Dr. Ajay M. Koshti Profile

Dr. Ajay M. Koshti

Nondestructive Evaluation Senior Discipline Expert at NASA Johnson Space Ctr

SPIE Involvement:

Conference Program Committee | Author

Area of Expertise:

Mechanical Engineering , Loads and Stress Analysis , Ultrasonic Stress and Load Measurement , Nondestructive Testing (NDE) , Infrared Thermography , NDE Probability of Detection Analysis

Profile Summary

Dr. Ajay Koshti holds B.Tech., M.S., and D. Sc. degrees; and Professional Engineer (PE) certification in Mechanical Engineering. He holds ASNT level III certification in five Nondestructive Evaluation (NDE) methods.
He worked as an NDE engineer on NASA Space Shuttle Program from 1988 to 2011. He also worked as Orbiter Ground Support Equipment (GSE), including Orbiter Handling Equipment (OHE), engineer for six years at Kennedy Space Center. He led the implementation of ultrasonic preload measurements on some Space Shuttle joints and designed Orbiter towing lines on the roads.

From 2004 to 2024, he worked as a lead NDE engineer, and later as a Senior Discipline Expert in NDE at NASA Johnson Space Center. Post NASA Shuttle Columbia accident, he led development and implementation of infrared nondestructive testing of Space Shuttle wing leading edge. He chaired NASA Orbiter NDE Working Group and contributed to nondestructive methods used on Space Shuttle Orbiter hardware such as vertical tail lug joints, main propulsion system (MPS) flowliners, MPS valve poppets, composite pressure vessels, midbody boron-aluminum struts, payload bay doors, laminated and honeycomb composite structures.

Since the end of Space Shuttle Program in 2011, he has been working on the NASA International Space Station (ISS) program, NASA Orion MPCV Program, and NASA Commercial Cargo & Crew Programs with SpaceX as a responsible NDE engineer. He co-chairs the NASA Orion MPCV NDE Working Group meeting and NASA Commercial Program NDE Group with SpaceX meeting.

He authored over 50 research papers. He pioneered analytical models for ultrasonic measurement of interference pressure, bending and shear load in bolted joints. He pioneered normalized “Koshti” contrast data processing in infrared thermography. He has ten patents in NDE. He pioneered limited validation approach for reliable flaw detection in NDE and analytical models for assessing X-ray crack detectability.

Publications (32)

Proceedings Article | 25 April 2023 Presentation + Paper

Presentation + Paper

Transfer function models and sensitivity analysis

Ajay Koshti

Proceedings Volume 12488, 124880T (2023) https://doi.org/10.1117/12.2652138

KEYWORDS: Data modeling, Nondestructive evaluation, Target detection, Calibration, Statistical analysis, Risk assessment, Statistical modeling, Phase only filters, Manufacturing, Reliability

Read Abstract +

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 a_90/95 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.

Proceedings Article | 19 April 2022 Presentation + Paper

Presentation + Paper

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

Ajay Koshti

Proceedings Volume 12048, 1204817 (2022) https://doi.org/10.1117/12.2610798

KEYWORDS: Statistical analysis, Reliability, Nondestructive evaluation, Monte Carlo methods, Statistical analysis, Reliability, Nondestructive evaluation

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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.

Proceedings Article | 5 April 2021 Presentation + Paper

Presentation + Paper

Probability of detection analysis in multi-hit flaw detection

Ajay Koshti

Proceedings Volume 11594, 115940K (2021) https://doi.org/10.1117/12.2595733

KEYWORDS: Statistical analysis, Reliability, Phase only filters, Nondestructive evaluation, Target detection, Tolerancing, Inspection, Computer simulations, Aerospace engineering

