Optimizing raster scanning parameters in nondestructive evaluation using simulation of probe sensitivity field

Koshti, Ajay M.

doi:10.1117/12.2581691

Cited by 3 publications

(2 citation statements)

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“…This work is limited to POD analysis and simulation approach for single-hit limited sample POD or LS POD. Broadly, LS POD covers signal response-based data for both single-hit and multi-hit POD applications including modeling of transfer functions [7][8][9][10] . Smaller sample size is usable in LS POD analysis.…”

Section: Introductionmentioning

confidence: 99%

Transfer function models for using empirical and physics-based simulation signal response data

Koshti

2024

Health Monitoring of Structural and Biological Systems XVIII

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In many situations, real or induced flaws such as tight cracks with known morphology cannot be manufactured in part geometry specimens or in real parts. Typically, surface fatigue cracks are manufactured in simple geometry specimens such as flat plates, dog-bone shaped flat or cylindrical specimens. If a nondestructive evaluation (NDE) technique is required to provide a reliably detectable flaw size, denoted as a90/95, for detection of induced flaws in a part, then a direct method for qualifying the NDE procedure is to use appropriate induced flaw specimens and perform NDE procedure demonstration on the specimens. Probability of detection (POD) analysis of the empirical data may provide estimation of a90/95. This approach is described as direct POD demonstration testing, which may follow guidelines of MIL-HDBK-1823. This paper considers a case, where embedded tight cracklike induced flaws are to be detected reliably using a signal response based NDE procedure. Here, it is assumed that it is not practical to make surface or embedded induced flaw specimens in part geometry or configuration. Therefore, a direct POD demonstration testing cannot be undertaken. It is also assumed that simulation of signal response is possible for both surface and embedded induced flaws in part geometry specimens using a physics-based model. The proposed approach for NDE procedure qualification uses artificial flaws in simple geometry and part geometry specimens, and induced flaws in the same type of simple geometry specimens. Signal response data is taken on all sets of artificial and induced flaws in simple geometry and part geometry specimens. Moreover, simulated signal response data is generated for surface and embedded flaws. Thus, a case of five signal response versus flaw size datasets is considered. Three of the datasets are empirical and two datasets are physics model-based simulation datasets. A method of devising and using transfer function calculation dataset blocks to estimate the reliably detectable flaw size or the demonstration flaw size is provided.

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Section: Introductionmentioning

confidence: 99%

Transfer function models for using empirical and physics-based simulation signal response data

Koshti

2024

Health Monitoring of Structural and Biological Systems XVIII

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“…This work is limited to POD analysis and simulation approach for single-hit limited sample POD 7 or LS POD. Broadly, LS POD covers signal response-based data for both single-hit and multi-hit POD applications including modeling of transfer functions [7][8][9][10][11][12][13][14][15] . Smaller sample size is usable in LS POD analysis.…”

Section: Introductionmentioning

confidence: 99%

Transfer function models and sensitivity analysis

Koshti

2023

Health Monitoring of Structural and Biological Systems XVII

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In many situations, real or induced flaws such as tight cracks with known morphology cannot be manufactured in part configuration specimens or in real parts. Typically, fatigue cracks are manufactured in simple geometry specimens such as flat plates, dog-bone shaped flat or round specimens. If a nondestructive evaluation (NDE) technique is required to provide a reliably detectable target flaw size denoted as a90/95 for induced flaws in real part, then the direct method for qualifying the NDE procedure is to use the appropriate induced flaw specimens and perform probability of detection analysis using these flaws. This can be described as direct POD demonstration testing, which may follow guidelines of MIL-HDBK-1823. This paper considers a case, where induced flaws are not available in part configuration specimens. Therefore, a direct POD demonstration study cannot be undertaken. In such situation, general practice for NDE procedure qualification is to use artificial flaws in simple geometry and part configuration specimens, and induced flaws in the same simple geometry specimens. Signal response data is taken on all sets of artificial and induced flaws. NDE procedure testing on induced flaws in simple geometry specimen is called NDE demonstration testing here. A transfer function NDE procedure qualification method for the forward case calculates predicted induced flaw size for demonstration using a chosen target flaw size. Another transfer function method for the inverse case, calculates the target flaw size using a given demonstration flaw size. The transfer function analysis assumes relationship of artificial flaw signal responses in real parts and simple geometry specimens; and induced flaw responses in simple geometry specimens to induced flaws in real parts. The signal response transfer relationships model should be defined before transfer function models can be devised. Assuming that the signal response transfer relationships model is valid, the forward and inverse case transfer function methods have been devised. Because of lack of signal response data from induced flaws in real part, the 90/95% POD/confidence (P/C) cannot be demonstrated directly. However, the transfer function method may be assessed using simulation to evaluate whether the resulting target flaw size or demonstration flaw size provides adequate confidence to the assumed signal response transfer relationships model. Therefore, the transfer function approach is a risk assessment approach. Both the signal response transfer relationships model and the transfer function model are important in managing risk in results provided by the transfer function NDE technique qualification or assessment. The signal response transfer relationships model needs to be validated with empirical data and then transfer function model needs to be validated for desired P/C on case-by-case basis.

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Assessing risk due to small sample size in probability of detection analysis using tolerance intervals

Koshti

2022

Health Monitoring of Structural and Biological Systems XVI

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Optimizing raster scanning parameters in nondestructive evaluation using simulation of probe sensitivity field

Cited by 3 publications

References 0 publications

Transfer function models for using empirical and physics-based simulation signal response data

Transfer function models for using empirical and physics-based simulation signal response data

Transfer function models and sensitivity analysis

Assessing risk due to small sample size in probability of detection analysis using tolerance intervals

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