刀具狀態為影響金屬機械加工成品精度與良率的重要直接因素之一，其中刀具磨耗為加工中狀態不斷改變之因素，易造成加工精度下降與不良品非預期產生。因此在磨耗程度無法被準確評估的情況之下，汰換刀具成為業者維持加工成品品質的策略之一，但也同時造成生產成本的上升。有鑑於端銑刀為機加工廠所常用於銑銷加工工序的一種刀具，因此本研究以端銑刀做為研究對象。
一般業界對於刀具磨耗的評估方式受個人主觀意識、加工工序、工件材料等因素影響，缺乏客觀量化標準。故本研究期能從科學定義的磨耗樣態發展出量化的分類標準，取代原分級方式。本研究已先前建立的背照式光學系統為基礎，建立一套自動化光學檢測系統並優化其演算法並提取刀具磨耗資訊，再以刀具磨耗資訊進行分析以辨識出刀具上所存在不同的磨耗樣態，以提供機加工業決策者更多資訊，包含磨耗樣態與磨耗數值等決策指標，幫助優化機加工業的整體效能。

Tool condition is having one of the important role in affecting the precision and yield of product. Tool wear is a factor that continuously changing in the processing period, which can easily cause the decrease of machining accuracy and unexpected production of scrap products. In the case where the wear level cannot be accurately evaluated, tool replacement has become one of the strategies for the industry to maintain the quality of the finished product, but it has also caused an increase in the cost of production. Given that the end mill is a milling tool that is widely used in milling process at manufacturing industry. This research takes the end mill as the research subject.
By industry, the assessment of tool wear is affected by subjective consciousness, processing method, materials of the work-piece, and it lacks objective quantitative standards. Therefore, this research aims to develop a quantitative classification standard from the scientific definition of wear type to replace the industrial grading method. This research based on the previously built inspection system, rebuild an automatic optical inspection system, modify the reconstruction algorithm also generate the wear information. By analyzing the wear information, it could recognize existing wear type on the end mill. Users in manufacturing industry could make the decision based on the wear information, including wear type and the index of wear, and optimized the performance of the manufacturing.

摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vii
符號表 viii
第一章、 緒論 1
一.1 前言 1
一.2 文獻回顧 2
一.2.1 刀具磨耗機制 2
一.2.2 機器視覺刀具檢測技術 6
一.2.3 市售影像式刀具檢測系統 7
一.3 研究動機 9
第二章、 基礎理論 10
二.1 投影輪廓重建技術 11
二.1.1 輪廓投影 12
二.1.2 輪廓擷取 12
二.1.3 拓樸重建 13
二.1.4 磨耗深度提取 14
二.1.5 正射投影 16
二.2 磨耗樣態分類技術 18
二.2.1 光學系統座標系 18
二.2.2 磨耗樣態分類與數值計算 22
第三章、 實驗設計 27
三.1 硬體系統設計 27
三.2 軟體系統設計 29
三.3 系統操作流程 31
第四章、 系統驗證與實驗結果 33
四.1 系統解析度測試 33
四.2 系統放大倍率與視野測試 34
四.3 物體邊緣偵測 35
四.4 重建刀具輪廓影像的利用與儲存 37
四.5 失焦影像與磨耗深度關係 39
四.6 刀具影像拍攝時間 41
四.7 刀具影像拍攝動作示意圖刀具影像重建時間 43
四.8 動力刀座偏擺測試 43
四.9 刀具樣本準備 46
第五章、 結論 47
第六章、 未來工作 48
參考文獻 49
附錄一、待測刀具規格 50
附錄二、遠心光源規格 51
附錄三、遠心鏡頭規格 52
附錄四、工業相機規格 53
附錄五、步進馬達規格 54
附錄六、動力刀座規格 55
附錄七、精密滑台規格 56

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