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Void nucleation, growth, and coalescence in A356 aluminum notch specimens was determined from a combination of experiments, finite element analysis, nondestructive analysis, and image analysis. Notch Bridgman tension experiments were performed on specimens to failure and then other specimens were tested to 90%, 95%, and 98% of the failure load. The specimens were evaluated with nondestructive X-ray tomography and optical image analysis. Finite element simulations of the notch tests were performed with an elastic–plastic internal state variable material model that incorporated the pertinent microstructures (silicon particle volume fraction and size distribution and porosity volume fraction and size distribution). Parametric finite element simulations were performed to give insight into various initial conditions and responses of the notch tensile bars. The various methods all corroborated the same damage progression.

Traditional algorithms for automatic target cueing (ATC) in hyperspectral images, such as the RX algorithm, treat anomaly detection as a simple hypothesis testing problem. Each decision threshold gives rise to a different set of anomalous pixels. The clustered Rx algorithm generates target cues by grouping anomalous pixels into spatial clusters, and retaining only those clusters that satisfy target specific spatial constraints. It produces one set of target cues for each of several decision thresholds, and conservatively requires {Omicron}(K{sup 2}) operations per pixel, where K is the number of spectral bands (which varies from hundreds to thousands in hyperspectral images). A novel ATC algorithm, known as ''Pixel Cluster Cueing'' (PCC), is discussed. PCC groups pixels into clusters based on spectral similarity and spatial proximity, and then selects only those clusters that satisfy target-specific spatial constraints as target cues. PCC requires only {Omicron}(K) operations per pixel, and it produces only one set of target cues because it is not an anomaly detection algorithm, i.e., it does not use a decision threshold to classify individual pixels as anomalies. PCC is compared both computationally and statistically to the RX algorithm.

An x-ray tomography system is being developed for high resolution inspection of large objects. The goal is to achieve 25 micron resolution over object sizes that are tens of centimeters in extent. Typical objects will be metal in composition and therefore high energy, few MeV x-rays will be required. A proof-of-principle system with a limited field of view has been developed. Preliminary results are presented.

We demonstrated dual-band infrared (DBIR) thermal imaging at the Grass Valley Creek Bridges near Redding CA. DBIR thermal imaging is an enabling technology for rapid, reliable, bridge deck inspections while minimizing lane closures. bridge-deck inspections were conducted from a mobile DBIR bridge inspection laboratory during November 2-3, 1995. We drove this self-contained unit at limited highway speeds over 0.4 lane miles of bridge deck. Using two thermal IR bands, we distinguished delaminations from clutter. Clutter, or unwanted thermal detail, occurs from foreign materials or uneven shade on the bridge deck surface. By mapping the DBIR spectral- response differences at 3-5 {mu}m and 8-12 {mu}m, we removed foreign material clutter. By mapping the deck diurnal thermal inertia variations, we removed clutter from uneven shade. Thermal inertia is a bulk deck property, the square root of thermal conductivity x density x heat capacity. Delaminated decks have below-average thermal inertias, or above-average day-night temperature excursions. Compared to normal decks areas, delaminated deck areas were typically 2 or 3 {degrees}C warmer at noon, and 0.5{degrees}C cooler at night. The mobile DBIR bridge inspection laboratory is currently undergoing extensive testing to examine bridges by the Federal Highway Administration.

We discuss three-dimensional dynamic thermal imaging of structural flaws using dual-band infrared (DBIR) computed tomography. Conventional (single-band) thermal imaging is difficult to interpret. It yields imprecise or qualitative information (e.g., when subsurface flaws produce weak heat flow anomalies masked by surface clutter). We use the DBIR imaging technique to clarify interpretation. We capture the time history of surface temperature difference patterns at the epoxy-glue disbond site of a flash-heated lap joint. This type of flawed structure played a significant role in causing damage to the Aloha Aircraft fuselage on the aged Boeing 737 jetliner. The magnitude of surface-temperature differences versus time for 0.1 mm air layer compared to 0.1 mm glue layer, varies from 0.2 to 1.6 degree(s)C, for simultaneously scanned front and back surfaces. The scans are taken every 42 ms from 0 to 8 s after the heat flash. By ratioing 3 - 5 micrometers and 8 - 12 micrometers DBIR images, we located surface temperature patterns from weak heat flow anomalies at the disbond site and remove the emissivity mask from surface paint of roughness variations. Measurements compare well with calculations based on TOPAX3D, a three-dimensional, finite element computer model. We combine infrared, ultrasound and x-ray imaging methods to study heat transfer, bond quality and material differences associated with the lap joint disbond site.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

We discuss dual-band infrared (DBIR) capabilities for imaging buried object sites. We identify physical features affecting thermal contrast needed to distinguish buried object sites from undisturbed sites or surface clutter. Apart from atmospheric transmission and system performance, these features include: object size, shape, and burial depth; ambient soil, disturbed soil and object site thermal diffusivity differences; surface temperature, emissivity, plant-cover, slope, albedo and roughness variations; weather conditions and measurement times. We use ground instrumentation to measure the time-varying temperature differences between buried object sites and undisturbed soil sites. We compared near surface soil temperature differences with radiometric infrared (IR) surface temperature differences recorded at 4.7 +/- 0.4 micrometers and at 10.6 +/- 1.0 micrometers . By producing selective DBIR image ratio maps, we distinguish temperature-difference patterns from surface emissivity effects. We discuss temperature differences between buried object sites, filled hole sites (without buried objects), cleared (undisturbed) soil sites, and grass-covered sites (with and without different types of surface clutter). We compared temperature, emissivity-ratio, visible and near-IR reflectance signatures of surface objects, leafy plants and sod. We discuss the physical aspects of environmental, surface and buried target features affecting interpretation of buried targets, surface objects and natural backgrounds.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

We discuss the use of dual-band infrared (DBIR) imaging for three quantitative NDE applications: location buried surrogate mines, mapping sea ice thicknesses and inspecting subsurface flaws in aging aircraft parts. Our system of DBIR imaging offers a unique combination of thermal resolution, detectability, and interpretability. Pioneered at Lawrence Livermore Laboratory, it resolves 0.2 {degrees}C differences in surface temperatures needed to identify buried mine sites and distinguish them from surface features. It produces both surface temperature and emissivity-ratio images of sea ice, needed to accurately map ice thicknesses (e.g., by first removing clutter due to snow and surface roughness effects). The DBIR imaging technique depicts subsurface flaws in composite patches and lap joints of aircraft, thus providing a needed tool for aging aircraft inspections.