumentation has been integrated into a large robot cell to enable fast data acquisition, high scan resolutions and accurate positional encoding. A fibre optic connection between the ultrasonic instrument and the server computer enables data transfer rates up to 1.6GB/s. Multiple data collection methods are compared. Performance of the integrated system allows traditional ultrasonic phased array scanning as well as full matrix capture (FMC). In FMC configuration, linear scan speeds up to 156mm/s with 64 pulses per frame are achieved - this speed is only constrained by the acoustic wave propagation in the component. An 8x increase of the speed (up to 1.25m/s) can be achieved using multiple transmission elements, reaching the physical limits for acceptable acoustic alignment of transmission and reception paths. Scan results, relative to a 1.2m x 3m carbon fibre sample, are presented
In this paper, we present a novel and flexible method for reliable and robust defect detection in difficult materials. It is well known in the literature that the interaction between ultrasonic beams and the insonified medium is a highly nonlinear process, which potentially exhibits distinctive frequency-dependent properties for defects and random reflectors with a degree of randomness. Instead of investigating the structure and pattern of the spectrum of an individual echo, the proposed method focuses on the distinction between the ensembles of defect signals and clutter noise. A training process is used to establish the statistical analysis, based on which a hypothesis test is then applied to received echoes to detect defects. The approach is expected to be adaptive to the material microstructure and characteristics due to the statistical training. Experiments with a 5MHz transducer on austenitic steel samples from a coal fired power station are conducted. Austenitic steel is highly scattering and attenuating, and the method demonstrates accurate and reliable defect detection. When applied to A-scan waveforms, the grain noise is significantly reduced while defect signals are enhanced, and the signal-to-noise ratio (SNR) is improved by about 20dB. As a result, the defect is more visible and can be readily identified in B-scan images. Initial results indicate that this method is robust and delivers good performance without additional calibration and compensation
This paper presents a robust frequency diversity based algorithm for clutter reduction in ultrasonic A-scan waveforms. The performance of conventional spectral-temporal techniques like Split Spectrum Processing (SSP) is highly dependent on the parameter selection, especially when the signal to noise ratio (SNR) is low. Although spatial beamforming offers noise reduction with less sensitivity to parameter variation, phased array techniques are not always available. The proposed algorithm first selects an ascending series of frequency bands. A signal is reconstructed for each selected band in which a defect is present when all frequency components are in uniform sign. Combining all reconstructed signals through averaging gives a probability profile of potential defect position. To facilitate data collection and validate the proposed algorithm, Full Matrix Capture is applied on the austenitic steel and high nickel alloy (HNA) samples with 5MHz transducer arrays. When processing A-scan signals with unrefined parameters, the proposed algorithm enhances SNR by 20dB for both samples and consequently, defects are more visible in B-scan images created from the large amount of A-scan traces. Importantly, the proposed algorithm is considered robust, while SSP is shown to fail on the austenitic steel data and achieves less SNR enhancement on the HNA data
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.