This study presents a novel method to quantify the effectiveness of wet magnetic particle inspection (MPI) when detecting possible defects. Wet MPI is an established method utilizing magnetic fields to locate possible areas of defects in ferromagnetic parts. The capability of this method has been evaluated in the past most notably using the probability of detection graphs. However, MPI requires a large amount of data and is subjective because it is based on human operators’ evaluation. The method proposed in this paper is an objective method to determine the effectiveness of the MPI test based on how well discontinuities can be delineated in the image. This approach utilizes the intensity of the particle illumination in the defect area and compares it to its surroundings. This analysis generates a value to objectively represent how well a discontinuity can be identified. This method was then used to validate the effect of surface roughness on the effectiveness of wet MPI using two experiments. The first experiment was conducted to test for the collection of particles on varying surface roughness levels, and the second experiment was used to evaluate the effect of surface roughness when detecting a subsurface discontinuity. Results indicate that there is a significant increase in particle collection as roughness increases, and as the surface roughness increases, the harder it is to locate discontinuities. This method provides a quantitative measure that could be used to aid parameter selection.

The authors recently investigated the use of conductive concrete to enhance nondestructive evaluation (NDE) capa- bilities. Preliminary results have shown that a conductive concrete can facilitate the utilization of an eddy current technique, where damages in a conductive specimen were easier to detect compared with a non-conductive substrate. While such results demonstrated the promise of using conductive concrete to facilitate and potentially accelerate the NDE process, the fabrication of an homogeneous conductive concrete is technically or economically challenging, depending on the conductive filler used in the process. In this paper, we propose a new cementitious composite to accelerate NDE. The composite uses inexpensive carbon black particles (CB) and a block-copolymer. The purpose of the block co-polymer, a styrene-ethylene-butylene-styrene (SEBS), is to facilitate the creation of conductive chains, therefore reducing the necessary concentration of conductive filler required to achieve electrical percolation. Several cementitious composite specimens of various concentrations of CB are fabricated, and results show that the utilization of SEBS reduces the electrical percolation threshold by approximately 50% with a gain on electrical conductivity relative to a non-conductive specimen mix of approximately 33%. Strain-sensing tests also demonstrate that SEBS-based specimens have good sensing properties, but lag behind those of conductive concrete specimens fabricated with CB only.

Technical challenges exist with infrastructure that can be addressed by nondestructive evaluation (NDE) methods, such as detecting corrosion damage to reinforcing steel that anchor concrete bridge railings to bridge road decks. Moisture and chloride ions reach the anchors along the cold joint between the rails and deck, causing corrosion that weakens the anchors and ultimately the barriers.

The Center for Nondestructive Evaluation at Iowa State University has experience in development of measurement techniques and new sensors using a variety of interrogating energies. This research evaluated feasibility of three technologies—x-ray radiation, ground-penetrating radar (GPR), and magnetic flux leakage (MFL)—for detection and quantification of corrosion of embedded reinforcing steel.

Controlled samples containing pristine reinforcing steel with and without epoxy and reinforcing steel with 25 percent and 50 percent section reduction were embedded in concrete at 2.5 in. deep for laboratory evaluation. Two of the techniques, GPR and MFL, were used in a limited field test on the Iowa Highway 210 Bridge over Interstate 35 in Story County.

The methods provide useful and complementary information. GPR provides a rapid approach to identify reinforcing steel that has anomalous responses. MFL provides similar detection responses but could be optimized to provide more quantitative correlation to actual condition. Full implementation could use either GPR or MFL methods to identify areas of concern, followed by radiography to give a visual image of the actual condition, providing the final guidance for maintenance actions.

The transfer of microstamped identifiers to the primers of fired cartridges was examined using a stereomicroscope and scanning electron microscope (SEM). The identifiers were placed on the firing pins of three different 9mm handguns, namely, a Sig Sauer, a Taurus, and a Hi-Point. Ten different brands of ammunition were fired from each handgun, 100 rounds being fired using each brand for a total of 1000 rounds fired per handgun. The quality of the markings was evaluated using a simple observation rubric. These results were compared to Vickers hardness values obtained from the ammunition primers and are discussed in light of the types of actions of firearms used.

