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研究生:戴信良
研究生(外文):Hsin-Liang Tai
論文名稱:支持向量機於沃斯回火延性鑄鐵製程窗口之建模
論文名稱(外文):Modeling Processing Window of Austempered Ductile Iron by Support Vector Machine
指導教授:張柳春 楊棧雲
指導教授(外文):Liu-chun chang Chan-Yun Yang
學位類別:碩士
校院名稱:北台灣科學技術學院
系所名稱:機電整合研究所
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:2008
畢業學年度:96
語文別:中文
論文頁數:101
中文關鍵詞:沃斯回火延性鑄鐵殘留沃斯田鐵碳含量製程窗口支持向量機貝斯構架
外文關鍵詞:austempered ductile ironretained austenitecarbon ratioprocessing windowsupport vector machineprinciple of Bayesian fra
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大間隔(Large margin)之支持向量機具有高泛化性能的能力,本研究延用此一特性於沃斯回火熱處理參數之預測分析,以期建立一個較高之泛化模型。本論文研究利用文獻上收集的數據資料,以建立沃斯回火延性鑄鐵熱處理的各種模型,共分四個主題,包括(1)預測沃斯田鐵化末期延性鑄鐵之碳含量,(2)預測沃斯回火延性鑄鐵於沃斯回火熱處理後殘留沃斯田鐵的體積率,(3)預測沃斯回火延性鑄鐵的製程窗口,(4)最佳沃斯回火時間(對應最高殘留沃斯田鐵體積率之沃斯回火時間)。本研究首先分別收集文獻上沃斯延性鑄鐵進行沃斯回火熱處理的初始碳含量、體積率、製程窗口所需時間與最佳沃斯回火時間之數據資料,這四主題分別利用支持向量機(Support Vector Machine,簡稱SVM)與貝斯架構(Bayesian Framework)、重新代入驗證法(Resubstitution)配合交叉驗證(Cross Validation)以期獲得一個泛化性能較高的迴歸預測機制。實驗比較實際值和預測值之誤差,誤差值越小,表示預測的結果越接近其實際值,本研究之模型除考慮誤差值最小化之外,亦兼顧模型複雜度,並以泛化性能的高低作為一個評估的依據。研究結果顯示所獲得的迴歸模式,同時兼具寬容與準確的特性預測延性鑄鐵在沃斯田鐵化末期之碳含量、沃斯回火延性鑄鐵於沃斯回火後的殘留沃斯田鐵體積率、製程窗口和最佳沃斯回火時間四個主題。支持向量機以柔軟的迴歸特性所獲得的預測機制,一如預期的具有優越的泛化性能。
Characterized by a large margin learning machine, support vector machine is shown to exhibit a high generalization performance, which is employed here to model the austempering heat treatment for austempered ductile irons. Four most critical parameters of austempered ductile irons are addressed to assess the possibility of the present approach and further to establish austempering heat treatment model models, including the carbon content of ductile iron at the completion of austenization, volume fraction of retained austenite after austempering heat treatment, and the processing window of austempering heat treatment, the optimum austempering time (corresponding to the time for the maximum retained austenite volume fraction). Relevant data are first collected, including the initial carbon content in austempering heat treatment and the volume fraction of retained austenite after austempering heat treatment. For processing window, the time corresponding to 10% and 90% of retained austenite are taken to represent the start and end of process window. With cross validations, the support vector machine-based models are selected to achieving a high generalization accuracy according to the principles of Bayesian framework and Resubstitution. Hence, the study considers not only minimizing the training errors but also minimizing the model complexity to prevent the models from over-fitting. The results show that the regression models obtained exhibit favorable generalization capability. The promising results also indicate the practical use of support vector machine in modeling the heat treatment of austempered ductile irons is feasible.
