DocumentCode :
3585209
Title :
Comparison of Logistic Regression and Support Vector Machine for the Classification of Microstructure and Interfacial Defects in Zircaloy-2
Author :
Ramkishore, S. ; Madhumitha, P. ; Palanichamy, P.
Author_Institution :
Media Eng., Akamai Technol., Bangalore, India
fYear :
2014
Firstpage :
130
Lastpage :
134
Abstract :
β-quenched Zircaloy-2 is a candidate material for Pressurized Heavy Water Reactors. Before the fabrication of PHWR components from Zircaloy-2 billets, it is very important to ensure that that the billets have undergone proper β-quenching treatment and they have the appropriate β-martensite structures. Improper β-quenching treatment given to the billets is strictly unacceptable. Previously, destructive techniques and semi-destructive techniques such as metallography and hardness measurements used to be employed to ensure proper quenching. Recently, ultrasonic velocity and attenuation measurement based techniques have been used with varying degree of success. In this work, Logistic regression and Support vector machine techniques have been attempted to ensure proper β-quenching treatment of Zircaloy-2 microstructures and for the classification of heat treatment given to the Zircaloy-2 specimens. The acquired A-scan signals (time domain signals) meant for ultrasonic measurements have been utilized in this study. Results indicate that both Logistic regression and SVM techniques can be effectively utilized for comparative quick classification of microstructures of Zircaloy-2 specimens, out of which SVM approach gives better confidence level than logistic regression to ensure proper classification of microstructures of β-quenched Zircaloy-2 specimens.
Keywords :
hardness; logistics; metallography; regression analysis; support vector machines; A-scan signals; PHWR components; SVM techniques; Zircaloy-2 billets; Zircaloy-2 microstructures; Zircaloy-2 specimens; attenuation measurement; classification; hardness measurements; heat treatment; interfacial defects; logistic regression; metallography; pressurized heavy water reactors; proper quenching; quenching treatment; semidestructive techniques; support vector machine; ultrasonic measurements; ultrasonic velocity; Acoustics; Equations; Intermetallic; Logistics; Mathematical model; Microstructure; Support vector machines; ß-quenching treatment; Logistic Regression; Microstructure; Support Vector Machine; Zircaloy-2;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Soft Computing and Machine Intelligence (ISCMI), 2014 International Conference on
Type :
conf
DOI :
10.1109/ISCMI.2014.23
Filename :
7079369
Link To Document :
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