Ebook: Electromagnetic Nondestructive Evaluation XXII
The use of electromagnetic nondestructive evaluation has grown significantly in recent years. This valuable technique enables the assessment of objects by observing the electromagnetic response to electric currents and/or magnetic fields introduced within them.
This book presents the proceedings of the 23rd International Workshop on Electromagnetic Nondestructive Evaluation (ENDE2018), held in Detroit, Michigan, USA, from 9 - 13 September 2018. The workshop provides an international forum for the exchange of information on state-of-the-art technologies and development in electromagnetic nondestructive evaluation, and the 19 papers presented here cover topics including sensors; modeling; signal processing; inverse problems; materials state awareness and characterization; damage diagnosis and prognosis; biomedical applications; and innovative industrial applications of eNDE.
Providing a comprehensive overview of current theoretical and applied research into electromagnetic nondestructive evaluation (eNDE) methods, the book will be of interest to all those whose work involves the non-destructive evaluation of objects, whatever their field.
The 23rd International Workshop on Electromagnetic Nondestructive Evaluation (ENDE2018) took place on September 9 to 13, 2018 at the Detroit Marriott Renaissance Center in Downtown Detroit, Michigan, USA. The workshop was organized by the Nondestructive Evaluation Laboratory (NDEL) at Michigan State University (MSU) and co-sponsored by the Japanese Society of Maintenology, American Chemistry Council, HMT Robot Limited Liability Partnership in Japan, and the College of Engineering at MSU. About 70 experts from more than 10 countries worldwide in nondestructive evaluation gathered at the workshop.
Electromagnetic nondestructive evaluation has grown considerably in recent years. This year’s technical sessions, student posters competition and special ENDE events provided a comprehensive and well-balanced program in theoretical and applied research of electromagnetic NDE (eNDE) methods. This workshop is intended to provide an international forum for the exchange of information on state-of-the-art technologies and development in NDE sensors, modeling, signal processing, inverse problems, materials state awareness and characterization, damage diagnosis and prognosis, biomedical applications, and innovative industrial applications of eNDE. Through a range of technical and social activities, ENDE2018 provided all the attendees a unique opportunity to interact with the world’s leading experts in electromagnetic NDE from academia, industry and government. Members of the organizing committee included MSU Executive Vice President for Administrative Services Satish Udpa and the following from the MSU Department of Electrical and Computer Engineering – Associate Professor Yiming Deng, Professor Lalita Udpa, Associate Professor Ming Han, Assistant Professor Sunil Chakrapani, and Professor Antonello Tamburrino of the Università degli Studi di Cassino e del Lazio Meridionale, Italy, and MSU.
Yiming Deng, the general chair of workshop, Satish S. Udpa and Engineering Dean Leo Kempel welcomed all the attendees to the conference and provided introductory remarks to open the workshop. The technical program commenced with three plenary talks: “Electromagnetic NDE and the US Air Force: an Overview” by Dr. Eric Lindgren, Nondestructive Evaluation Technology Lead in the Materials State Awareness Branch of the Materials and Manufacturing Directorate of the Air Force Research Laboratory (AFRL); “U.S. Army TARDEC NDE Efforts” by Dr. David J. Gorsich, Chief Scientist, U.S. Army Tank Automotive Research, Development and Engineering Center; and “The Potential of Voluntary Information Sharing and Nondestructive Evaluation for Pipeline Safety” by Dr. Sherry Borener, Chief Data Officer and Senior Research Advisor at U.S. Department of Transportation. Three distinguished speakers from the aerospace, vehicle and gas pipeline industries were invited to discuss challenges confronting them and the ways in which NDE can assist in addressing these issues. An invited talk entitled “Innovation in a Regulated Space” was given by Joshua Arnold, R&D Program Manager, PHMSA, U.S. Department of Transportation, discussing the Office of Pipeline Safety (OPS) process, successes, lessons learned, and future opportunities for partnerships with NDE community. About 70 contributed technical presentations were organized into eight oral sessions covering topics on: (i) Advanced eNDE sensors and sensing systems, (ii) Electromagnetics for aerospace materials, (iii) Inverse problems, data processing and big data applications, (iv) Analytical and numerical modeling of eNDE, (v) Methodology in damage diagnosis and prognosis, (vi) Advanced composite NDE, (vii) Materials state awareness and characterization, (viii) Hybrid NDE, plus two poster sessions. Several experts in the field, S.S. Udpa, H. Zhang, J. Arnold, T. Takagi, G.Y. Tian, L. Udpa, S. Mukherjee, C. Reboud, N. Yusa, T. Uchimoto, M. Cherry, Z. Chen, S. Chakrapani, O. Karpenko, A. Tamburrino, R. Miorelli and V.T. Rathod chaired the sessions. Every presentation was followed by stimulating discussions. The conference concluded with closing remarks by Satish S. Udpa and scientific program committee chair Antonello Tamburrino. Gui Yun Tian, the general chair of ENDE2019 announced the venue for the next workshop, which will be held in Chengdu, China.