Read Abstract +

Current approaches to qualification or reliability assessment of NDE procedures by traditional probability of detection (POD) testing per MIL-HDBK-1823A require significant number of specimens with known size real or simulated flaws. For many practical applications in aerospace industry, either it is difficult to produce or it is economically not feasible to fabricate the necessary flaw specimens in required quantity. Thus, there is a strong need for alternative methodologies to provide assessments of reliability of NDE procedures using fewer flaw specimens. The paper provides an alternate NDE reliability assessment approach using dependence of the probability of detection (POD) and probability of false positive (POF), on the contrast-to-noise ratio (CNR) and decision threshold-to-noise ratio (TNR) for selected POD and POF models. The NDE reliability assessment approach is termed as limited sample (LS) POD assessment. Current work assumes a multi-hit flaw detection methodology i.e. the flaw detection calls are mapped to form an indication with cluster of pixels in a 2D image. In LS POD approach, the signal response from a fixed size flaw of interest is assumed to follow a normal distribution. The approach uses statistical tolerances computed using signal response sample data and k₁-factor. Similar to traditional POD analysis, a flaw size with chosen POD and confidence is determined. Alternately, POD/Conf. can be determined for a chosen decision threshold for the flaw size used in the analysis. The POF is also estimated in the analysis. Noise is assumed to follow either a normal or lognormal distribution. For reliable detection of the target size flaw, minimum POD/Conf. of 90/95% and maximum POF of 1% are assumed. The sample of signal responses is assumed to be representative of the assumed population of signal responses. Similarly, the noise measurements are assumed to be representative of those expected in the inspection of real hardware. If representative flaw sample and noise measurements are used, LS POD results pose no risk in the POD and POF estimation. Risk to meeting NDE flaw size detection requirements can be assessed based on how representative the flaw sample and noise measurements are and based on positive margin between LS POD results and procedure reliability requirements.

Proceedings Article | 22 March 2021 Presentation + Paper

Presentation + Paper

Assessing visual and system flaw detectability in nondestructive evaluation

Ajay Koshti

Proceedings Volume 11592, 1159210 (2021) https://doi.org/10.1117/12.2581685

KEYWORDS: Visualization, Optical inspection, Nondestructive evaluation, Optical resolution, Contrast sensitivity, X-rays, Inspection, Imaging systems, Image visualization

Read Abstract +

NDE methods assume capability to detect certain flaw size. Sometimes the capability is validated using probability of detection (POD) demonstration. Although, NDE imagers may highlight indications that meet certain indication detection criteria, the inspection results are evaluated by a certified operator and the operator takes responsibility of the results of his assessment. The operator assesses both i.e. the NDE data of the indication and the visual indication itself. Normally, assessment of NDE data also has a visual component and may also have visual inspection of the part. Visual assessment of image data and visual inspection of part are two kinds of visual flaw detection. Every visual flaw detection has either implicit or explicit flaw detectability size associated with it. Explicit flaw detectability size has POD/confidence associated with it. The paper provides methodology to assess flaw detectability in visual detection. Indication contrast, indication size, background illumination, and noise are important factors in flaw detectability. Visual perception is affected by these factors. Visual acuity of operators is measured using Jaeger chart. Visual resolution is measured by another visual acuity test using line-pair targets. Contrast sensitivity as a function of line-pair frequency can be measured using a line-pair target with sinusoidally varying luminance across the line-pair widths with logarithmically decreasing contrast along length of the line-pairs. Operator contrast sensitivity function, indication contrast-to-noise ratio (CNR) indication size are necessary for assessment of flaw detectability in visual inspection. Alternately, a function called CNR sensitivity function, CNR and new parameter called resolution ratio have been proposed to relate to reliability of detecting indication. In visual assessment of data, important factors to consider are system resolution, resolution of data display monitor, contrast sensitivity, indication contrast, indication size, background illumination, and noise.

Proceedings Article | 22 March 2021 Presentation + Paper

Presentation + Paper

Using requirements on merit ratios for assessing reliability of NDE flaw detection

Ajay Koshti

Proceedings Volume 11593, 1159323 (2021) https://doi.org/10.1117/12.2581688

KEYWORDS: Nondestructive evaluation, Reliability, Phase only filters, Target detection, Statistical analysis, Data modeling, Signal detection, Inspection, Computer simulations