Planting depth (PD) plays an essential role in crop production by substantially impacting germination rates and yield potential. However, techniques to measure PD nondestructively have not been developed. A two-dimensional gprMax simulation study was conducted to investigate the effects of soil electromagnetic properties on ground-penetrating radar (GPR) waves. The primary objective was to examine the possibility of using GPR as a nondestructive sensor to detect subsurface corn seeds with the goal of measuring PD. A conventional fixed-offset gprMax antenna in contact with the soil surface was used in the simulations. Corn seed models of different materials and sizes were simulated, with properties of natural and synthetic (metal) corn seeds. The seed models were spherical, with radial dimensions of 0.006 and 0.024 m to simulate small and large corn seeds, respectively. Corn seed models were embedded in three homogeneous soil models (sandy loam, loam, and clay), and 1.6 and 2.6 GHz antenna models were used as excitation frequencies. A-scans and B-scans were obtained from the simulations. The A-scans showed that all targets (small natural corn and metal corn models, and large natural corm and metal corn models) successfully provided response amplitudes proportional to their dielectric properties in sandy loam and loam, but not in clay. In high bulk density soils, GPR waves failed to penetrate the soil models, and the targets were not detected. The 2.6 GHz antenna provided better response amplitudes from the targets. In the driest soil models (2.5%, and 5%), no response amplitude signatures were observed. In dry and relatively dry soil models (15%), the simulation times were much shorter to obtain a response amplitude from the targets (with feeble response amplitudes) compared to relatively wetter soils. To validate these models, laboratory experiments were conducted with three treatment factors (soil type, planting depth, and moisture content). In dry soils, corn seeds could be detected using a 2.6 GHz GPR antenna; however, the detection varied substantially within replicates of the same moisture group. Further research is necessary to understand the effects of soil moisture on the detection variability of buried corn seeds.

In the design of exhaust manifolds for internal combustion engines the materials used must exhibit resistance to corrosion at high temperatures while maintaining a stable microstructure. Cast iron has been used for manifolds for many years by auto manufacturers due to a combination of suitable mechanical properties, low cost, and ease of casting. Over time cast iron is susceptible to microstructural changes, corrosion, and oxidation which can result in failure due to fatigue. This thesis seeks to answer the question: “Can observed microstructural changes and measured high temperature fatigue life in cast iron alloys be used to develop a predictive model for fatigue life?” the importance of this question lies in the fact that there is little data for the behavior of cast iron alloys at high temperature. For this study two different types of cast iron, 50HS and HSM will be examined. Of particular concern for the high Si+C cast irons (and Mo in the case of the HSM cast iron) are subsurface microstructural changes that result due to heat treatment including (1) decarburization, (2) ferrite formation, (3) graphitization, (4) internal oxidation of the Si, (5) high temperature fatigue resistance, and (6) creep potential. Initial results obtained include microstructure examination after being exposed to high temperatures, grain size, nodule size, and hardness measurements. The initial examinations concluded that both cast irons performed fairly similarly, although the microstructure of the HSM samples did show slightly better resistance to high temperature as compared to that of the 50HS. Follow on work involved high temperature fatigue testing of these two materials in order to better determine if the newer alloy, HSM is a better choice for exhaust manifolds. Correlations between fatigue performance and microstructure were made and discussed, with the results examined in light of current and proposed models for predicting fatigue performance based on computational methods, to see if any suitable models exist that might be used to assist in designing with these cast alloys.

Often, the 3-D raw data coming from an optical profilometer contains spiky noises and irregular grid, which make it difficult to analyze and difficult to store because of the enormously large size. This paper is to address these two issues for an optical profilometer by substantially reducing the spiky noise of the 3-D raw data from an optical profilometer, and by rapidly re-sampling the raw data into regular grids at any pixel size and any orientation with advanced computer graphics tools. Experimental results will be presented to demonstrate the effectiveness of the proposed approach.

