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研究生:王厚鈞
研究生(外文):Hou-Jiun Wang
論文名稱:基因演算法應用於數位影像關係法及薄膜變形量測
論文名稱(外文):The Deformation Measurement of Thin Film by Using Genetic Algorithms and Digital Image Correlation Method
指導教授:黃順發黃順發引用關係
指導教授(外文):Shun-Fa Hwang
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:機械工程系碩士班
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2005
畢業學年度:93
語文別:中文
論文頁數:114
中文關鍵詞:MEMS局部最佳解基因演算法雙線性插分法位移應變數位影像關係法體型-面型結合加工
外文關鍵詞:digital image correlation (DIC) methodstraindisplacementgenetic algorithmMEMSbulk-surface combined micromachining
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數位影像關係法(DIC,digital image correlation)是利用數位影像以數值來表示和記錄影像的特性並加以分析以得到所需的資訊,藉著比對試片測試前後成對影像來得到應變和位移的分佈,在過程中採用雙線性插分法對數位影像進行連續化的處理,而應變與位移等變數的搜尋方式則採用基因演算法(genetic algorithms, GAs),基因演算法是模擬自然界演化的一種最佳化搜尋方法,改善了傳統尋序搜尋法耗時與易落入局部最佳解的缺點,研究中探討如何應用基因演算法來進行數位影像關係法求解的最佳化,並利用亮度值平移與變數疊代的方式來增加GA求解的性能,再依此數值方法所得結果換算變形的各項參數,以建立起用數位影像特性來量測材料應變的方法。

為了驗證程式,在實驗方面第一部份先以大試片進行剛體平移與轉角的測試,第二部份則是對大試片進行拉伸實驗,前兩部份的實驗過程皆以數位相機擷取影像,而實驗的第三部份是利用MEMS(Micro-Electro-Mechanical Systems)製程中常見的混合加工(體型-面型結合加工)技術製作鎳、銅等金屬薄膜與SU-8光阻結構,再以MTS公司所生產的微拉力試驗機對微試片進行拉伸測試,並配合CCD鏡頭擷取影像。

利用本文所建立的分析程式,分析實驗過程所紀錄的數位影像,由最後得到的結果顯示,在有高品質影像與應變量大於千分之一情形下,程式分析的結果與試驗機量測結果比較,大試片拉伸應變量測最低誤差可達3.84%,微試片拉伸應變最低誤差可達0.5%,而剛體平移最低誤差也可達1.5%,轉角測試的最低誤差則可達5%,完成以實驗來驗證程式的正確性的目的,並建立一低成本且高準確性的測量方法。

關鍵字:數位影像關係法,數位影像,應變,位移,雙線性插分法,基因演算法局部最佳解,MEMS,體型-面型結合加工
Digital image correlation (DIC) method employs the property of digital image recorded. This method can afford strain information of a specimen by processing two digital images that were respectively captured from the unforced and forced specimens. A bilinear interpolation technique is used to transform the digital image data into a continuous form, and the searching strategy named genetic algorithm is used to search for variables about strain and displacement. This work will discuss how to combine this algorithm with digital image correlation method and how to adjust intensity pattern and variable iteration method to increase the convergence speed, reliability, and precision of solutions. Hence, all deformation data could be obtained by the proposed combined algorithm.

Experiments were performed in three types of specimens in order to test the proposed combined algorithm. In the first type of specimens, ideally deformed image tests, rigid body motion test of large specimens are invested. The uniaxial tensile tests of large specimens are considered as the second type of specimens. In the third type of specimens, nickel, copper and SU8 thin film structures are made by bulk-surface combined micromachining commonly used in MEMS fabrication. Then, a MTS Tytron 250 micro tensile test machine is used to do the tensile tests of the micro structures, and the images are captured by CCD camera.

The results obtained by the proposed combined algorithm with high quality images are compared with those from test machine as follows. The minimum errors in rigid body motion and rotation test are 1.5 and 5 percent, respectively. If the strain values are greater than 0.001, the minimum error of large specimen is 3.84 percent, and the minimum error in micro specimens tensile test is 0.5 percent. Therefore, it can be said that the proposed combined algorithm is a low cost and high accurate measurement method.

Key word:digital image correlation (DIC) method, strain, displacement, genetic algorithm, MEMS, bulk-surface combined micromachining
目錄
頁次
中文摘要……………………………………………………………………………. i
英文摘要……………………………………………………………………………. ii
誌謝…………………………………………………………………………………. iv
目錄…………………………………………………………………………………. v
表目錄………………………………………………………………………………. vii
圖目錄………………………………………………………………………………. viii
符號說明……………………………………………………………………………. xii

一、 前言…………………………………………………………………………... 1
1.1 研究動機……………………………………………………………….. 1
1.2 研究目的……………………………………………………………….. 2
1.3 文獻回顧……………………………………………………………….. 2
1.3.1 數位影像關係法(DIC)..………………………..……………. 2
1.3.2 基因演算法(Genetic Algorithm)…………………………… 4
1.3.3 微拉伸測試(Microtensile test)…………………………… 5
1.4 論文架構……………………………………………………………….. 9

二、 理論基礎……………………………………………………………………... 10
2.1 數位影像關係法………………..…………………………………….. 10
2.1.1 數位影像在DIC上的前處理…………………………………....... 10
2.1.2 子影像的位移變形與假設…………………………………….….. 13
2.1.3 影像平滑化………………..………………………..……………. 15
2.1.4 以correlation function判斷影像的相關性……………………… 16
2.1.5 DIC程式流程圖…………..………………………..…………….. 17
2.2 基因演算法…………………………………………………………….. 18
2.2.1 基因演算法基本架構與資料結構(data structure)…………… 18
2.2.2 定義目標函數與適應函數(object function&fitness function) 19
2.2.3 基本運算子………………..………………………..……………. 20
2.2.4 控制參數…….……………..………………………..…………… 21
2.2.5 改良型基因演算法…………..………………………..…………… 22
2.2.6 ARGA和數位影像關係法的結合…………………..…………… 24

三、 製造技術……………………………………………………………………... 30
3.1微機電系統的製造技術………………………………………………... 30
3.1.1 微細加工技術……………………………………………………... 31
3.2 試片取樣設計與製作…………………………………………………. 33
3.2.1 試片製作流程……………………………………………………... 34

四、 研究步驟及方法……………………………………………………………... 41
4.1 大試片實驗規劃……………………………………………………….. 41
4.1.1 試片取樣與實驗流程…………………………………………….. 41
4.2 微試片實驗規劃……………………………………………………….. 43
4.2.1 夾具設計與實驗流程…………………………………………….. 44
4.3 實驗儀器與設備……………………………………………………….. 46

五、 程式驗證與結果討論……………………………….………………….. 50
5.1 相關函數值驗證與結果…………………………….…………………. 50
5.2 Z-方向平移模式比較…….……………………….………………….. 55
5.3 變數搜尋方式比較……….……………………….………………….. 59
5.4 剛體運動測試………………………………………………………….. 61
5.5 試片拉伸測試………………………………………………………….. 67

六、 結論與建議………………………………………………………………….. 93
6.1 結論…………………………………………………………………….. 93
6.2 建議…………………………………………………………………….. 93

參考文獻…………………………………………………………………………….. 94
簡歷…………………………………………………………………………………. 97
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