Read Abstract +

Current approaches to qualification or reliability assessment of NDE procedures by traditional probability of detection (POD) testing per MIL-HDBK-1823A require significant number of specimens with known size real or simulated flaws. For many practical applications in aerospace industry, either it is difficult to produce or it is economically not feasible to fabricate the necessary flaw specimens in required quantity. Thus, there is a strong need for alternative methodologies to provide assessments of reliability of NDE procedures using fewer flaw specimens. The paper provides an alternate NDE reliability assessment approach using dependence of the probability of detection (POD) and probability of false positive (POF), on the contrast-to-noise ratio (CNR) and decision threshold-to-noise ratio (TNR) for selected POD and POF models. The NDE reliability assessment approach is termed as limited sample (LS) POD assessment. Current work assumes a single-hit flaw detection methodology i.e. the flaw detection calls are not mapped to form an indication with cluster of pixels in a 2D image. In the LS POD approach, the signal response from a fixed size flaw of interest is assumed to follow a normal distribution. The approach uses statistical tolerances computed using signal response sample data and 𝑘₁-factor. Similar to traditional POD analysis, a flaw size with chosen POD and confidence is determined. Alternately, POD/Conf. can be determined for a chosen decision threshold for the flaw size used in the analysis. The POF is also estimated in the analysis. Noise is assumed to follow either a normal or lognormal distribution. For reliable detection of the target size flaw, minimum POD/Conf. of 90/95% and maximum POF of 1% are assumed. The sample of signal responses is assumed to be representative of the assumed population of signal responses. Similarly, the noise measurements are assumed to be representative of those expected in the inspection of real hardware. If representative flaw sample and noise measurements are used, LS POD results pose no risk in the POD and POF estimation. Risk to meeting NDE flaw size detection requirements can be assessed based on how representative the flaw sample and noise measurements are, and based on positive margin between LS POD results and procedure reliability requirements.

Proceedings Article | 22 March 2021 Presentation + Paper

Presentation + Paper

Using requirements on merit ratios for assessing reliability of NDE flaw detection in multi-hit detection in digital radiography

Ajay Koshti

Proceedings Volume 11593, 115932K (2021) https://doi.org/10.1117/12.2581690

KEYWORDS: Reliability, Radiography, Nondestructive evaluation, Phase only filters, Interference (communication), Statistical analysis, Signal to noise ratio, Inspection, Data analysis, Analytical research

Read Abstract +

Current approaches to qualification or reliability assessment of NDE procedures by traditional probability of detection (POD) testing per MIL-HDBK-1823A require significant number of specimens with known size real or simulated flaws. For many practical applications in aerospace industry, either it is difficult to produce or it is economically not feasible to fabricate the necessary flaw specimens in required quantity. Thus, there is a strong need for alternative methodologies to provide assessments of reliability of NDE procedures using fewer flaw specimens. The paper provides an alternate NDE reliability assessment approach using dependence of the probability of detection (POD) and probability of false positive (POF), on the contrast-to-noise ratio (CNR) and decision threshold-to-noise ratio (TNR) for selected POD and POF models. The NDE reliability assessment approach is termed as limited sample (LS) POD assessment. LS POD approach for single hit flaw detection is developed first. Current work provides some progress in extending LS POD single hit approach to LS POD multi-hit approach. In multi-hit flaw detection, flaw detection data is mapped to form an indication with cluster of pixels in a 2D image which is absent in single-hit flaw detection. Resolution of the measurement system is additional factor to be accounted for in multi-hit LS POD. Resolution is addressed with a term defined as resolution ratio. These limits can be determined through modeling but should also be corroborated through empirical data. In the LS POD approach, the signal response from a fixed size flaw of interest is assumed to follow a normal distribution. The approach uses statistical tolerances computed using signal response sample data and 𝑘₁-factor. Similar to traditional POD analysis, goal of the multi-hit LS-POD approach is to determine POD, except emphasis is on relationship of resolution ratio and CNR with POD and POF. The flaw size is determined from the resolution ratio. The POF is also estimated in the analysis. Noise is assumed to follow either a normal or lognormal distribution. For reliable detection of the target size flaw, minimum POD/Conf. of 90/95% and maximum POF of 1% are assumed. The sample of signal responses is assumed to be representative of the assumed population of signal responses. Similarly, the noise measurements are assumed to be representative of those expected in the inspection of real hardware. Using simulation, current work indicates that resolution ratios relate to minimum CNR for reliable flaw detection. An idealized case of a round x-ray indication is chosen for the simulation. Empirical approach for multi-hit POD analysis is provided.

Proceedings Article | 22 March 2021 Presentation + Paper

Presentation + Paper

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

Ajay Koshti

Proceedings Volume 11592, 115920Q (2021) https://doi.org/10.1117/12.2581691

KEYWORDS: Transducers, Raster graphics, Nondestructive evaluation, Ultrasonics, Sensors, Spatial resolution, Ultrasonography, Target detection, Signal detection, Reflectors

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Proceedings Article | 22 March 2021 Presentation + Paper

Presentation + Paper

Modeling reliability of NDE method providing C-scan, a case of flaw field simulation

Ajay Koshti

Proceedings Volume 11593, 115932J (2021) https://doi.org/10.1117/12.2582425

KEYWORDS: Transducers, Sensors, Reliability, Nondestructive evaluation, Ultrasonics, Spatial resolution, Raster graphics, Ultrasonography, Target detection, Signal detection

