Classification of materials using artificial intelligence techniques based on modal properties generated by vibration

The motivation of this project is to integrate the technology of signal processing and materials characterization into developing a system of non-destructive test of material identification. The purpose of this research is to obtain the modal parameters of stainless steel SS 304, aluminium 1100 and...

Full description

Saved in:

Bibliographic Details
Main Author:	Mohd Hilman, Mohd Akil Tan
Format:	Thesis
Language:	English
Subjects:	Material identification Modal parameters Vibration Modal analysis Materials parameters excitation Artificial intelligence techniques
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44201/1/p.1-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44201/2/full%20text.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-unimap-44201
record_format	uketd_dc
spelling	my-unimap-442012016-11-29T08:29:00Z Classification of materials using artificial intelligence techniques based on modal properties generated by vibration Mohd Hilman, Mohd Akil Tan Nur Liyana Tajul Lile The motivation of this project is to integrate the technology of signal processing and materials characterization into developing a system of non-destructive test of material identification. The purpose of this research is to obtain the modal parameters of stainless steel SS 304, aluminium 1100 and glass, validating the parameters by finding and comparing the elastic constants of the materials to theoretical and conventional testing values, and classifying the parameters using linear discriminant analysis (LDA), k-nearest neighbor (k-NN), and artificial neural network (ANN) according to their respective material types. The modal parameters were obtained by modal analysis method, a vibration technique that employs impulse excitation of the materials by using impact hammer and analysis of the frequency response function (FRF) resulted from the excitation through peak-picking on the stabilization diagram. The values for modal parameters were validated by LMS Modal Synthesis, a program in the modal analysis software, normality test by MiniTab 17, a statistical software, and by comparing elastic constants of materials between the ones obtained from exploiting the modal parameters, conventional testing and theoretical values. LMS Modal Synthesis compares the percentages of the correlation and error of two FRF signals; one being the signal from the exact experimental values and another from the synthesize signal generated by the software itself. Normality test analyses on how closely the modal parameters will follow the normal distribution based upon the Anderson-Darling test. Elastic constant determination shows how credible and precise the values of modal parameters based upon the correlation of the elastic constants from the exploitation of modal parameters with the experimental and theoretical values. The validated modal parameters are the used as the features for classification by three different classifiers. LDA gave the best performance for this research. The architectures are then used for classification of modal parameters with the addition of noise to further test the reliability of the classification system. All the results and analysis are presented and discussed thoroughly in the thesis. Universiti Malaysia Perlis (UniMAP) 2013 Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/44201 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44201/1/p.1-24.pdf 9f37a512d2591465502b999e78079d00 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44201/2/full%20text.pdf d8a7a4da91c5fc597df1226e9a68021b http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44201/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 Material identification Modal parameters Vibration Modal analysis Materials parameters excitation Artificial intelligence techniques School of Mechatronic Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
advisor	Nur Liyana Tajul Lile
topic	Material identification Modal parameters Vibration Modal analysis Materials parameters excitation Artificial intelligence techniques
spellingShingle	Material identification Modal parameters Vibration Modal analysis Materials parameters excitation Artificial intelligence techniques Mohd Hilman, Mohd Akil Tan Classification of materials using artificial intelligence techniques based on modal properties generated by vibration
description	The motivation of this project is to integrate the technology of signal processing and materials characterization into developing a system of non-destructive test of material identification. The purpose of this research is to obtain the modal parameters of stainless steel SS 304, aluminium 1100 and glass, validating the parameters by finding and comparing the elastic constants of the materials to theoretical and conventional testing values, and classifying the parameters using linear discriminant analysis (LDA), k-nearest neighbor (k-NN), and artificial neural network (ANN) according to their respective material types. The modal parameters were obtained by modal analysis method, a vibration technique that employs impulse excitation of the materials by using impact hammer and analysis of the frequency response function (FRF) resulted from the excitation through peak-picking on the stabilization diagram. The values for modal parameters were validated by LMS Modal Synthesis, a program in the modal analysis software, normality test by MiniTab 17, a statistical software, and by comparing elastic constants of materials between the ones obtained from exploiting the modal parameters, conventional testing and theoretical values. LMS Modal Synthesis compares the percentages of the correlation and error of two FRF signals; one being the signal from the exact experimental values and another from the synthesize signal generated by the software itself. Normality test analyses on how closely the modal parameters will follow the normal distribution based upon the Anderson-Darling test. Elastic constant determination shows how credible and precise the values of modal parameters based upon the correlation of the elastic constants from the exploitation of modal parameters with the experimental and theoretical values. The validated modal parameters are the used as the features for classification by three different classifiers. LDA gave the best performance for this research. The architectures are then used for classification of modal parameters with the addition of noise to further test the reliability of the classification system. All the results and analysis are presented and discussed thoroughly in the thesis.
format	Thesis
author	Mohd Hilman, Mohd Akil Tan
author_facet	Mohd Hilman, Mohd Akil Tan
author_sort	Mohd Hilman, Mohd Akil Tan
title	Classification of materials using artificial intelligence techniques based on modal properties generated by vibration
title_short	Classification of materials using artificial intelligence techniques based on modal properties generated by vibration
title_full	Classification of materials using artificial intelligence techniques based on modal properties generated by vibration
title_fullStr	Classification of materials using artificial intelligence techniques based on modal properties generated by vibration
title_full_unstemmed	Classification of materials using artificial intelligence techniques based on modal properties generated by vibration
title_sort	classification of materials using artificial intelligence techniques based on modal properties generated by vibration
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Mechatronic Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44201/1/p.1-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44201/2/full%20text.pdf
_version_	1747836829430710272

Classification of materials using artificial intelligence techniques based on modal properties generated by vibration

Similar Items