In industrial non-destructive evaluation (NDE), it is increasingly common for data acquisition to be automated, driving a recent substantial increase in the availability of data. The collected data need to be analysed, typically necessitating the painstaking manual labour of a skilled operator. Moreover, in automated NDE a region of an inspected component is typically interrogated several times, be it within a single data channel due to multiple probe passes, across several channels acquired simultaneously or over the course of repeated inspections. The systematic combination of these diverse readings is recognized to offer an opportunity to improve the reliability of the inspection, but is not achievable in a manual analysis. This paper describes a data-fusion-based software framework providing a partial automation capability, allowing component regions to be declared defect-free to a very high probability while readily identifying defect indications, thereby optimizing the use of the operator's time. The system is designed to applicable to a wide range of automated NDE scenarios, but the processing is exemplified using the industrial ultrasonic immersion inspection of aerospace turbine discs. Results obtained for industrial datasets demonstrate an orders-of-magnitude reduction in false-call rates, for a given probability of detection, achievable using the developed software system.
In order to accurately predict the performance of materials under dynamic loading conditions, models have been developed that describe the rate-dependent material behavior and irrecoverable plastic deformation that occurs at elevated strains and applied loads. Most of these models have roots in empirical fits to data and, thus, require the addition of specific parameters that reflect the properties and response of specific materials. In this work, we present a systematic approach to the problem of calibrating a Johnson-Cook plasticity model for 304L stainless steel using experimental testing in which the parameters are treated as dependent on the state of the material and uncovered using experimental data. The results obtained indicate that the proposed approach can make the presence of a discrepancy term in calibration unnecessary and, at the same time, improve the prediction accuracy of the model into new input domains and provide improved understanding of model bias compared to calibration with stationary parameter values.
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