Papers by Author: Man Yong Choi

Abstract: Defect size of wall thinned pipe is measured by using Speckle Shearing Interferometry (SSI) and Digital Image Correlation (DIC) techniques. A wall thinned defect of a carbon steel pipe was typically caused by flow accelerated corrosion (FAC). As wall thinned pipe can cause a huge accident at the nuclear power plant (NPP), a wall thinned defect should be detected for structure safety. SSI is one of the optical nondestructive techniques and can provide to inspect in real-time and to measure on the whole visible area at a time. DIC is a kind of the visual testing method. This method which uses a stereo vision system can measure the deformation or strain/stress of a structure in 3D. In this paper, ASTM A106 Gr.B carbon steel pipe is used as specimen. When the pressure load is provided by the pressure pump, the out-of-plane deformation along the longitudinal direction of a pipe can be detected quantitatively. Both results of SSI and DIC experiments are compared.

Abstract: The objective of this paper is measurement of the surface roughness of tensile specimens under different tensile speed. In the test, the tensile specimens were loaded by tensile testing machine. The roughness of the surfaces was measured by digital holographic interferometric system (DHI). From the results, it was confirmed that DHI could measure the roughness of the various types of the specimen surface. In this paper, as the tensile speed was faster, the roughness of specimen surface was smaller.

Abstract: The temperature at the surface of the bony recipient site during drilling for orthodontic micro-implant placement was monitored using infrared thermography. The primary objective was to identify proper drilling conditions to allow efficient drilling without raising the bone temperature above the threshold temperature of 44oC to 47oC. Bovine ribs were selected to provide cortical bone of a similar quality to the human mandible. Four drilling conditions combining 2 motor speeds (600 and 1200 rpm) and 2 pressure loads (500 g and 1000 g) were established based on clinical practice. Much care was taken to duplicate an oral environment, although no irrigation was used to allow the infrared radiation to transmit without being hindered by cooling water. Thermal images were recorded using a Thermovision 900 system (Amega, Danderryd, Sweden). The results showed that the temperature rise relys significantly on the drilling speed and pressure. When both the drilling speed and the pressure were low, the cortical bone could not be cut. However, increasing either the speed or the pressure resulted in a temperature increase. Drill speed of 600 rpm at the pressure load 1000g produced more or less the same temperature, 40- 45 o C, as when the drill speed was increased to 1200 rpm while keeping the load at 500g. Yet, a temperature as high as 62.4 o C was recorded when combining the high motor speed and high pressure. Most of the temperature rise took place during the initial 5-10 seconds of drilling, indicating that intermittent irrigation at an interval of 5 seconds or less would be of particular importance to minimize possible thermal trauma.

Abstract: In this study, microwave free-space transmission technique was applied to measure the moisture content of powdered food (wheat flour, milk powder, and coffee powder). In frequency range from 1 to 15 GHz, the microwave attenuation and phase shift due to moisture content of food samples were measured and analyzed using vector network analyzer, double rigid horn antennas, and sample holder filled with moist food samples. To estimate the relationship between moisture density of powdered food and the attenuation and phase shift, correlation analysis was performed. The correlation coefficients at each food sample were greater than 0.91. The calibration equation for moisture content measurement having attenuation and phase shift as independent variables at 15 GHz was developed and evaluated. The coefficient of determination and root mean square for all food samples were 0.974 and 0.328 % respectively.

Abstract: This study is aimed to analyze the thermal imaging patterns presented by infrared(IR) thermography at which the metal with internal defects are thermally heated. Through the knowledge of non-destructive testing which infrared thermography can be applied to detect the defects inside the materials, there are two materials experimented; one is stainless steel and the other is cast-iron. Thermally, each material of specimens is heated at the base of the material and kept with constant temperature, The artificial defects in the specimen are formulated. Under the shape and location of the defects, temperature profiles are also measured and validated using the computer simulation. It is concluded that the characteristics of thermal patterns obtained from IR thermography are consistent with those of measurement and computations.

Abstract: This study aims at identifying the structural dynamic characteristics using an ambient vibration, and developing a health monitoring system which adopts damage detecting algorithms. One of the main problems for this system design is to measure long-time dynamic response signals and simultaneously estimate the structural dynamic properties. In order to be suitable for a long-time monitoring, we conduct an ambient dynamic testing for a 3-floor moment resistance steel structure and analyze structural dynamic characteristics using time domain estimation techniques. Also, a damage detecting test is performed to evaluate damage state by various detecting algorithms (modal correlation method (MAC & COMAC), eigen-parameter change method). Finally, this paper suggests the optimal algorithms for the identification of the structural damage locations and damage quantities with all such comparisons. The algorithms presented in this paper prove to be applicable in structural health monitoring of structures.

Abstract: This study was performed to classify the acoustic emission (AE) signal due to surface check and water movement of the flat-sawn boards of oak (Quercus Variablilis) during drying using the principle component analysis (PCA) and artificial neural network (ANN). To reduce the multicollinearity among AE parameters such as peak amplitude, ring-down count, event duration, ring-down count divided by event duration, energy, rise time, and peak amplitude divided by rise time and to extract the significant AE parameters, correlation analysis was performed. Over 96 % of the variance of AE parameters could be accounted for by the first and second principal components. An ANN was successfully used to classify the AE signals into two patterns. The ANN classifier based on PCA appeared to be a promising tool to classify the AE signals from wood drying.