Development Of Moiré Fringe Recognition System Using Artificial Neural Network For 2-D Displacement Measurement

Pelbagai kaedah telah dicadangkan untuk mendapatkan maklumat anjakan dalam analisis corak moiré. Kaedah-kaedah ini boleh dikategorikan kepada analisis manual oleh inspektor manusia, kaedah komputasi dan kaadah analisis berasaskan imej. Analisa manual terdedah kepada ralat manusia kerana ia bergan...

Full description

Saved in:

Bibliographic Details
Main Author:	Woo, Wing Hon
Format:	Thesis
Language:	English
Published:	2018
Subjects:	T Technology TJ181-210 Mechanical movements
Online Access:	http://eprints.usm.my/44598/1/Development%20Of%20Moir%C3%A9%20Fringe%20Recognition%20System%20Using%20Artificial%20Neural%20Network%20For%202-D%20Displacement%20Measurement.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-usm-ep.44598
record_format	uketd_dc
spelling	my-usm-ep.445982019-06-13T09:11:31Z Development Of Moiré Fringe Recognition System Using Artificial Neural Network For 2-D Displacement Measurement 2018-04 Woo, Wing Hon T Technology TJ181-210 Mechanical movements Pelbagai kaedah telah dicadangkan untuk mendapatkan maklumat anjakan dalam analisis corak moiré. Kaedah-kaedah ini boleh dikategorikan kepada analisis manual oleh inspektor manusia, kaedah komputasi dan kaadah analisis berasaskan imej. Analisa manual terdedah kepada ralat manusia kerana ia bergantung kepada keputusan manusia dalam analisa corak moiré. Penggunaan kaedah pengiraan dalam analisa corak moiré adalah terhad kepada corak moiré yang dihasil daripada parutan berfrekuensi tinggi yang sinusoid. Dalam kaedah berasaskan analisis imej, Algoritma yang kompleks menyebabkan butir-butir halus dalam corak moiré terhilang dalam operasi pra-proses imej. Situasi ini menyebabkan ketidakpastian dalam analisa corak moiré. Untuk mengatasi kelemahan yang disebut di atas, kaedah rangkaian saraf buatan (ANN) dicadangkan untuk sistem pengenalan corak moiré dalam pengukuran anjakan 2-D. Sistem pengenalan corak moiré terdiri daripada dua ANN dengan dua tugas yang berbeza iaitu (i) penentuan pusat pinggiran moiré dan (ii) penentuan kesipian berdasarkan corak moiré. Kaedah ANN dibandingkan dengan kaedah analisa grafik (GAM), sejenis kaedah analisa berasaskan imej, dari segi ketepatan dan masa pengiraan untuk pengukuran anjakan 2-D pola moiré. The experiments prove that ANN approach has a higher accuracy to GAM with mean errors with 95% confidence of 0.068 ± 0.013 mm for eccentric magnitudes and 1.85 ± 0.465º. An improvement of 66.18% in the computation time is also reported in the comparison. A straightforward solution for the moire fringe recognition system of circular grating moire pattern is achieved using ANN approach. _______________________________________________________________________________________________________ Various methods have been proposed in the analysis of moiré pattern. These methods can be categorized into manual inspection by human inspector, computational methods and image analysis based methods. Manual interpretation of moiré patterns is prone to human errors as it is highly dependent on the decision of the human inspector. The computational methods are lack of flexibility as they are limited to high frequency gratings which are sinusoidal in the transmittance of grating. As for the image analysis based methods, complex algorithms can unintentionally remove the fine details in the moiré patterns and cause uncertainty in the analysis. To overcome the above mentioned drawbacks, an artificial neural network (ANN) approach is proposed for a moiré fringe recognition system in 2-D displacement measurement. The moiré fringe recognition system consists of two ANNs with two different tasks : (i) the determination of moiré fringe centers of the circular grating moiré patterns and (ii) the determination of eccentricity magnitudes and eccentricity directions of the circular grating moiré patterns. The ANN approach is compared to graphical analysis method (GAM), an image analysis based method, in terms of accuracy and computational time for 2-D displacement measurement of circular grating moiré patterns. The experiments prove that ANN approach has a higher accuracy to GAM with mean errors with 95% confidence of 0.068 ± 0.013 mm for eccentric magnitudes and 1.85 ± 0.465º. An improvement of 66.18% in the computation time is also reported in the comparison. A straightforward solution for the moire fringe recognition system of circular grating moire pattern is achieved using ANN approach. 2018-04 Thesis http://eprints.usm.my/44598/ http://eprints.usm.my/44598/1/Development%20Of%20Moir%C3%A9%20Fringe%20Recognition%20System%20Using%20Artificial%20Neural%20Network%20For%202-D%20Displacement%20Measurement.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Mekanikal
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	T Technology TJ181-210 Mechanical movements
