In-Sik Yun

The Defect Detection and Non-Destructive Evaluation in Weld Zone of Austenitic Stainless Steel 304 Using Neural Network-Ultrasonic Wave

In recent years, the importance of non-destructive evaluation has rapidly increased due to the collapse of large structures and the shooting up of safety accidents. The ultrasonic method. which is often used as a major non-destructive testing (NDT) technique in many engineering fields, is playing a significant role as a volumetric test regarded highly for evaluating a material’s integrity. This paper is recommended for publication the detecting any defects of the weld zone in austenitic stainless steel type 304 using ultrasonic waves, employing neural network on the basis of the detected defects and evaluating them. In detecting defects, we drew a distance amplitude curve on a standard scan sensitivity and a preliminary scan sensitivity shown in the correlation between the ultrasonic probe, the instrument and the materials on a quantitative standard, and quantitative evaluation is used to draw a distance amplitude curve. A total of 93. 3% of the defect types was distinguished by testing 30 defects after organizing a neural network system based on the defects on the ultrasonic evaluation and learning the neural network system. Thus the proposed ultrasonic wave-neural network in this work is useful for defects detection and Ultrasonic Non-Destructive Evaluation (UNDE) of the weld zone of austenitic stainless steel 304.

Evaluation of Technology Activity, Innovation and Productivity using Korean Patent Information

Patent information as the innovative index for activity of industry, science and technology reflects the inventive outcome of the nation, region, technology, or company etc.. and is able to be used as a tool evaluating the R&D product and technology diffusion. In this study, the index for analysing the productivity, innovation, and activity of the technology is provided to evaluate the technology in fields of the pharmaceutic, transport, biotechnology, textile, construction, machine parts, information media, and electric/telecommunication, which are becoming the national core technology. As a result of analysis, the technology activity in fields of the construction, pharmaceutic, and biotechnology shows a growing trend which reflect the interest in the quality and the extension of the life, but vice versa in fields of the textile, and information media. The innovation index in fields of the construction, pharmaceutic, and biotechnology index more than average, but vice versa in fields of the information media and electric/telecommunication. In case of technology productivity, more than 2 patentees are included in one patented technology. It has been determined that the technology productivity is decreased because of an increasing number of researcher participating the technology development, which is the recent trend of technology advancement.

Chaoticity Evaluation of Ultrasonic Signals in Welding Defects by 6dB Drop Method

This study proposes the analysis and evaluation method of time series ultrasonic signal using the chaotic feature extraction for ultrasonic pattern recognition. Features extracted from time series data using the chaotic time series signal analysis quantitatively welding defects. For this purpose analysis objective in this study is fractal dimension and Lyapunov exponent. Trajectory changes in the strange attractor indicated that even same type of defects carried substantial difference in chaoticity resulting from distance shills such as 0.5 and 1.0 skip distance. Such differences in chaoticity enables the evaluation of unique features of defects in the weld zone. In experiment fractal(correlation) dimension and Lyapunov exponent extracted from 6dB ultrasonic defect signals of weld zone showed chaoticity. In quantitative chaotic feature extraction, feature values(mean values) of 4.2690 and 0.0907 in the case of porosity and 4.2432 and 0.0888 in the case of incomplete penetration were proposed on the basis of fractal dimension and Lyapunov exponent. Proposed chaotic feature extraction in this study enhances ultrasonic pattern recognition results from defect signals of weld zone such as vertical hole.