Seventy participants registered for the conference. Short versions of all contributed papers were published in the Workshop digest electronically, and nineteen full papers were accepted after peer-reviews and are published in this processing book.
The organizers gratefully acknowledge the financial support of the sponsors. Thanks are due to the members of the Standing Committee, particularly Professors Yusa and Ramos and Dr. Reboud, Chair of the International Standing Committee, who provided much guidance and support through their successful ENDE experiences in recent past years. The Editors are also indebted to the referees, listed below, for their timely reviews of the paper. Lastly, we acknowledged the hard work of NDEL student volunteers, whose dedication and interactions with all ENDE participants ensured the smooth and successful operation of the ENDE2018 Workshop and administrative arrangements.
Antonello Tamburrino, Yiming Deng and Sunil Chakrapani
The electromagnetic acoustic resonance (EMAR) method using an electromagnetic acoustic transducer (EMAT) has been proposed to perform pipe wall thickness measurements. However, in some parts of pipes, identification of the resonance frequency can be a difficult task. In this paper, a new data processing method called multiplication of nth compression (MNC) is proposed to determine the resonance frequency. The performance of the MNC method is compared with that of the previously used autocorrelation function (ACF) and superposition of nth compression (SNC) methods in two experiments. Finally, a two-step data processing procedure for use in pipe wall thickness measurements is proposed. This procedure can improve the data processing accuracy.
Sonic Infrared Imaging (IR) technology is new to the NDT family; it is fast and wide area imaging method. It uses ultrasound as excitation source along with an infrared imaging system to observe the temperature change of the crack within the sample. This technology has shown the great capability to detect different types of defects in various materials. The ultrasound transducer generates a high energy ultrasonic pulse to be coupled to the test sample. The ultrasound transducer plays an important role in the SIR system; The occurrence of the nonlinear phenomenon and the resulting images are highly influenced by the choice of transducer size as well as the induced frequency. In this paper, we will present our study on the relationship between different transducer tip size with different single excitation frequency and energy consumption in the crack vicinity in Sonic Infrared Imaging.
Inspection of multi-layered riveted structures of aircraft and detection of subsurface cracks under fastener head is a major challenge in aviation industry. Magnetic field image (MFI) obtained with high sensitivity sensor array is widely used for inspection of this kind of defects. The magnetic field image contains defect information, from which the defect can be identified. Furthermore, in order to quantitatively analyze the degree of hazard of the defect to the aircraft, it is important to know the actual dimensions of the defects. In this paper, a method based on error back propagation (BP) training artificial neural networks (ANN) is proposed to estimate the defect dimensions from MFI. The output error of the network is decreased along the gradient direction by intelligently adjusting the connection strength and the threshold of the ANN using gradient descent method. It is demonstrated that the BP neural networks have a high accuracy for quantifying defects in multilayer rivet structure.
Scanning near-field microwave microscopy is a versatile tool to image the surface of materials, with sub-wavelength spatial resolution, in terms of the impedance at microwave frequencies. The microscopic impedance spectroscopy gives the nature of the surface in terms of the dielectric and magnetic properties. The retrieval of the dielectric and magnetic properties depends on the stand-off distance between the near-field microwave source emanating from the electrically small monopole antenna and the surface of the sample. To maintain the stand-off distance, a novel phase based feedback control is proposed and implemented in a flexure scanning stage. This paper presents the enhancement of the images obtained using this feedback mechanism.
