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Operational NDT Simulations Involving Real Operators

In aeronautics, the reliability of nondestructive testing (NDT) procedures is quantified through probability of detection (POD) studies. In this approach, inspection results are collected for different defect sizes and then a statistical analysis is performed on the inspection results to estimate the probability to detect a defect with regard to its dimension. To get a consistent estimation, a large number of inspection results is required and makes the POD studies complex and expensive to implement. Typically, several inspectors are asked to inspect several samples containing discontinuities. In the last 15 years, the approach of using simulation to provide data and to lower the cost of such POD campaigns has been developed and is known under the acronym MAPOD (model-assisted POD). A major limitation of this approach is related to its ability to properly account for human factors. To take a step further, the idea of developing an “operational NDT simulator” involving human operations and simulated data, as a flight simulator does to train pilots, has arisen. In this paper, we describe this concept and show recent results of an early prototype dedicated to ultrasonic inspections of composite materials. The concept is reviewed in the first section. Then, the technical challenges of its implementation for ultrasound NDT are described. Finally, the human perception of the operational NDT simulation is evaluated, showing the performance of the current prototype.


Calmon, P., S. Mahaut, S. Chatillon, and R. Raillon, 2006, “CIVA: An Expertise Platform for Simulation and Processing NDT Data,” Ultrasonics, Vol. 44, pp. e975–e979.

Dong, Y., S. Lefebvre, X. Tong, and G. Drettakis, 2008, “Lazy Solid Texture Synthesis,” Computer Graphics Forum, Vol. 27, No. 4, pp. 1165–1174.

Dominguez, N., and D. Simonet, 2014, “Method of Simulating Operations of Non-Destructive Testing under Real Conditions using Synthetic Signals,” US Patent No. US20140047934A1, 20 February 2014; available at

Efros, A.A., and T.K. Leung, 1999, “Texture Synthesis by Non-Parametric Sampling,” IEEE International Conference on Computer Vision, Corfu, Greece, doi: 10.1109/ICCV.1999.790383.

Guibert, F., M. Rafrafi, D. Rodat, E. Prothon, N. Dominguez, and S. Rolet, 2016, “Smart NDT Tools: Connection and Automation for Efficient and Reliable NDT Operations,” 19th World Conference on Non-Destructive Testing, Munich, Germany.

Pati, Y.C., R. Rezaiifar, and P.S. Krishnaprasad, 1993, “Orthogonal Matching Pursuit: Recursive Function Approximation with Applications to Wavelet Decomposition,” Proceedings of 27th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, doi: 10.1109/ACSSC.1993.342465.

Rasmussen, C.E., and C.K.I. Williams, 2006, Gaussian Processes for Machine Learning, The MIT Press, Cambridge, MA.

Rodat, D., F. Guibert, N. Dominguez, and P. Calmon, 2018a, “Data-Driven Modelling Approaches Combined to Physical Models for Real-Time Realistic Simulations,” 12th European Conference on Non-Destructive Testing, Gothenburg, Sweden.

Rodat, D., F. Guibert, N. Dominguez, and P. Calmon, 2018b, “Introduction of Physical Knowledge in Kriging-Based Meta-Modelling Approaches Applied to Non-Destructive Testing Simulations,” Simulation Modelling Practice Theory, Vol. 87, pp. 35–47.

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