Papers by Author: Asa Prateepasen

Abstract: Stress measurement based on the change of ultrasonic surface wave has been accepted to find out residual or existing stress on material. This paper shows the effect of rolling direction and grain size of material on surface wave velocity and energy attenuation. Different grain and rolling direction of material type SS 400, S420 and A516 were selected to test its effect. Three grain sizes of each material were varied by normalizing process at three temperature range (no heating, 850°C, 980°C and 1160°C). Through transmission ultrasonic surface wave, frequencies 2.25 and 5 MHz, were applied and recorded the velocity and attenuation of the response. The results show that rolling direction and grain size slightly effect on attenuation of ultrasonic wave but unaffected on sound velocity. Its outcome was compared with the effect of the material coating.

Abstract: Ultrasonic surface wave have been implemented to measure or predict the existing stress on material. Surface wave velocity shows linearly increase with stress applied in material. However, various applications were coated their surfaces with high corrode resistance material for example paint or aluminum thermal sprays. It may cause the change of the velocity of surface wave and lead to miss prediction. This paper presents the effect of material coating on surface wave velocity and its attenuations. Paint and Aluminum thermal spray coated on low carbon steel graded S420 (EN 10025 Standard) in the range of 100-500 micron. Through transmission ultrasonic surface wave was applied to measure the velocities change. Their frequencies are 2.25 and 5 MHz respectively. It was found that coating thickness show effect on sound velocity and sound wave attenuation. The benefit is to know the effect of coating and to approve the accuracy of stress measurement by ultrasonic wave.

Abstract: This paper presents a machine vision method to inspect the maturity of pineapples that ripe naturally. Unlike previous methods, the proposed technique can be categorized as a real-time non destructive testing (Real-Time NDT) approach. It consists of two phases, learning and recognition phases. In the learning phase, the system constructs a library of reference pineappleskin- color models. In the recognition phase, the same process is performed to build a pineappleskin- color model of the testing subject. The model is then compared with each of the reference in the library by a method called region-segmented histogram intersection. The subject is then labeled with the grade of the best match. The system achieved a high performance and speed (3 frames/sec.) in our experiment. The system also includes weighing machine on belt transmission for weight prediction.

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.