In this work, the detection and characterization of sub-surface crack’s depths using GMR, TMR and uniform magnetic field distribution with multi-frequency excitation were studied. Experimental tests were performed in two stacked aluminum plates with their thickness equal to 3 mm and 4 mm. The multi-frequency signal was used to exploit the penetration of the eddy currents into the two stacked aluminum plates in different layers. A planar coil especially designed to produce a uniform magnetic field distribution around the magnetic field sensors was used to exploit the invariance of the excitation field for small spatial translations. As only low testing frequencies (up to 500 Hz) were chosen for the detection of the sub-surface cracks, GMR and TMR sensors were used to obtain the measurement of the magnetic field perturbation due to the presence of cracks in the two stacked aluminum plates. The obtained results and the characteristics of the two sensors are discussed in this paper.
We developed ECT systems using high-sensitivity magnetic sensors with small size; and used the systems to evaluate a titanium alloy sample prepared by a 3D laser printer. The magnetic sensor was constructed using an amorphous wire with the diameter of 0.1 mm and the length of 5 mm. The magnetic field resolution was about 30 pT/√Hz. For the ECT system, a small excitation coil was also wrapped around the amorphous wire, and the excitation frequency was chosen to be 100 kHz. The image of the scanning result shows that the ECT method was able to detect the positions of flaws in the test sample. We also compared the results with that of traditional ECT system with inductive coilprobe.
It is difficult for traditional magnetic flux leakage methods to detect inner surface defects of thick-walled steel pipe or plate due to magnetic shielding of the wall and strong magnetic background noise, and for eddy current testing as well due to its skin effect. Hence, the “perturbation” course of internal magnetic field variations is analyzed using magnetic dipole model and the mechanism of magnetic permeability perturbation in MB-ECT is revealed. The theoretical analysis and simulations show that the significant permeability perturbation always appears around the defect. A new nondestructive testing method based on permeability perturbation is proposed for the detection of corrosion in thick-walled pipe.
The magnetic leakage field of the crack gets weaker rapidly as the lift-off value increases, which makes the detection more difficult, especially for many insulated pipes. On the other hand, for eddy current testing, the AC magnetic flux of the excitation coil decreases exponentially with respect to the probe lift-off value, meanwhile, the surface eddy current density decreases rapidly according to the law of electromagnetic induction. To achieve the inspection in a large lift-off, a large life-off NDT method based on the interaction between AC Magnetic field and MFL field is proposed. A series of simulations and experiments are conducted to verify its feasibility.
This paper proposes a machine learning framework for efficient cracks localization and characterization in configurations of fastener inspections using surface eddy current probes. The learning algorithm relies on a supervised learning procedure. In this work, the training set is a simulated set of input/output couples. Furthermore, in order to assess variability associated to real inspection procedures, uncertainties impacting probe lift-off and tilt have been introduced in the inversion scheme in order to evaluate its robustness. Results obtained through a numerical validation campaign shown that both cracks localization and characterization are possible even when some uncertainties are taken into account. Impacts of uncertainties on the prediction accuracy as well as on the CPU time efficiency are finally discussed.
Ising model is a promising tool for simulation magnetic Barkhausen noise (MBN) signal for nondestructive evaluation. However, theoretical modeling of two-phase or multi-phase ferromagnetic materials are seldom reported by this model. In order to fill the blank, we extended Ising model combine with double Boltzmann transition function to simulated MBN signal of two-phase ferromagnetic materials. The volume fraction was considered as a vital parameter which effect the shape of MBN profiles directly. A continuous transition form was assumed and the proposed model was simulated by Monte Carlo algorithm. The second order Gaussian curve was used to fit the two-peak shape MBN profile. This simulation show that the first peak in lower field increased and the second peak in higher field gradually decreased with the first phase simulated volume fraction increases. In addition, the ratio feature about the peaks amplitude show a linear increased trend. The proposed model offer a feasible tool for predict MBN in two-phase materials.
The global sensitivity analysis of electromagnetic nondestructive evaluation (ENDE) is dealt with in this work. The goal is to calculate the Sobol’ indices, which quantitatively describe the sensitivity of the output(s) with respect to the input variability of the ENDE model. To reduce the computational burden, a recent surrogate modeling technique, the canonical low-rank approximation is applied. Studies are presented via representative examples from eddy-current and magnetic flux leakage nondestructive evaluation.