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Proceedings Article | 22 March 2021 Presentation + Paper

Presentation + Paper

Assessment of flaw detectability using transfer function

Ajay Koshti

Proceedings Volume 11592, 115920O (2021) https://doi.org/10.1117/12.2581689

KEYWORDS: Nondestructive evaluation, Manufacturing, Interference (communication), Inspection, Error analysis, Direct methods

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Proceedings Article | 1 April 2019 Presentation + Paper

Presentation + Paper

NDE flaw detectability validation using smaller number of signal response data-points

Ajay Koshti

Proceedings Volume 10971, 109710M (2019) https://doi.org/10.1117/12.2513960

KEYWORDS: Monte Carlo methods, Statistical analysis, Nondestructive evaluation, Signal to noise ratio, Inspection, Thermal modeling

Read Abstract +

The paper provides a procedure for validating flaw detection size using smaller number of signal response data points or number of flaws. MIL-HDBK-1823 probability of detection (POD) analysis of inspection test data results in a flaw size estimate, denoted as a_90/95. The a_90/95. flaw size has 90% POD and minimum 95% confidence. Here, a case is considered where the inspection test data points are not sufficient to run the POD analysis but are sufficient for limited validation approach provided here. The procedure is based on an approach developed by the author. The approach is conservative and it validates a flaw size that is greater than a theoretical a₉₀ used in the simulation data. By using simulated signal response data, the paper shows that conditions based on limits on ratios, called merit ratios, such as contrast-to-noise ratio (CNR), and net decision threshold-to-noise ratio (TNR), and contrast to threshold ratio (CTR) can be used in validating a reliably detectable flaw size. Monte Carlo random data samples from a population of data of a signal response characteristics are used. Decision threshold for each sample and corresponding merit ratios are computed. Using the decision threshold computed from each sample and the signal to flaw size characteristics, the theoretical a_th90/95 is computed for each sample. The merit ratios and theoretical a_th90/95 for each sample are plotted as distributions. These distributions are analyzed to calculate confidence in the meeting the merit ratio conditions. The target flaw size is compared with the distribution of the theoretical a_th90/95 to calculate confidence in validating the target size. The procedure requires measurement of noise. Results of the simulated data prove validity of the approach. Minimum six data points are recommended. Although, small number of flaws are sufficient in this validation procedure, these flaw sizes are carefully selected based on experimental work to find the smallest flaw size that can meet the merit ratio conditions. Therefore, some trial-and-error effort is needed to choose correct target flaw size so that the merit conditions can be met.

Proceedings Article | 1 April 2019 Presentation + Paper

Presentation + Paper

NDE flaw detectability size estimation using smaller number of hit-miss data-points

Ajay Koshti

Proceedings Volume 10971, 109711S (2019) https://doi.org/10.1117/12.2513959

KEYWORDS: Statistical analysis, Nondestructive evaluation, Monte Carlo methods, Inspection, Computer simulations

Read Abstract +

The paper provides an engineering analysis approach for estimating non-destructive evaluation (NDE) flaw size using smaller number of hit-miss data-points. Probability of detection (POD) analysis of inspection test data provides a flaw size, denoted as a_90/95. The flaw size has 90% POD and minimum 95% confidence. In this approach, we do not estimate a_90/95 flaw size. Here, goal is to estimate the flaw detectability size that is larger than the unknown a₉₀ for the NDE technique. The approach is devised using a large set of simulated signal response data and estimating population a_90/95p using an â versus a POD model. The signal response data is converted to hit-miss data using a decision threshold. A hit-miss or â versus a POD model is assumed here. Then a sampling scheme is set-up for generating limited number of data-points and flaw size estimation equations are established by substituting POD model quantities by their approximate estimates based on demonstrated flaw sizes. The data sampling scheme used here is like a Monte Carlo simulation allowing evaluation of engineering estimation approach. The distribution of multiple engineering flaw size estimates provides a confidence on the engineering estimate being larger than a_90/95p used in the simulation. NDE practitioner normally uses some rule of thumb to estimate flaw size based on limited flaw detection data. A typical rule of thumb may be to multiply the smallest detected flaw size by a factor such as two. The rule of thumb is subjective. Compared to the rule of thumb, paper provides more complex equations and demonstrates these equations on simulated randomized data. NDE flaw detection size is used in fracture mechanics analysis of aerospace structures. Fracture mechanics uses flaw growth analysis using NDE flaw size to determine service life of the part. The part design is optimized for performance, weight, service life and cost. In order to tolerate larger NDE flaw size, parts are designed to be stronger and heavier to reduce stress. Therefore, a_90/95 flaw size is desirable. But many times, POD demonstration, requiring a large number of real known size flaws, is cost prohibitive. Thus, if we can estimate risk or confidence associated with the engineering estimate of flaw detectability size, program managers may accept the engineering approach. Here, we need to assess confidence that engineering estimate of flaw size is greater than or equal to estimated a^90/95p of the simulated data. Also, we need to assess smallest possible value of the flaw size estimate and compare it with the estimated a_90/95p. Lastly, the probability of false positive (POF) also needs to be assessed. A technique is considered reliable, if it provides flaw detectability size with 90/95 POD/confidence and also provides a POF less than or equal to a chosen value i.e. 1%. Linear correlation is used between the signal response data and flaw size. POD software mh1823 uses generalized linear model (GLM) in POD analysis after transforming the flaw size and signal response, if needed, using logarithm. Therefore, this simulation approach is in agreement with the linear signal correlation used in mh1823. The approach is conservative and is designed to provide a larger flaw detectability size compared to POD approach with high confidence and takes into account number of data-points used. Such NDE flaw detectability size estimation approach, although, not as rigorous as POD, can be cost effective if the larger estimated flaw size can be tolerated.

