柳杉(Cryptomeria japonica)為臺灣中北部山區常見的一種人工林資源，同時也是臺灣重要的林業經濟樹種。當柳杉原木經過加工製成板材時，每一片板材會由於裁切方向與表面特徵的不同而產生不同的彈性模數(Modulus of Elasticity, MOE)。
本研究透過國立清華大學光測力學實驗室所研發之三維木材纖維方向掃描光學系統(Three-dimensional Fiber Orientation Scanning System, 3D FOSS)拍攝木材表面之雷射散斑影像，將量測到的平面內纖維方向角α和平面外纖維角β，代入應力應變關係式與全域座標轉換式來獲得預測MOE(即E_(local,pred))，而整個試片的E_(local,pred)之平均即為E_(global,pred)。同時，透過掃描木材的X-ray CT影像（X-ray Computed Tomography Image），獲得木材內部的早材、晚材與節點等特徵分布，並依此繪製成三維有限單元法模型。將E_(global,pred)、蒲松比、剪力模數和四點抗彎實驗的條件輸入有限單元法模擬，來預測木材受力後的位移、應力與應變等。最終，本研究利用三維數位影像相關（Three-dimensional Digital Image Correlation, 3D-DIC）法結合四點抗彎實驗，以獲得實驗位移、應變與實驗MOE(E_4P)。
實驗結果證實模擬的位移與實驗位移數值非常相近且分布類似，而模擬應變與實驗應變的數值皆落在相近的範圍內，且分布情況相似。而試片如果受心材化影響，則會使得整體E_(global,pred)值較E_4P值低，若是沒有受到心材化影響的試片，則E_(global,pred)值與E_4P值相近。未來可透過機器學習或深度學習來改良E_(global,pred)值，也可以對心材重新取樣來修改心材的β與短長軸比關係式，以改善E_(global,pred)值。

Japanese cedar (Cryptometria japonica), is a prevalent artificial forest resource in the mountainous areas of northern and central Taiwan. It is also an important species for Taiwan's forestry economy. When wood logs of Japanese cedar are processed into timbers, due to variations in cutting directions and surface characteristics of each piece of timber, different values of modulus of elasticity (MOE) will be exhibited.
In this research, the three-dimensional fiber orientation scanning system (3D FOSS) developed in the Photomechanics Laboratory at National Tsing Hua University was used to capture laser speckle images of the wood surface. The measured in-plane fiber direction angle α and out-of-plane fiber direction angle β were then incorporated into the stress-strain relationship and global coordinate transformation to obtain the predicted modulus of elasticity (E_(local,pred)). The average E_(local,pred) of the entire specimen is considered as E_(global,pred). Simultaneously, X-ray computed tomography images of the scanned wood were used to acquire information about the distribution of earlywood, latewood, and knots within the wood, a three-dimensional finite element model was created. The MOE and conditions of the four-point bending experiment were input into the finite element simulation to predict the displacement, stress, and strain after the wood is subjected to external forces. Finally, the three-dimensional digital image correlation (3D-DIC) in conjunction with the four-point bending experiment were utilized to obtain experimental displacement, strain, and experimental MOE(E_4P).
The experimental results confirms that the simulated displacement values closely match the experimental values and exhibit similar distributions. Additionally, the simulated strain values align closely with the experimental strain values, both values are within almost the same range of strain values. In addition, the distribution patterns of both values are similar. If the specimen is influenced by heartwood formation, it leads to a lower overall E_(global,pred) value compared to the E_4P value. In contrast, specimens not affected by heartwood formation, values of E_(global,pred) and E_4P are close to each other. In the future, accurate prediction of E_(global,pred) can be achieved through machine learning or deep learning techniques. Furthermore, by collecting data from the heartwood and modifying relationship between β and the minor-to- major axis ratio of heartwood, the predicted values of E_(global,pred) may be improved.

目錄
目錄----i
表目錄----vi
圖目錄----vii
一、簡介----1
1.1 研究動機----1
1.2 研究目的----2
1.3 研究流程----3
二、文獻回顧----5
2.1 木材簡介----5
2.2 影響木材強度之因素----7
2.3 木材缺陷分類----9
2.4 管胞效應----11
2.5 木材X-ray CT影像----12
三、實驗原理----14
3.1 管胞效應之應用----14
3.1.1 橢圓方程式----14
3.1.2 纖維角度檢測----18
3.2 木材之彈性模數----19
3.3 X-ray CT影像[23-28]----26
3.4 有限單元法[29-31]----29
3.4.1 求解過程[30]----29
3.4.2 非線性分析[31]----35
3.5 數位影像相關法----39
3.5.1 位移和變形[33-34]----39
3.5.2 相關函數[33-34]----41
3.5.3 三維數位影像相關法[35-37]----42
3.6 靜態抗彎實驗----46
四、實驗裝置----48
4.1 實驗樹種與試片----48
4.1.1 重量、體積、密度量測----50
4.1.2 柳杉的機械性質----50
4.2 木材纖維量測系統----51
4.2.1 光學架設模組----51
4.2.2 三軸位移平臺----52
4.2.3 3D FOSS系統整合軟體----52
4.3 掃描器----53
4.4 X-ray CT影像軟硬體----54
4.5 Ansys WorkBench軟體----54
4.6 四點抗彎與3D-DIC實驗----55
五、實驗程序----57
5.1 3D FOSS拍攝與分析程序----58
5.1.1 3D FOSS拍攝程序----58
5.1.2 3D FOSS分析程序----59
5.2 早材和晚材MOE分析程序----60
5.3 X-ray CT影像掃描與分析程序----61
5.3.1 X-ray CT影像掃描程序----61
5.3.2 X-ray CT影像分析程序----62
5.4 三維有限單元法模型建立----62
5.4.1 Engineering Data----63
5.4.2 Geometry----64
5.4.3 Model----65
5.5 四點抗彎與3D-DIC實驗----72
六、結果與討論----74
6.1 3D FOSS結果----74
6.1.1 試片B62結果----75
6.1.2 試片C31結果----77
6.2 X-ray CT影像拍攝結果----79
6.2.1 試片B62結果----79
6.2.2 試片C31結果----80
6.3 有限單元法模型模擬結果----83
6.3.1 試片B62結果----83
6.3.2 試片C31結果----87
6.4 搭配3D-DIC之四點抗彎實驗結果----93
6.4.1 試片B62結果----94
6.4.2 試片C31結果----95
七、結論與未來展望----98
7.1 結論----98
7.2 未來展望----101
參考文獻----104
附錄A：Canny邊緣檢測法----265
附錄B：Otsu自動閾值選擇算法----266
附錄C：網格品質的收斂性----267
附錄D：年輪傾斜角對木材強度的影響----269

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