Asa Prateepasen

Acoustic emission and vibration for tool wear monitoring in single-point machining using belief network

This paper proposes an implementation of calibrated acoustic emission (AE) and vibration techniques to monitor progressive stages of blank wear on carbide tool tips. Three cutting conditions were used on workpiece material type EN24T, in turning operation. The root-mean-square value of AE (AErms) and the coherence function between the acceleration signals at the tool tip in the tangential and feed directions was studied. Three features were identified to be sensitive to tool wear AErms, coherence function in the frequency ranges 2.5-55 kHz and 18-25 kHz. Belief network based on Bayes rule was used to integrate information in order to recognise the occurrence of worn foot. The three features obtained from the three cutting conditions and machine time were used to train the network. The set of feature vectors for worn tools was divided into two equal subsets: one to train the network and the other to test it. The AErms in term of AE pressure equivalent was used to train and test the network to validate the calibrated acoustic. The overall success rate of the network in detecting a worn tool was high with low error rate.

Identification of AE Source in Corrosion Process

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.

A Real-Time Automatic Inspection System for Pattavia Pineapples

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.

Semi-Parametric Learning for Classification of Pitting Corrosion Detected by Acoustic Emission

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.

Corrosion-source location by an FPGA-PC based acoustic emission system

This paper presents a novel low-cost acoustic emission (AE) source-location system to locate corrosion sources in AISI304 austenitic stainless steel. The system is implemented using an FPGA-PC configuration. Three AE sensors with 150 kHz resonance frequency were used to detect the AE activities generated from the corrosion. Experiments were set up to show performance of the system in locating uniform and pitting corrosions in stable state using corrosive solutions and electrochemical environment. A commercial AE system was used to assure the AE activities with the location results. Experimental results showed the ability of the AE source location system to locate corrosion sources. In conclusion, the proposed AE source-location system is appropriate and practicable to locate corrosion with flexibility and affordability.

Fitting a Pineapple Model for Automatic Maturity Grading

In this paper, we present a pineapple skin model and a method to fit the model. Our main application of the model is for automatic maturity grading of pineapples in canned pineapple industry. The model consists of two subparts: Phyllotaxis and pineapple scale models. The Phyllotaxis model represents the spiral arrangement of pineapple-scales, which is a growing pattern of the fruit. It includes a string of scale-model cells. The scale model includes boundary, internal area and petal part of scale. Modified snake algorithm is used to construct the structure model while Active Shape Model (ASM) is applied to each scale. The model can accurately fit to pineapple skins in our experiment and classification features of the fruit can be extracted.

Effect of Sulfuric Acid Concentration on Acoustic Emission Signals in Uniform-Corrosion

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.

Monitoring Nugget Formation of Nickel-Alloys in Micro Spot Welding Using Acoustic Emission

The aim of this paper is to apply the acoustic emission (AE) technique for monitoring the soundness of welded nugget and for identifying the appropriate range of energy in a micro spot welding process. In this research, 0.12 mm-nickel alloys strips were welded by a 160 joulecapacitor discharge (direct current: DC) micro spot welder. An AE transducer, whose resonant frequency is 150 kHz, was placed at the lower electrode of the welder to detect the AE signal generated by the welded nugget. The collected AE signals were analyzed and compared with the results of peel test and metallography. The experimental results show that count, amplitude and root-mean-square (RMS), which are time-domain AE parameters, significantly relate to the appropriate range of welding energy producing sound nuggets. The outcome can be used for reducing the number of samples for destructive testing and for determining the accepted nugget quality. Introduction Resistance spot welding (RSW) is a process of welding workpieces using heat generated by electrical resistance of the material. Due to its fast operation, it is widely used in many automated and robotized production lines, e.g. automobile, electronic parts and construction [1]. By increasing the soundness of welded nugget, the number of spots can be reduced to achieve the maximum productivity [2, 3]. To do this, welding current, duration and electrode force must be carefully controlled since they are associated with the contact resistance, which determines the soundness of welded nugget. The detail of contact resistance including its mathematical model and heat transfer can be found in [4, 5]. Several destructive testing methods are available for determining welding quality, e.g., peel, chisel and twist tests. The standard of such methods for specimens whose thickness is not less than 0.4 mm can be found in [6]. On the other hand, ultrasonic test, which is non-destructive, can be used for indicating size and soundness of the welded nugget [7, 8, 9, 10]. However, apart from acoustic emission (AE) technique, none of non-destructive testing (NDT) methods is capable of real time monitoring the soundness of welded nugget. Using the AE technique, the welding process can be investigated at the stages of nugget formation and expulsion. It was employed in the welding process of 1.6 mm-carbon steel plate and the result was confirmed by thermal imaging technique [11]. The correlation between AE parameters and strength evaluated by wrenching (pull by force) was studied for characterizing the welding soundness of 1 mm-Zn coated specimen [12]. The AE parameters in time domain were utilized to separate the level of welding energy whereas in frequency domain, the AE spectrum revealed the characteristic of welded nugget. In this paper, an AE technique for monitoring the soundness of a micro spot welding process is presented. Experiments of welding 0.12mm-nickel alloys strips were performed on a 160 joulecapacitor discharge (DC) micro spot welder. A 150 kHz-AE transducer was placed at the lower electrode of the welder to detect the AE signal, which is physically related to the nugget formation and material expulsion. Results of peel test and metallography were compared with the analysis of AE maximum amplitude, count and root mean square (AE-RMS) and with our previous work on Key Engineering Materials Online: 2004-08-15 ISSN: 1662-9795, Vols. 270-273, pp 510-517 doi:10.4028/www.scientific.net/KEM.270-273.510

Classification of Corrosion Detected by Acoustic Emission

This paper presents an Acoustic Emission (AE) to detect pitting corrosion in stainless steel. The AE signals were analyzed to reveal the correlation between AE parameters and severity levels of pitting corrosion in austenitic stainless steel 304 (SS304). In this work, the corrosion severity is graded roughly into five levels based on the depth of corrosion. Relationships between a number of time-domain AE parameters and the corrosion severity were first studied and key parameters identified. The corrosion severity was also categorized into three stages: initial, propagation and final stages based on the source mechanisms of the AE signals. We identified these stages from the frequency-domain characteristic of the AE signal and the visual characteristic of the corroded pits in each level of corrosion severity. A number of measures were employed to quantify such characteristics and the source mechanisms hypothesized. To demonstrate the usefulness of such parameters, a feed-forward neural network was used to classify the corrosion severity. Preprocessing and verification techniques were provided to facilitate and to maintain the generalization capability of the network. The classification performance is excellent and demonstrates that the AE technique and a neural network can be efficiently used to detect and monitor the occurrence of corrosion as well as to classify the corrosion severity.Copyright

Implementation of Cast Steel Nodes by Considering of CTOD Value

Cast steel nodes are being increasingly popular in steel structure joint application. Cast steel node joint consists of two parts: casting itself and the welds between the node and the steel member. The fatigue resistances of these two parts are very different. This paper presents a using of the MSF (Main Structure Farm) casting nodes S420 instead of carbon steel plate by considering the Crack Tip Opening Displacement (CTOD) value and the percentage of coarse grain. The sampling work piece from the weld and heat affected zone (HAZ) were tested and compared the CTOD value and mechanical properties to the standard. This result shows that the CTOD values are under allowable value. After that, a finite element (FEM) program was corporately used to simulate. It is acceptable cast node even the CTOD is undesirable. The benefit of the paper is to show the procedure to prove cast steel nodes by using CTOD.