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Ajay M Koshti

Publications and source records attributed to Ajay M Koshti.

Considerations for Qualifying Reliable Eddy Current Array Technique for Detection of Backwall Cracks

A reliable nondestructive evaluation (NDE) technique provides minimum 90% probability of detection(POD) with 95% confidence for detection of cracklike flaws of a qualified size. In this case, the flaws are on backside of material. Eddy current array (ECA) probe or a single sensor eddy current probe in c-scanning mode is used for the flaw detection. Here, two geometries are considered for the test specimens. The two geometries are, a flat plate, and tube or pipe. The probe is assumed to be on outer diameter (OD)surface and the flaw is assumed to be at the inner diameter (ID) surface for tube inspection. The flaw and probe are on opposite side of wall thickness for inspection of plate material. The application may be used for acreage inspection or just for inspection of butt weld and heat affected zone (HAZ) in a tube, cylinder, or plate. The proposed approach is based on developing an instrument standardization or calibration procedure. Decision threshold of inspection procedure is determined using empirical qualification and noise data. The work explores essential parameters that are required to meet certain conditions to ensure reliable flaw detection. Physics-based simulation of eddy current array flaw detection is used to understand effect of essential parameters on signal response (amplitude and phase),and therefore flaw detectability. Simulation data is used to justify choice of calibration reference standard requirements and the qualification approach. An ECA technique qualification model is provided. The paper gives brief description of tasks to be completed for qualifying ECA technique for reliable detection of cracklike flaws.

Eddy current array inspection, nondestructive eval

Guidelines for Mapping Reliably Detectable Dye Penetrant Crack Size at External Corners with Fillet Radii

The technical memorandum addresses some concerns with dye penetrant crack sizes stated in Table 1 (and Table 2) of NASA-STD-5009B. Table 1 corner crack size of a = 0.100” and c = 0.150” is reasoned to be larger than it should be based on the original Standard dye penetrant qualification. In the original Standard dye penetrant qualification, surface crack size with depth a = 0.075” x length 2c = 0.150” was qualified with minimum 90% Probability of Detection (POD) with 95% confidence (conf.). No corner crack size was qualified by direct POD testing in original dye penetrant qualification. Standard dye penetrant detectable crack size for radiused corner is not addressed in NASA-STD-5009B.

Dye Penetrant Testing

Guidelines for Determining X-ray Shots for Inspection of Cylindrical Parts

For reliable detection of Standard size crack for x-ray radiography, NASA-STD-5009 requires that x-ray angle shall be within +/-5 deg. of assumed crack plane at the point of x-ray intersection with crack. Equations for calculation of shot length and number of shots on a girth weld of a cylindrical part are provided to meet this requirement for selected cases of x-ray set-ups. Crack plane is assumed to be radial-axial with respect to the part geometry. Limit angles other than +/-5 deg. may be chosen. These equations can be used to interpret limit x-ray angle requirements to calculate corresponding shot length and number of shots on a cylindrical part. If certain percent (5-10%) overlap is needed, then the limit angle used in the calculations should be decreased by that percentage so that the overlap area meets the limit angle requirements.

Ajay M Koshti

Assessing Risk Due to Small Sample Size in Probability of Detection Analysis Using Tolerance Intervals

Small sample size (e.g.6-30) poses risk in results of probability of detection (POD) analysis using tolerance intervals. This method is also called as the limited sample or LS POD. The analysis is performed either during NDE procedure qualification or for assessment of reliability of an NDE procedure. The risk is primarily due to sampling error. Smaller samples are not likely to be random to the population or representative of the population. The small samples are likely to be biased. Biased samples have smaller standard deviation compared to the population. POD analysis with small biased sample can lead to overestimation of POD. Many sampling schemes are available in statistics to mitigate sampling risk. Primary objective of POD analysis is to determine a decision threshold from signal response measurements of a sample such that it is less than or equal to population decision threshold for 90% POD. Sampling error implies that this NDE reliability condition is violated. One of sampling types is called a representative sample. Representative samples reduce variance in POD estimates but also reduce magnitude of the error. Sampling sensitivity analysis for some sampling types is performed here using repetitive random sampling or Monte Carlo method. Six sampling types are considered for comparison. Some of the sampling types are similar to drawing a representative sample. LS POD model assumes random sampling. Therefore, random sampling is used as a basis for comparison with each sampling type. The sampling types used in the analysis are, A. Nominal and worst-case sampling, B. Worst-case sampling, C. Nominal case sampling, D. Random sampling, E. Random target, and sub-target sampling. F. Nominal target and sub-target sampling. Results of Monte Carlo simulation indicate that type F sampling can mitigate sampling risk and is also more practical to implement. Type A sampling may also mitigate the sampling risk, but it may be less practical to implement.

Ajay M Koshti

Importance of Contrast-to-Noise Ratio Sensitivity Function in Nondestructive Evaluation

A reliable nondestructive evaluation (NDE) technique provides minimum 90% probability of detection (POD) with 95% confidence for detection of cracklike flaws of a specific size. This requirement comes from fracture control. Fracture control is a risk assessment and control approach for preventing structural failure of fracture critical hardware during its service life. One of the approaches of fracture control is damage tolerance safe life analysis, which assumes that NDE reliably detectable size flaw may exist in the hardware post nondestructive evaluation. Analysis and/or testing are performed to verify that the hardware will not fail during its service life despite existence of such a flaw. NDE methods are conventionally qualified by probability of detection (POD) demonstration testing to ensure reliable flaw detection. Here, a novel and alternative to POD flaw detection reliability approach that uses contrast-to-noise ratio (CNR) is given. The approach uses CNR sensitivity function (SF) and CNR. CNR SF is the minimum CNR needed for reliable flaw detection as a function of certain flaw indication characteristics. Primary difference between POD approaches and CNR flaw detection reliability approach is resolution of flaw detection system. POD approach does not account for the resolution of the system, while CNR approach accounts for the flaw detection resolution. Resolution is determined from the modulation transfer function (MTF) of the flaw detection system. CNR flaw detection reliability approach emphasizes quantitative assessment of “how well each flaw indication is detected”. Therefore, CNR approach is helpful in reducing number of flaws in the sample used in NDE procedure qualification, resulting in cost and time savings. CNR approach is also a case of multi-hit flaw detection analysis which has better flaw detectability size than conventional single-hit flaw detectability. Thus, CNR approach, where applicable, may be superior to conventional POD approach. The paper is an overview of development of CNR modeling, CNR SF modeling and measurement by the author. The paper gives key references to currently used NDE procedure POD qualification approaches including those specific to CNR and CNR SF development in multi-hit flaw detection in NDE. The paper covers core topics in nondestructive evaluation engineering.

Contrast-to-noise ratio