spellingShingle	T Technology TJ181-210 Mechanical movements Woo, Wing Hon Development Of Moiré Fringe Recognition System Using Artificial Neural Network For 2-D Displacement Measurement
description	Pelbagai kaedah telah dicadangkan untuk mendapatkan maklumat anjakan dalam analisis corak moiré. Kaedah-kaedah ini boleh dikategorikan kepada analisis manual oleh inspektor manusia, kaedah komputasi dan kaadah analisis berasaskan imej. Analisa manual terdedah kepada ralat manusia kerana ia bergantung kepada keputusan manusia dalam analisa corak moiré. Penggunaan kaedah pengiraan dalam analisa corak moiré adalah terhad kepada corak moiré yang dihasil daripada parutan berfrekuensi tinggi yang sinusoid. Dalam kaedah berasaskan analisis imej, Algoritma yang kompleks menyebabkan butir-butir halus dalam corak moiré terhilang dalam operasi pra-proses imej. Situasi ini menyebabkan ketidakpastian dalam analisa corak moiré. Untuk mengatasi kelemahan yang disebut di atas, kaedah rangkaian saraf buatan (ANN) dicadangkan untuk sistem pengenalan corak moiré dalam pengukuran anjakan 2-D. Sistem pengenalan corak moiré terdiri daripada dua ANN dengan dua tugas yang berbeza iaitu (i) penentuan pusat pinggiran moiré dan (ii) penentuan kesipian berdasarkan corak moiré. Kaedah ANN dibandingkan dengan kaedah analisa grafik (GAM), sejenis kaedah analisa berasaskan imej, dari segi ketepatan dan masa pengiraan untuk pengukuran anjakan 2-D pola moiré. The experiments prove that ANN approach has a higher accuracy to GAM with mean errors with 95% confidence of 0.068 ± 0.013 mm for eccentric magnitudes and 1.85 ± 0.465º. An improvement of 66.18% in the computation time is also reported in the comparison. A straightforward solution for the moire fringe recognition system of circular grating moire pattern is achieved using ANN approach. _______________________________________________________________________________________________________ Various methods have been proposed in the analysis of moiré pattern. These methods can be categorized into manual inspection by human inspector, computational methods and image analysis based methods. Manual interpretation of moiré patterns is prone to human errors as it is highly dependent on the decision of the human inspector. The computational methods are lack of flexibility as they are limited to high frequency gratings which are sinusoidal in the transmittance of grating. As for the image analysis based methods, complex algorithms can unintentionally remove the fine details in the moiré patterns and cause uncertainty in the analysis. To overcome the above mentioned drawbacks, an artificial neural network (ANN) approach is proposed for a moiré fringe recognition system in 2-D displacement measurement. The moiré fringe recognition system consists of two ANNs with two different tasks : (i) the determination of moiré fringe centers of the circular grating moiré patterns and (ii) the determination of eccentricity magnitudes and eccentricity directions of the circular grating moiré patterns. The ANN approach is compared to graphical analysis method (GAM), an image analysis based method, in terms of accuracy and computational time for 2-D displacement measurement of circular grating moiré patterns. The experiments prove that ANN approach has a higher accuracy to GAM with mean errors with 95% confidence of 0.068 ± 0.013 mm for eccentric magnitudes and 1.85 ± 0.465º. An improvement of 66.18% in the computation time is also reported in the comparison. A straightforward solution for the moire fringe recognition system of circular grating moire pattern is achieved using ANN approach.
format	Thesis
qualification_level	Master's degree
author	Woo, Wing Hon
author_facet	Woo, Wing Hon
author_sort	Woo, Wing Hon
title	Development Of Moiré Fringe Recognition System Using Artificial Neural Network For 2-D Displacement Measurement
title_short	Development Of Moiré Fringe Recognition System Using Artificial Neural Network For 2-D Displacement Measurement
title_full	Development Of Moiré Fringe Recognition System Using Artificial Neural Network For 2-D Displacement Measurement
title_fullStr	Development Of Moiré Fringe Recognition System Using Artificial Neural Network For 2-D Displacement Measurement
title_full_unstemmed	Development Of Moiré Fringe Recognition System Using Artificial Neural Network For 2-D Displacement Measurement
title_sort	development of moiré fringe recognition system using artificial neural network for 2-d displacement measurement
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Mekanikal
publishDate	2018
url	http://eprints.usm.my/44598/1/Development%20Of%20Moir%C3%A9%20Fringe%20Recognition%20System%20Using%20Artificial%20Neural%20Network%20For%202-D%20Displacement%20Measurement.pdf
_version_	1747821386797154304

Development Of Moiré Fringe Recognition System Using Artificial Neural Network For 2-D Displacement Measurement

Similar Items