UT System Composition and Welding Flaw Classification for SWP Stability Estimation
The purpose of this research is stability estimation of plant structure through classification and recognition about welding flaw in SWP(Spiral Welding Pipe). And, In this research, we used nondestructive test based on ultrasonic test as inspection method, and made up inspection robot in order to control of ultrasonic probe on the SWP surface, and programmed to signal processing code and pattern classifying code by user made programming code. Inspection robot is simply constructed as 2-axes because of welding bead with fixed pitch. So, inspection of welding part can be possible as composition of inspection part for tracking on welding line. For evaluation of flaw signal is reflected on welding flaw, user-made program codes are composed of signal processing and Bayesian classifier and perceptron neural network and back-propagation neural network. And then, we confirmed to superiority of neural network method compared with Bayesian classifier for classification and recognition rate. According to this result, we selected back-propagation neural network as classification and recognition method about the system of SWP stability Estimation. Through this process, we proved efficiency on the system of SWP stability Estimation, and constructed on the base of the system of SWP stability Estimation for the application in industrial fields.
Kikuo Kishimoto, Masanori Kikuchi, Tetsuo Shoji and Masumi Saka
J. Y. Kim et al., "UT System Composition and Welding Flaw Classification for SWP Stability Estimation", Key Engineering Materials, Vols. 261-263, pp. 1385-1390, 2004