Proceedings Article | 1 April 2019 Presentation + Paper

Presentation + Paper

Assessing reliability of NDE flaw detection using smaller number of demonstration data points

Ajay Koshti

Proceedings Volume 10971, 109710L (2019) https://doi.org/10.1117/12.2513955

KEYWORDS: Signal to noise ratio, Nondestructive evaluation, X-rays, Contrast sensitivity, Image resolution, Reliability, Radiography, Statistical analysis

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The paper provides an engineering analysis approach for assessing reliability of NDE flaw detection using smaller number of demonstration data points. It explores interdependence of probability of detection (POD), probability of false positive (POF), contrast-to-noise ratio, and net decision threshold-to-noise ratio in a simulated data; and draws some generically applicable inferences to devise the approach. ASTM nondestructive evaluation standards provide requirements on signal-to-noise ratio and/or contrast-to-noise ratio in order to provide reliable flaw detection and limit false positive calls. POD analysis of inspection test data results in a flaw size, denoted by a_(90/95). The flaw size has 90% POD and minimum 95% confidence. POF is also estimated in the analysis. POD demonstration requires specimen with flaws of known size. In many situations, it is very expensive to produce the large number of flaws required for the POD analysis. In some situations, only real flaws can truly represent the flaws for demonstration. Real flaws of correct size and location within part may be difficult to produce, if not impossible. Here, an engineering analysis approach is devised to assess reliability of NDE technique when a limited number of flaws are available for demonstration. A technique is considered reliable, if it provides flaw detectability equal to or better than a_(90/95) and also provides a POF less than or equal to a chosen value. The paper uses simulated signal response versus flaw size data to devise the approach. Linear correlation is used between the signal response data and flaw size. POD software mh1823 uses generalized linear model (GLM) in POD analysis after transforming the flaw size and signal response, if needed, using logarithm. Therefore, this approach is in agreement with the linear signal correlation used in mh1823. Using the POD analysis of data, generic conditions on contrast-to-noise ratio and net decision threshold-to-noise ratio are derived for reliable flaw detection. In order to assess technique reliability using the engineering approach, signal response-to-flaw size correlation about the flaw size of concern is needed. In addition, measurement of noise is also needed. If the technique meets the above requirements, assumption of linear signal –to-flaw size correlation and conditions on noise, then the technique can be assessed using this analysis as it fits the underlying POD model used here. The approach is conservative and is designed to provide a larger flaw size compared to POD approach. Such NDE technique assessment approach, although, not as rigorous as POD, can be cost effective if the larger flaw size can be tolerated. Typically, this is a situation for all quality control NDE inspections. Here, an NDE technique needs to be reliable and the true a₉₀ is not known, but the assessed flaw size is assumed to be larger than the true a₉₀ due to conservative factors or margins. Applicability of the approach for assessing reliability of flaw detection in x-ray radiography and 2D imaging in general is also explored.

