BASELINE UT MEASUREMENTS FOR ARMOR INSPECTION
Some prototype armor panels are fabricated from several layers of dissimilar material bonded together. These may include ceramics, graphite composites, fiberglass composites and rubber. The ultrasonic properties of these layers influence inspections for armor defects. In this paper we describe measurements of ultrasonic velocity, attenuation, sound beam distortion and signal fluctuations for the individual layers comprising one armor prototype. We then discuss how knowledge of these properties can be used when choosing an optimum frequency for an ultrasonic pitch/catch immersion inspection. In our case an effective inspection frequency near 1.5 MHz affords: (1) adequate strength of through‐transmitted signals in unflawed armor; (2) adequate lateral resolution for detecting small disbonds at interfaces; and (3) low levels of UT signal fluctuations due to the natural inhomogeneity of certain armor layers. The utility of this approach is demonstrated using armor panels containing artificial disbonds at selected interfaces.Some prototype armor panels are fabricated from several layers of dissimilar material bonded together. These may include ceramics, graphite composites, fiberglass composites and rubber. The ultrasonic properties of these layers influence inspections for armor defects. In this paper we describe measurements of ultrasonic velocity, attenuation, sound beam distortion and signal fluctuations for the individual layers comprising one armor prototype. We then discuss how knowledge of these properties can be used when choosing an optimum frequency for an ultrasonic pitch/catch immersion inspection. In our case an effective inspection frequency near 1.5 MHz affords: (1) adequate strength of through‐transmitted signals in unflawed armor; (2) adequate lateral resolution for detecting small disbonds at interfaces; and (3) low levels of UT signal fluctuations due to the natural inhomogeneity of certain armor layers. The utility of this approach is demonstrated using armor panels containing artificial disbonds at selec...
Determination of Density Variation and Microstructure in Reaction‐sintered SiC Ceramics Using Ultrasonic Time‐of‐Flight
Ultrasonic time-of-flight (TOF) is investigated as a predictor of density variation across reaction-sintered silicon carbide (SiC) ceramics. The importance of this research is heightened by the fact that the reaction-sintered SiC ceramic tiles being investigated are manufactured using the reaction sintering process that involves the infusion of liquid silicon into a porous ceramic preform. This can potentially lead to the formation of islands of free silicon, small closed areas of un-sintered silicon material as well as conventional porosity. All these defects can result in local variations of density which cannot be detected by conventional bulk density measurement techniques. To study the microstructural differences, the porosity dependence of ultrasonic TOF of the reflected signals was investigated to establish a correlation between the velocity and density across the ceramic tile that aids in characterizing the material. The data suggest that for these ceramic tiles, TOF C-scan mapping surfs as a much better indicator of sample homogeneity than the amplitude of the back wall echo. At the current time, ceramic tiles are inspected offline and this takes time and effort and is very expensive. In particular, this study contains the procedure followed to establish a quantitative method to quantify density variation across selected ceramic tile.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.