Papers by Keyword: Non-Destructive Evaluation (NDE)

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Authors: Kazuya Mori, I. Torigoe, M. Tokunaga, S. Hatanaka, Y. Shiramizu
349
Authors: S.J. Farley, J.F. Durodola, N.A. Fellows, Luis Héctor Hernández-Gómez
Abstract: A method is presented to demonstrate the use of artificial neural networks (ANNs) in providing additional information regarding defects or flaws when used in conjunction with the ultrasonic A-scan method. ANNs were employed both as pattern classifiers and as function approximators to maximise the amount of data available from the temporal A-scan signal. A steel bar was modelled in 2D using ABAQUS finite element analysis (FEA) software. A single defect was introduced to the bar, modelled as a void, and parametric studies conducted to record data with the defect at various locations. An ultrasonic Lamb wave was introduced at the top of the bar. The longitudinal wave propagated along the length of the bar and was partially reflected by the defect. Multiple cases were simulated, modelling voids between 1mm and 6mm in width in various locations. Mean displacement of all the nodes at the top of the bar was recorded throughout the simulation, and features extracted from this waveform to create the data set for the ANNs. The ANNs were trained with a percentage of the data collected, selected at random, and assessed with the remaining data. The target data for the ANNs were the depth and size of the defect. The case of two separate defects was also investigated. The procedure was carried out in the same manner as for one defect, but in this case the target data for the ANNs were the depth of the first defect and the distance between the defects. A separate ANN was employed as a pattern classifier, to determine if the reflected A-scan signal represented one or two defects. The final system was tested using previously unseen data, and provided very good results both in determining the number of defects and the size and location of the defects, even with data to which noise had been added.
125
Authors: M. Yamano, S.R. Ghorayeb
855
Authors: Jin Yi Lee, Ji Seoung Hwang, Kyung Chul Lee, Se Ho Choi
Abstract: It is important to estimate a magnetic field’ intensity distribution for the application of to industrial nondestructive evaluations. A magnetic camera provides the same spatial resolution and a distribution of a quantitative magnetic field with homogeneous lift-off. The method for arraying sensors is important on the magnetic camera because it must obtain a high spatial resolution. This work proposes an equation to evaluate the limitation of spatial resolution on the condition of the decided size of a magnetic sensor package. The possibility of obtaining the high spatial resolution in spite of the above-mentioned limitation would also be verified.
235
Authors: Jong Duk Chung, Seok Yoon Han, Hong Jung Chun
Abstract: Engineering safety diagnosis of crashed subway electric multiple units (EMUs) was conducted for safety assessment. Several advanced engineering analysis techniques including nondestructive evaluation (NDE) techniques and stress and structural analyses programs, were performed for better understandings and exploration of failure analysis and safety concerns. NDE techniques such as ultrasonic testing and magnetic particle testing, were used to detect manufacture-induced and/or in-service defects and collision-induced flaws, and measure the dimensions of deformed and non-deformed parts on damaged EMUs due to a rear-end collision. Moreover, stress and structural analyses using commercial I-DEAS software provided important information on stress distribution and load transfer mechanisms as well as the amount of damages during the crash. A good agreement has been found between structural analysis results and the results of actual damages in EMUs during crash. In this investigation, various advanced engineering analysis techniques for the safety analysis of subway EMUs have been introduced and the analysis results have been used to provide the critical information for the safety assessment of crashed EMUs.
114
Authors: Guo Shuang Shui, Yue Sheng Wang, Jian Min Qu
Abstract: In this paper, a new theoretical model is developed to characterize the damage of the adhesive joint. Elastic modulus of adhesive joints is an important parameter to represent damage characteristics. Based on the fact that the thickness of the adhesive layer is very small, it is reasonable to believe that damage will decrease the tension modulus of the adhesive joint while the compression modulus will keep unchanged. Modeling the adhesive joint as an interface with different modulus in tension and compression, and applying integral transform method, we solve the associated nonlinear boundary problem to obtain the nonlinear ultrasonic waves transmitting through the adhesive layer. With this nonlinear ultrasonic wave, variation of elastic modulus and damage variable of the adhesive layer are thereafter characterized nondestructively by a nonlinear coefficient.
339
Authors: Min Rae Lee, Joon Hyun Lee
887
Authors: Yung Chun Lee, Zi Bin Lin, Shi Hoa Kuo
471
Authors: Sang Woo Choi, Joon Hyun Lee, An Jin Nam, Sridhar Krishnaswamy
787
Authors: Jai Won Byeon, C.S. Kim, Y.H. Kim, S.I. Kwun, S.J. Hong
Abstract: An attempt was made to assess the surface damage in the isothermally degraded FSX414 Co-based superalloy using ultrasonic Rayleigh surface wave. Microstructural analysis (amount of solute Cr near the surface) and measurement of ultrasonic velocity and attenuation were performed after isothermal degradation heat treatment at 1100°C for up to 4000 hours. Rayleigh wave velocity was observed to increase with aging time, which was attributed to the increased depletion of Cr near the surface. Strong frequency dependence of Rayleigh wave velocity was found in the specimens with surface depletion layer. Attenuation coefficient of Rayleigh wave increased with increasing aging time. Therefore, both attenuation and velocity of ultrasonic Rayleigh wave are suggested as potential nondestructive evaluation parameters for the characterization of the thermally aged FSX414 superalloy.
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