Proceedings Article | 27 March 2018 Presentation + Paper

Presentation + Paper

Assessment of dye penetrant crack detectability in external corners using similarity analysis

Ajay Koshti

Proceedings Volume 10599, 105991L (2018) https://doi.org/10.1117/12.2286788

KEYWORDS: Inspection, Nondestructive evaluation, Physics, Signal to noise ratio

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Proceedings Article | 27 March 2018 Presentation + Paper

Presentation + Paper

X-ray ray tracing simulation and flaw parameters for crack detection

Ajay Koshti

Proceedings Volume 10600, 106000R (2018) https://doi.org/10.1117/12.2286784

KEYWORDS: X-rays, Modulation transfer functions, Ray tracing, X-ray detectors, Contrast sensitivity, Radiography, Image resolution

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NDE flaw detectability is given by flaw size such as crack length. Flaw size parameters or flaw response parameters are used to assess crack detectability. Normalized exposure is used to calculate the flaw size parameter in x-ray radiography. Detector response is used to calculate more complex flaw response parameters. The flaw size parameter provides crack indication width and crack indication amplitude parameter such as void path length ratio parameter. X-ray flaw size parameter model, given here, uses ray tracing model in two dimensions to compute flaw size parameters such as the indication width and the void path length ratio parameter. Results from an analytical model previously published by the author were compared with the ray tracing model. Two runs of ray tracing model are provided. These runs include normal and oblique incident angle x-ray. Since the revised analytical model provides better agreement with the ray tracing model for indication width and normalized exposure, it was used to provide flaw detectability assessments using contrast-to-noise ratio and net unsharpness. Film and digital detector responses are modeled in the analytical model. Modulation transfer function (MTF) is also modeled in the analytical model based on measured basic detector resolution SRb as given in ASTM E2033. Using the detector response and detector MTF, flaw response parameters are computed. Several runs of analytical model were conducted. These runs included zero and oblique incident angle x-ray; simulated film detector, simulated digital detector, micro-focus source and conventional (0.4 mm focal spot) source. Part thickness was also varied. Surface plots with crack depth on x-axis and flaw width on y-axis are provided for MTF. These runs provide plots of MTF, flaw size parameter width, void path length amplitudes, MTF accounted amplitude parameter, simulated detector signal, revealing effect of various quantities on the flaw detection parameters and crack detectability using ASTM E2698 contrast sensitivity, contrast-to-noise ratio and net unsharpness. The paper provides a method to assess and optimize capability of x-ray technique to detect cracks or cracklike flaws reliably through simulation and analysis.

Proceedings Article | 27 March 2018 Presentation + Paper

Presentation + Paper

Eddy current crack detection capability assessment approach using crack specimens with differing electrical conductivity

Ajay Koshti

Proceedings Volume 10599, 105991M (2018) https://doi.org/10.1117/12.2286789

KEYWORDS: Calibration, Inspection, Nondestructive evaluation, Statistical analysis, Data analysis

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Like other NDE methods, eddy current surface crack detectability is determined using probability of detection (POD) demonstration. The POD demonstration involves eddy current testing of surface crack specimens with known crack sizes. Reliably detectable flaw size, denoted by, a_90/95 is determined by statistical analysis of POD test data. The surface crack specimens shall be made from a similar material with electrical conductivity close to the part conductivity. A calibration standard with electro-discharged machined (EDM) notches is typically used in eddy current testing for surface crack detection. The calibration standard conductivity shall be within +/- 15% of the part conductivity. This condition is also applicable to the POD demonstration crack set. Here, a case is considered, where conductivity of the crack specimens available for POD testing differs by more than 15% from that of the part to be inspected. Therefore, a direct POD demonstration of reliably detectable flaw size is not applicable. Additional testing is necessary to use the demonstrated POD test data. An approach to estimate the reliably detectable flaw size in eddy current testing for part made from material A using POD crack specimens made from material B with different conductivity is provided. The approach uses additional test data obtained on EDM notch specimens made from materials A and B. EDM notch test data from the two materials is used to create a transfer function between the demonstrated a_90/95 size on crack specimens made of material B and the estimated a_90/95 size for part made of material A. Two methods are given. For method A, a_90/95 crack size for material B is given and POD data is available. Objective of method A is to determine a_90/95 crack size for material A using the same relative decision threshold that was used for material B. For method B, target crack size a_90/95 for material A is known. Objective is to determine decision threshold for inspecting material A.

