Model-based software for simulating ultrasonic pulse/echo inspections of metal components

Under the sponsorship of the National Science Foundation’s Industry/University Cooperative Research Center at Iowa State University, an effort was initiated in 2015 to repackage existing research-grade software into user friendly tools for the rapid estimation of signal-to-noise ratio (S/N) for ultrasonic inspections of metals. The software combines: (1) a Python-based graphical user interface for specifying an inspection scenario and displaying results; and (2) a Fortran-based engine for computing defect signals and backscattered grain noise characteristics. The later makes use the Thompson-Gray Model for the response from an internal defect and the Independent Scatterer Model for backscattered grain noise. This paper provides an overview of the ongoing modeling effort with emphasis on recent developments. These include: treatment of angle-beam inspections, implementation of distance-amplitude corrections, changes in the generation of “invented” calibration signals, efforts to simulate ultrasonic C-scans; and experimental testing of model predictions. The simulation software can now treat both normal and oblique-incidence immersion inspections of curved metal components having equiaxed microstructures in which the grain size varies with depth. Both longitudinal and shear-wave inspections are treated. The model transducer can either be planar, spherically-focused, or bi-cylindrically-focused. A calibration (or reference) signal is required and is used to deduce the measurement system efficiency function. This can be “invented” by the software using center frequency and bandwidth information specified by the user, or, alternatively, a measured calibration signal can be used. Defect types include flat-bottomed-hole reference reflectors, and spherical pores and inclusions. Simulation outputs include estimated defect signal amplitudes, root-mean-squared grain noise amplitudes, and S/N as functions of the depth of the defect within the metal component. At any particular depth, the user can view a simulated A-scan displaying the superimposed defect and grain-noise waveforms. The realistic grain noise signals used in the A-scans are generated from a set of measured “universal” noise signals whose strengths and spectral characteristics are altered to match predicted noise characteristics for the simulation at hand. We present simulation examples demonstrating recent developments, and discuss plans to improve simulator capabilities.