Classification of stainless steel and mild steel using vibration technique

The production of material in industry must attain some standard such as the standard required by American Society for Testing and Materials (ASTM) International. The requirement of the material standard is important in some crucial field such as aerospace, engineering and automotive. This resear...

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Main Author:	Intan Maisarah, Abd Rahim
Format:	Thesis
Language:	English
Subjects:	Vibration technique Stainless steel Vibration analysis Material mechanical properties > Testing Stainless steel > Testing methods
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31301/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31301/2/Full%20text.pdf
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spelling	my-unimap-313012014-01-19T07:22:43Z Classification of stainless steel and mild steel using vibration technique Intan Maisarah, Abd Rahim The production of material in industry must attain some standard such as the standard required by American Society for Testing and Materials (ASTM) International. The requirement of the material standard is important in some crucial field such as aerospace, engineering and automotive. This research presents a development of a material classification scheme with non-destructive testing on the material to classify the material type. The classification of the material can be useful in post-production verification. Many testing methods have been developed to reach the standard of the material production. The testing of the material mechanical properties using vibration technique could determine the natural frequencies, the damping ratio and mode shapes of the structure. The testing method chose to be implemented in this research is impact hammer testing. Frequency Response Function (FRF) signals obtained from the testing and natural frequencies of the materials are extracted from FRF signals. In this research, the features considered as the input data for the algorithm training are the natural frequencies of the material and its amplitude. Later, the input data obtained are classified using Artificial Neural Network (ANN) with Levenberg-Marquardt Backpropagation and k-Nearest Neighbor (k-NN). Each of the classifier produced a different classification rate depending on the performance of the training input data set. The result from the classification system shows that k-NN is giving the accuracy of 99.69% with the k value of 3. While, Levenberg-Marquardt Backpropagation is giving the best classification rate of 99.43%. Universiti Malaysia Perlis (UniMAP) 2011 Thesis en http://dspace.unimap.edu.my:80/dspace/handle/123456789/31301 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31301/1/Page%201-24.pdf fba1b282aaa621e8650de6ba1f955346 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31301/2/Full%20text.pdf f275a8d0743fef7d42456a70f451c0bf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31301/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 Vibration technique Stainless steel Vibration analysis Material mechanical properties -- Testing Stainless steel -- Testing methods School of Mechatronic Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
topic	Vibration technique Stainless steel Vibration analysis Material mechanical properties -- Testing Stainless steel -- Testing methods
spellingShingle	Vibration technique Stainless steel Vibration analysis Material mechanical properties -- Testing Stainless steel -- Testing methods Intan Maisarah, Abd Rahim Classification of stainless steel and mild steel using vibration technique
description	The production of material in industry must attain some standard such as the standard required by American Society for Testing and Materials (ASTM) International. The requirement of the material standard is important in some crucial field such as aerospace, engineering and automotive. This research presents a development of a material classification scheme with non-destructive testing on the material to classify the material type. The classification of the material can be useful in post-production verification. Many testing methods have been developed to reach the standard of the material production. The testing of the material mechanical properties using vibration technique could determine the natural frequencies, the damping ratio and mode shapes of the structure. The testing method chose to be implemented in this research is impact hammer testing. Frequency Response Function (FRF) signals obtained from the testing and natural frequencies of the materials are extracted from FRF signals. In this research, the features considered as the input data for the algorithm training are the natural frequencies of the material and its amplitude. Later, the input data obtained are classified using Artificial Neural Network (ANN) with Levenberg-Marquardt Backpropagation and k-Nearest Neighbor (k-NN). Each of the classifier produced a different classification rate depending on the performance of the training input data set. The result from the classification system shows that k-NN is giving the accuracy of 99.69% with the k value of 3. While, Levenberg-Marquardt Backpropagation is giving the best classification rate of 99.43%.
format	Thesis
author	Intan Maisarah, Abd Rahim
author_facet	Intan Maisarah, Abd Rahim
author_sort	Intan Maisarah, Abd Rahim
title	Classification of stainless steel and mild steel using vibration technique
title_short	Classification of stainless steel and mild steel using vibration technique
title_full	Classification of stainless steel and mild steel using vibration technique
title_fullStr	Classification of stainless steel and mild steel using vibration technique
title_full_unstemmed	Classification of stainless steel and mild steel using vibration technique
title_sort	classification of stainless steel and mild steel using vibration technique
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Mechatronic Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31301/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/31301/2/Full%20text.pdf
_version_	1747836790448848896

Classification of stainless steel and mild steel using vibration technique

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