Proceedings Article | 19 April 2017 Paper

Paper

Crack detection flaw size parameter modeling for x-rays at grazing angle to crack faces

Ajay Koshti

Proceedings Volume 10169, 101691V (2017) https://doi.org/10.1117/12.2257497

KEYWORDS: Sensors, Signal attenuation, Reflection, X-ray characterization, X-rays, Nondestructive evaluation, Reflectivity, X-ray imaging, Scattering, X-ray technology, Reliability, Probability theory, Specular reflections, X-ray detectors

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Proceedings Article | 19 April 2017 Presentation + Paper

Presentation + Paper

Optimizing probability of detection point estimate demonstration

Ajay Koshti

Proceedings Volume 10169, 101691U (2017) https://doi.org/10.1117/12.2257484

KEYWORDS: Data modeling, Interference (communication), MATLAB, Data analysis, Thermal modeling, Statistical analysis, Probability theory, Nondestructive evaluation, Standards development, Signal to noise ratio

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Proceedings Article | 19 April 2017 Paper

Paper

Reliably detectable flaw size for NDE methods that use calibration

Ajay Koshti

Proceedings Volume 10169, 101691R (2017) https://doi.org/10.1117/12.2257481

KEYWORDS: Signal to noise ratio, Interference (communication), Phase only filters, Inspection, Statistical analysis, Nondestructive evaluation, Calibration, Probability theory

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Proceedings Article | 22 April 2016 Presentation + Paper

Presentation + Paper

A method to measure and estimate normalized contrast in infrared flash thermography

Ajay Koshti

Proceedings Volume 9804, 98041K (2016) https://doi.org/10.1117/12.2211676

KEYWORDS: Video, Thermography, Image processing, Temperature metrology, Reflection, Video processing, Infrared imaging, Infrared radiation, Nondestructive evaluation, Heat flux, Cameras, Infrared cameras, Content addressable memory, Convection, Lamps, Data acquisition

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Proceedings Article | 1 April 2015 Paper

Paper

Simulating the x-ray image contrast to setup techniques with desired flaw detectability

Ajay Koshti

Proceedings Volume 9437, 94370W (2015) https://doi.org/10.1117/12.2083225

KEYWORDS: X-rays, Sensors, Modulation transfer functions, X-ray detectors, X-ray imaging, Radiography, Data modeling, Modulation, Neodymium, Image quality standards

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Proceedings Article | 1 April 2015 Paper

Paper

Infrared contrast data analysis method for quantitative measurement and monitoring in flash infrared thermography

Ajay Koshti

Proceedings Volume 9437, 94370X (2015) https://doi.org/10.1117/12.2083244

KEYWORDS: Thermography, Signal attenuation, Infrared imaging, Cameras, Infrared cameras, Calibration, Data acquisition, Microscopy, Infrared radiation, Data analysis

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Proceedings Article | 1 April 2015 Paper

Paper

Considerations for ultrasonic testing application for on-orbit NDE

Ajay Koshti

Proceedings Volume 9437, 94372H (2015) https://doi.org/10.1117/12.2083245

KEYWORDS: Ultrasonics, Nondestructive evaluation, Inspection, Commercial off the shelf technology, Transducers, Neodymium, Aluminum, Phased arrays, Scanners, X-rays

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Proceedings Article | 1 April 2015 Paper

Paper

Ultrasonic measurement and monitoring of loads in bolts used in structural joints

Ajay Koshti

Proceedings Volume 9437, 94370T (2015) https://doi.org/10.1117/12.2083220

KEYWORDS: Ultrasonics, Transducers, Computer simulations, Aerospace engineering, Ray tracing, Reflectors, Bridges, Inspection, Solids, Beam analyzers

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Proceedings Article | 9 March 2014 Paper

Paper

Modeling the x-ray process and x-ray flaw size parameter for POD studies

Ajay Koshti

Proceedings Volume 9063, 90631C (2014) https://doi.org/10.1117/12.2044677

KEYWORDS: X-rays, Process modeling, Nondestructive evaluation, Calibration, Reliability, X-ray imaging, X-ray detectors, Sensors, Computer simulations

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Proceedings Article | 18 April 2006 Paper

Paper

Status of thermal NDT of space shuttle materials at NASA

K. Elliott Cramer, William Winfree, Kenneth Hodges, Ajay Koshti, Daniel Ryan, Walter Reinhardt

Proceedings Volume 6205, 62051B (2006) https://doi.org/10.1117/12.669684

KEYWORDS: Inspection, Thermography, Nondestructive evaluation, Principal component analysis, Imaging systems, Defect detection, Photography, Infrared cameras, Image processing, Cameras

