Application of Wavelet Theory and Neural Network on Ultrasonic Testing 潘永振、葉競榮
E-mail: 9303430@mail.dyu.edu.tw
ABSTRACT
Weld flaws may be roughly classified into two categories, i.e., planar flaws and volumetric flaws. The former are highly unacceptable because they are very easy to propagate into cracks. Hence during construction, flaws of this kind should be removed regardless of their sizes. Therefore it is a critical issue for Ultrasonic Testing inspectors to distinguish this kind of flaws from others. In this research, we first used wavelet transform to extract feature parameters from digitized UT signals, and then planar flaws were
recognized by neural network analysis. Preliminary results have shown correct recognition rates for planar flaws and volumetric flaws are 94% and 90.19% respectively. Therefore, it is reasonable to say that the proposed process may become a practical one through further improvement.
Keywords : Ultrasonic Testing ; Neural Network ; Wavelet Transform ; Planar Flaw Table of Contents
目錄 封面內頁 簽名頁 授權書...iii 中文摘要...iv 英文摘要...v 誌 謝...vi 目錄...vii 圖目錄...ix 第一章 緒
論...1 1.1 研究動機與目的...1 1.2 文獻回顧...2 1.3 論文架 構...3 第二章 超音波檢測...4 2.1 非破壞性檢測概述...4 2.2 超音波檢 測...5 2.2.1 超音波產生之原理...5 2.2.2 音波的種類...6 2.2.3 超音波檢測方 法...9 2.3 不同瑕疵之回波特性...10 第三章 小波理論...11 3.1 小波轉換簡 介...11 3.2 時頻分析...11 3.3 離散小波轉換...15 3.4 多重解
析...16 3.5 多重解析度之金字塔架構...21 第四章 類神經網路...28 4.1 類神經網路發 展史...28 4.2 類神經網路基本架構...28 4.3 人工神經元模型...29 4.4 類神經網路之分 類...31 4.5 類神經網路之運作原理及特性...33 4.6 倒傳遞演算法...34 第五章 實驗方法與結 果...37 5.1 實驗流程...37 5.2 實驗結果...42 第六章 結論...49 參 考 文 獻...50
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