Identification of Depth and Size of Subsurface Defects by a Multiple-Voltage Probe Sensor: Analytical and Neural Network Techniques

A theoretical study was conducted using a multiple-voltage probe sensor for detecting nonconducting inclusions in conducting media. Results show that the multiple-voltage probe sensor is capable of providing precise quantitative measurements of submerged nonconducting objects if the surface voltage response has a standard two-peak form. The standard response is observed for well-localized non-slender single inclusions below the sensor surface. In this case, the peak separation distance is associated with the inclusion depth whereas the peak magnitude is associated with the inclusion volume. Linear dependencies of the inclusion depth and the inclusion volume are observed for a wide variety of inclusion shapes. The predefined form of the surface voltage response makes it feasible to identify useful signal responses at very high noise levels. This is accomplished by using a 2D neural network classifier, based on the probabilistic neural network. A reasonable recognition error of less than 20 % is obtained if the signal-to-noise ratio is larger than or equal to 1/10. A metal casting example shows that the multiple-voltage probe sensor can measure inclusion concentrations in hot conducting melts (gas bubbles and sludge) with inclusion radii in the range from 100 to 1000 μm. In contrast to existing particle counter technology, this sensor construction is simple to construct and does not require special aperture and vacuum treatment.