Papers by Author: Chalermkiat Jirarungsatean

Abstract: Acoustic emission testing can be used to detect the energy emitted from material fracture and the advantage of this method is the real time monitoring, however the weld metal discontinuities are normally inspected by using conventional NDT methods such as Penetrant Testing (PT), Magnetic particle Testing (MT), Ultrasonic Testing (UT) and Radiographic Testing (RT) after the completion of welding. The weld defect must be repaired, which involves the cost and consumes a lot of time as well as reduce the reliability of manufactures. This paper presents the application of acoustic emission (AE) technique for monitoring and detecting the discontinuities during welding. In this study, gas tungsten arc welding (GTAW) was selected as test process. Carbon steel plate and autogenous welding technique were used to simulate the hot crack. The data acquisition (DAQ) and AE sensor were used to capture the acoustic signal generated during welding. The AE signals were amplified and filtered by using preamplifier. Then, signals were modified by wavelet transforms (WT) technique and classified by Fast Fourier Transform (FFT) technique. The results showed the possibility to use AE technique for monitoring and detecting the low signal amplitude generated from crack by using frequency domain. The advantage of this research is to propose the technique for monitoring the weld metal discontinuities during welding.

Abstract: In petroleum industry, corrosion failures of steel structures are common. The severity of corrosion in oil distillery inorganic compounds is higher than in those of organic compounds. Inorganic compounds such as sulfur are the most influential corrosive activators inside oil or chemical storage tanks. They normally have the tanks inspected and repaired along their life time. In addition the concentration of sulfur compound increases due to the accumulation of the residuals inside the tank, and so does the corrosive rate. In this paper, Acoustic Emission (AE) has been chosen to study the characteristic of AE signals received from the uniform corrosion mechanism of mild steel (A36) in various concentrations of Sulfuric acid (H2SO4) solution. AE signals were captured using a wide band sensor (WD) and recorded by AE system model LOCAN 320. The relationship between AE signals and sulfur concentrations as well as pH were exhibited.

Abstract: This paper presents a Non-Destructive Testing (NDT) technique, Acoustic Emission (AE) to classify pitting corrosion severity in austenitic stainless steel 304 (SS304). The corrosion severity is graded roughly into five levels based on the depth of corrosion. A number of timedomain AE parameters were extracted and used as features in our classification methods. In this work, we present practical classification techniques based on Bayesian Statistical Decision Theory, namely Maximum A Posteriori (MAP) and Maximum Likelihood (ML) classifiers. Mixture of Gaussian distributions is used as the class-conditional probability density function for the classifiers. The mixture model has several appealing attributes such as the ability to model any probability density function (pdf) with any precision and the efficiency of parameter-estimation algorithm. However, the model still suffers from model-order-selection and initialization problems which greatly limit its applications. In this work, we introduced a semi-parametric scheme for learning the mixture model which can solve the mentioned difficulties. The method was compared with conventional Feed-Forward Neural Network (FFNN) and Probabilistic Neural Network (PNN) to evaluate its performance. We found that our proposed methods gave much lower classificationerror rate and also far smaller variance of the classifiers.

Abstract: In this paper acoustic emission (AE) was implemented to detect and study the corrosion on austenitic stainless steel grade AISI 304. Two tests were conducted at room temperature using an acidic 30% Chloride solution in passive tests procedure and 3% NaCl solution in electrochemical process. From the experimental works, it appeared that AE signals could be detected during corrosion. Data were studied in time and frequency domain to characterize and to find out the relation between AE parameter and corrosion. In addition the source of generated acoustic signals and corrosive mechanism in the different corrosive environment condition were discussed.