Subsurface corrosion in conductive structures severely influences the structural integrity. It is of great significance to carry out noninvasive evaluation of subsurface corrosion via effective nondestructive evaluation techniques. Gradient-field Pulsed Eddy Current technique (GPEC) has exhibited its feasibility in detection and evaluation of corrosion in conductors. Following previous investigation, in this paper a novel GPEC probe inducing net magnetic field of high Degree of Field Uniformity (DFU) is proposed for quantitative evaluation and imaging of subsurface corrosion. A 2D finite element model of the proposed probe is established to analyze the characteristics and DFU of the net magnetic field. Experiments regarding subsurface corrosion imaging via GPEC have been conducted. It has been found that the proposed probe is advantageous in terms of corrosion-edge recognition and evaluation of corrosion opening size. The corrosion depth can further be assessed by the derived images from the integral-based image processing algorithm.
The focal plane characterization of spot focusing horn antennas used for microwave NDE is presented using three experimental methods. Reflections from a metal sphere were analyzed in time domain to quantify the focal spot and spot size in the first method. In the second method, a rectangular waveguide padded with absorber was used as the sensor. Finally, graphene based miniaturized electric (E)-field sensor printed on photo paper using inkjet printing was used to measure antenna focal plane characteristics. The focal plane measured using the metal ball and waveguide techniques was within 5% of the simulated value. Higher error was recorded in focal spot measurement (≥25%) for the first two methods. The focal plane and spot size measured by the miniaturized E-field sensor was within 10% of the simulated antenna characteristics. The results indicate that the flexible printed sensor has better measurement accuracy and simple setup for field measurement.
For powder metallurgy nickel-based superalloys, non-metallic inclusions (NMIs) and non-metallic particles are frequently present. If an EC inspection can reliably classify NMI indications from crack indications, there would be significant payoff for the USAF. Progress is presented on the development of a model-based inversion scheme incorporating VIC-3D® EC NDE simulated results. Inversion results are presented highlighting recent work to demonstrate characterization of sub-surface volumetric indications.
Eddy current measurements of cracks in titanium can be corrupted by the presence of spatially correlated grain noise generated by the material. This grain noise complicates both the detection and characterization of cracks in titanium. A promising solution is to model the grain noise as a Gaussian process; this model was successfully applied in the characterization problem (see ). Here, a grid search algorithm which makes use of the noise model is developed to detect cracks obscured by grain noise. As in , the inverse problem is solved in the Bayesian framework. The algorithm is applied successfully to both simulated and experimental data for cracks in specimens of Ti-6Al-4V.
This paper presents progress on the characterization of discontinuities in multilayer fastener sites using bolt hole eddy current (BHEC) techniques with model-based inversion. Improvement to the inversion process have been made, however, some model discrepancy remains resulting in greater inversion error for smaller corner discontinuities. Sensitivity to probe liftoff and the calibration process was also observed during experimental testing, indicating the need to estimate and compensate for varying probe state and certain adjacent material conditions.
Sonic Infrared (SIR) NDE is a novel nondestructive testing technique that uses a short pulse of ultrasonic excitation along with infrared imaging to expose hidden defects in the structures being inspected. In , we presented a model that describes circular subsurface heat sources. This model can be used to describe heat diffusion from impact damage in composite structures during SIR inspection. The model uses certain aspects of the temperature-time curve for defect depth profiling, namely, half-maximum power point, the peak slope point, and the second derivative peak point. In this study, we investigate the effect of defect size on defect’s detectability and the effect of defect size on the quantitative estimation of defect’s depthspecifically, half-maximum power time.
The feasibility of non-contact and non-destructive evaluation (NDE) of planar aerospace dielectric composites using microwave is examined in this paper. Free space microwave measurement set up includes spot focusing horns to gather the scattering parameters of the composites with the help of (Gated–thru reflect line) G–TRL calibration. The dielectric properties are studied from the measured scattering parameters to characterize the material’s defective and non-defective region. Particulate reinforced composite with an air gap of 0.1 mm thickness and 10 mm width is used for validation of the technique.
Probability of Detection models, for a specific NDE system, is very important to assess the probability at which a flaw of certain dimension can be detected. This paper proposes a fully model assisted probability of detection (MAPOD) with a Bayesian decision to classify the flaw size. Multiple correlated flaw features were integrated to the POD analysis. This was made by assigning the observation model directly to a multivariate Gaussian distribution. The mean and covariance of the distributions were empirically computed to form a Gaussian Mixture Model (GMM) to obtain the classical Hit/Miss POD curve. A risk analysis was performed on the probability of misclassification considering a 0/1 loss function. Classification decisions were made from the posterior estimates within the Bayesian framework. For the analysis, a finite element simulation study was performed for a flaw located at the sub-surface of a stainless steel (SS304) specimen.