The effects of seed spacing and depth at planting contribute greatly to corn production. Correct seed spacing, and planting depth may enable moisture absorption which facilitates seedling emergence with the establishment of healthy and robust root structure. Therefore, measuring seed spacing and planting depths in a closed furrow is necessary for precision seeding and corn production. In agricultural applications, Ground Penetrating Radar (GPR) is used for nondestructive evaluations as a potential sensor to maximize the qualitative and precision or repeatable assessments in long-term research. Yet GPR system has not been used to measure seed spacing and planting depths, but it has the potential to measure the two parameters. The objective of this experimental research was to use a non-destructive 2.6 GHz GPR system to detect agricultural Corn Seeds (CS) buried at different depths (3.81, 6.35, and 8.89 cm) and spacing (15.24 and 25.4 cm) in sandy-loam and loam soils. The data was processed using the Fast Discrete Curvelet Transform to denoise and enhance edge responses from CS. In bone-dry soils some CS were detected, while in intermediate and moist soils it was difficult to detect CS. The two-way travel time in nanoseconds and soil dielectric permittivity from experimental data were used to estimate planting depth while the spatial distance between the CS was computed from the antenna cart encoder. The Topp‘s dielectric, soil mixing, and the Topp-Mixing (TM) model were used to estimate the soil dielectric permittivity. The TM model was developed as a function of the Topp‘s dielectric, and soil mixing models to minimize and optimize planting depth error (PDE). The TM model was found to be effective in predicting permittivity used to approximate planting depth with minimal PDE. The assessment of the 2.6 GHz antenna effectiveness was based on the percent coefficient of precision (CP3) and coefficient of planting depth accuracy (CPDA). The CP3 values were < 30% but differed for the three moisture groups and soil types. The TM model had the best CPDA of 9.9%. While the results are promising, more research is needed to enable detection and depth measurements of CS in soil conditions that are typical of a ploughed field.

Magnetic particle inspection (MPI) has been widely utilized for decades, and sees considerable use in the aerospace industry with 90% of the steel parts being inspected with MPI at some point in the lifecycle. Typical aircraft locations inspected are landing gear, engine components, attachment hardware, and doors. In spite of its numerous applications the method remains poorly understood, and there are many aspects that would benefit from in-depth study. This shortcoming is due to the fact that MPI combines the complicated nature of electromagnetics, metallurgical material effects, fluid-particle motion dynamics, and physiological human factors into a single inspection. To promote understanding of the intricate method issues that affect sensitivity, or would assist with the revision of industry specifications and standards, research studies will be prioritized through the guidance of a panel of industry experts. This approach has worked successfully in past fluorescent penetrant inspection (FPI) research efforts[i].

The magnetic particle inspection technique has been used for many years for aviation applications, but unfortunately very few aids exist that assist in proper test setup. There are many ‘rule-of-thumb’ equations available to calculate current settings for a given sample geometry, but very often this results in gross over-magnetization and reduced sensitivity. Further, magnetic particle test specifications prescribe current values, which are affected by the control waveforms used for regulating the current intensity. This introduces harmonics in the waveforms, which makes it difficult to establish a relationship between peak and rms values of a current waveform, which is important in the practical use of MPI. Each of the waveforms has its own characteristics and interactions between leakage fields at discontinuities and the particles can vary significantly. It is therefore possible to miss the detection of defects by choosing inappropriate current waveforms. In recent Air Transport Association NDT Forums, the airlines have identified the need for additional research to support fundamental understanding of the MPI technique and the factors which affect sensitivity. Of particular concern is the direction “complete 100% magnetic particle inspection” without specific guidance on setup parameters which is common in OEMprocedures. Another issuewith overwhelming support is the impact of coatings and platings on MPI sensitivity.

In this paper we investigate the use of ground penetrating radar (GPR) to detect corrosion-induced thinning of rebar in concrete bridge structures. We consider a simple pulse/echo amplitude-based inspection, positing that the backscattered response from a thinned rebar will be smaller than the similar response from a fully-intact rebar. Using a commercial 1600-MHz GPR system we demonstrate that, for laboratory specimens, backscattered amplitude measurements can detect a thinning loss of 50% in rebar diameter over a short length. GPR inspections on a highway bridge then identify several rebar with unexpectedly low amplitudes, possibly signaling thinning. To field a practical amplitude-based system for detecting thinned rebar, one must be able to quantify and assess the many factors that can potentially contribute to GPR signal amplitude variations. These include variability arising from the rebar itself (e.g., thinning) and from other factors (concrete properties, antenna orientation and liftoff, etc.). We report on early efforts to model the GPR instrument and the inspection process so as to assess such variability and to optimize inspections. This includes efforts to map the antenna radiation pattern, to predict how backscattered responses will vary with rebar size and location, and to assess detectability improvements via synthetic aperture focusing techniques (SAFT).