臺灣博碩士論文加值系統

近年來工業4.0的提倡，對於金屬工件的品質全檢逐漸受到重視，尤其是航太及汽車所使用的螺絲螺帽、金屬扣件等。目前金屬扣件硬度檢測普遍都採抽樣破壞檢測，但金屬扣件硬度關係著整體機構的安全性，因此金屬扣件的硬度是不可忽略的安全問題。本論文提出一套應用渦電流檢測技術做金屬扣件硬度篩選檢測系統，結合倒傳遞神經網路進行智慧篩選並使用主成份分析法做數據趨勢解析。從文獻可得知金屬工件機械性質與電特性較難以捉摸，無法以簡易方式進行篩選。而工業上使用渦電流進行金屬扣件硬度非破壞檢測時，當遇到硬度判讀模稜兩可的情況時，須藉由經驗人工調整公差圓範圍使達到可接受程度，但此種方式並沒有系統的科學依據，純粹是依操作者的經驗決定。因此本論文嘗試解決此問題，提出應用倒傳遞神經網路進行線上訓練與即時智慧判讀，再使用主成份分析優化公差圓的包覆範圍，最後使用同種鋼材不同硬度的金屬扣件進行實驗來驗證本檢測系統的篩選能力。

The promotion of Industry 4.0 in past years has induced the requirement of the quality inspection of metal works gradually be emphasized. Especially, destructive inspection by sampling is commonly applied to the hardness testing of metal fasteners for aerospace and automobiles. Since the hardness of metal fasteners is related to the safety of the mechanical system, it cannot be neglected. A testing system using eddy currents method for the hardness screening and inspection of metal fasteners is proposed in this study, in which Back Propagation Neural Network (BPNN) is integrated for intelligent screening and Principal Components Analysis (PCA) is utilized for data analysis. Literatures revealed that the mechanical properties and electrical properties of metal work piece were hard to handle that simple methods could not be used for screening. Eddy current testing has been used for non-destructive inspection of metal fastener hardness in industry for decades, the ambiguous hardness interpretation would be manually adjust the tolerance to the acceptable range through experiences. Such a method did not have systematic science reference, but purely depended on operators’ experiences. Attempting to solve such a problem, this study therefore proposes the application of BPNN to online training and real-time interpretation, the use of PCA for optimizing the tolerance range, and the experiments of metal fasteners with same kind of steel but different hardness to prove the screening ability of the inspection system.

摘 要 III
Extended Abstract IV
致謝 X
目錄 XI
表目錄 XIII
圖目錄 XIV
第一章 緒論 1
1.1 研究背景 1
1.2 國內外文獻回顧 2
1.3 研究動機與目的 7
1.4 論文大鋼 8
第二章 理論探討 9
2.1 前言 9
2.2 渦電流檢測金屬扣件硬度原理 9
2.2.1 電磁感應原理 9
2.2.2 影響渦電流檢測之因素 10
2.2.3 渦電流檢測等效模型 12
2.2.4 金屬扣件硬度二維分析圖 14
2.3 移動平均濾波器 15
2.4 人工神經網路概述 17
2.4.1 人工神經元 17
2.4.2 神經網路架構與學習方法 19
2.5 主成份分析簡述 21
第三章 研究方法與設計 23
3.1 前言 23
3.2 改良式移動平均濾波器 23
3.3 倒傳遞神經網路架構與運算方式 25
3.4 主成份分析步驟 32
第四章 系統架構與設計 34
4.1 系統架構簡述 34
4.2 系統硬體架構與設計 35
4.2.1 FPGA硬體流程 35
4.2.2 阻抗曲線特性校準流程 36
4.3 系統介面與操作流程 39
第五章 系統實測與討論 45
5.1檢測探頭與系統外觀 45
5.2 金屬扣件規格與硬度驗證 47
5.3 系統篩選實測 50
5.3.1 A組 — 使用HRC 20-25為標準樣本 51
5.3.2 B組 — 使用HRC 30-35為標準樣本 54
5.3.3 C組 — 使用HRC 40-45為標準樣本 57
5.4 結果與討論 60
第六章 結論與未來展望 68
5.1 結論 68
5.2 未來展望 69
參考文獻 70
附錄 74

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