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Proceedings Article | 11 June 2002 Paper

Paper

Estimating temperature rise in pulsed thermography using irreversible temperature indicators

Ajay Koshti

Proceedings Volume 4702, (2002) https://doi.org/10.1117/12.469878

KEYWORDS: Temperature metrology, Lamps, Thermography, Nondestructive evaluation, Visible radiation, Infrared cameras, Inspection, Cameras, Sensors, Adhesives

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Proceedings Article | 11 June 2002 Paper

Paper

Simulation of effect of bending stress on the ultrasonic beam

Ajay Koshti

Proceedings Volume 4702, (2002) https://doi.org/10.1117/12.469874

KEYWORDS: Ultrasonics, Transducers, Ultrasonography, Optical simulations, Numerical analysis, Differential equations, Distance measurement, Ray tracing, Reflectors, Beam analyzers

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Proceedings Article | 11 June 2002 Paper

Paper

Simulation of ultrasonic preload measurement on a bolt in an interference fit joint

Ajay Koshti

Proceedings Volume 4702, (2002) https://doi.org/10.1117/12.469904

KEYWORDS: Ultrasonics, Tellurium, Temperature metrology, Head, Ultrasonography, Time metrology, Calibration, Dubnium, Transducers, Computer simulations

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Proceedings Article | 11 June 2002 Paper

Paper

Simulation of ultrasonic measurement on a bolt in a shear joint

Ajay Koshti

Proceedings Volume 4702, (2002) https://doi.org/10.1117/12.469903

KEYWORDS: Ultrasonics, Transducers, Reflectors, Phase shifts, Crystals, Ultrasonography, Nondestructive evaluation, Optical simulations, Device simulation, Commercial off the shelf technology

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Proceedings Article | 24 July 2001 Paper

Paper

Effect of bending on ultrasonic preload measurements in bolts

Ajay Koshti

Proceedings Volume 4335, (2001) https://doi.org/10.1117/12.434168

KEYWORDS: Ultrasonics, Transducers, Reflectors, Distortion, Distance measurement, Signal attenuation, Nondestructive evaluation, Superposition, Phase shifts, Signal to noise ratio

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Proceedings Article | 24 July 2001 Paper

Paper

Estimation of accuracy in ultrasonic preload measurements

Ajay Koshti

Proceedings Volume 4335, (2001) https://doi.org/10.1117/12.434186

KEYWORDS: Ultrasonics, Tolerancing, Calibration, Transducers, Temperature metrology, Ultrasonography, Error analysis, Picosecond phenomena, Manufacturing, Statistical analysis

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Proceedings Article | 7 July 1995 Paper

Paper

Preload measurement in sleeve bolts using an ultrasonic technique

Ajay Koshti

Proceedings Volume 2455, (1995) https://doi.org/10.1117/12.213557

KEYWORDS: Ultrasonics, Head, Ultrasonography, Calibration, Temperature metrology, Velocity measurements, Manufacturing, Transducers, Aerospace engineering, Time metrology

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Showing 5 of 32 publications

Conference Committee Involvement (17)

Health Monitoring of Structural and Biological Systems XVIII

25 March 2024 | Long Beach, California, United States

Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVIII

25 March 2024 | Long Beach, California, United States

Health Monitoring of Structural and Biological Systems XVII

13 March 2023 | Long Beach, California, United States

Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVII

13 March 2023 | Long Beach, California, United States

Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVI

7 March 2022 | Long Beach, California, United States

Health Monitoring of Structural and Biological Systems XVI

7 March 2022 | Long Beach, California, United States

Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XV

22 March 2021 | Online Only, California, United States

Health Monitoring of Structural and Biological Systems XV

8 March 2021 | Online Only, California, United States

Health Monitoring of Structural and Biological Systems XIV

27 April 2020 | Online Only, California, United States

Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XIV

27 April 2020 | Online Only, California, United States

Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XIII

4 March 2019 | Denver, Colorado, United States

Health Monitoring of Structural and Biological Systems XIII

4 March 2019 | Denver, Colorado, United States

Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XII

5 March 2018 | Denver, Colorado, United States

Health Monitoring of Structural and Biological Systems XII

5 March 2018 | Denver, Colorado, United States

Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XI

26 March 2017 | Portland, Oregon, United States

Health Monitoring of Structural and Biological Systems XI

26 March 2017 | Portland, Oregon, United States

Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure X

21 March 2016 | Las Vegas, Nevada, United States

Showing 5 of 17 Conference Committees

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