中文摘要 i
ABSTRACT iii
謝誌 v
符號說明 vi
目 錄 vii
表目錄 xii
圖目錄 xiv
第1章 緒論 1
1.1. 研究動機 1
第2章 文獻回顧 3
2.1. 沃斯回火延性鑄鐵 3
2.2. 製程窗口 5
2.3. 沃斯田鐵化對製程窗口的影響 7
2.4. 沃斯回火對製程窗口的影響 8
2.5. 合金元素對製程窗口的影響 10
2.6. 預測製程窗口的重要性 12
第3章 支持向量機 13
3.1. 支持向量機 13
3.2. 支持向量機迴歸算法 14
3.3. 核函數 18
第4章 研究程序 19
4.1. 資料數據前處理 19
4.2. 訓練與驗證 21
4.2.1. 基於交叉驗證之參數設定法 22
4.2.2. 基於重新代入驗證之參數設定法 23
4.2.3. 基於貝斯架構之參數設定法 25
4.3. 研究流程 31
第5章 結果與討論 33
5.1. 數據前處理之檢討 33
5.2. 碳含量Cro迴歸模型及其一般化 41
5.2.1. 基於交叉驗證法之參數選擇 41
5.3. Vmax、t90R and t90L、tmax迴歸模型及其一般化 43
5.3.1. 參數之σ選擇 43
5.3.2. 基於貝斯架構之模型選擇 46
5.4. 沃斯田鐵化末期延性鑄鐵碳含量(Cro)之模型檢驗 55
5.5. 殘留沃斯田鐵的體積率(Vmax)之模型檢驗 58
5.6. 沃斯回火延性鑄鐵的製程窗口(t90L、t90R)之模型檢驗 59
5.7. 最佳沃斯回火時間(tmax)之模型檢驗 61
5.8. t90R、t90L與tmax之參數決定 62
第6章 結論 63
參考文獻 65
附錄一 73
圖A-1 殘留沃斯田鐵體積率輸入特徵x1(Tr沃斯田鐵化溫度) 之資料數據 73
圖A-2 殘留沃斯田鐵體積率輸入特徵x2(Si矽含量)之資料數據 74
圖A-3 殘留沃斯田鐵體積率輸入特徵x3(Mn錳含量)之資料數據 75
圖A-4 殘留沃斯田鐵體積率輸入特徵x4(Mo鎘含量)之資料數據 76
圖A-5 殘留沃斯田鐵體積率輸入特徵x5(Ni鎳含量)之資料數據 77
圖A-6 殘留沃斯田鐵體積率輸入特徵x6(Cu銅含量)之資料數據 78
圖A-7 製程窗口(t90R)輸入特徵x1(Tr沃斯田鐵化溫度)之資料數據 79
圖A-8 製程窗口(t90R)輸入特徵x2(Si矽含量)之資料數據 80
圖A-9 製程窗口(t90R)輸入特徵x3(Mn錳含量)之資料數據 81
圖A-10 製程窗口(t90R)輸入特徵x4(Mo鎘含量)之資料數據 82
圖A-11 製程窗口(t90R)輸入特徵x5(Ni鎳含量)之資料數據 83
圖A-12 製程窗口(t90R)輸入特徵x6(Cu銅含量)之資料數據 84
圖A-13 製程窗口(t90L)輸入特徵x1(Tr沃斯田鐵化溫度)之資料數據 85
圖A-14 製程窗口(t90L)輸入特徵x2(Si矽含量)之資料數據 86
圖A-15 製程窗口(t90L)輸入特徵x3(Mn錳含量)之資料數據 87
圖A-16 製程窗口(t90L)輸入特徵x4(Mo鎘含量)之資料數據 88
圖A-17 製程窗口(t90L)輸入特徵x5(Ni鎳含量)之資料數據 89
圖A-18 製程窗口(t90L)輸入特徵x6(Cu銅含量)之資料數據 90
圖A-19 最佳沃斯回火時間(tmax)輸入特徵x1(Tr沃斯田鐵化溫度)
之資料數據 91
圖A-20 最佳沃斯回火時間(tmax)輸入特徵x2(Si矽含量)之資料數據 92
圖A-21 最佳沃斯回火時間(tmax)輸入特徵x3(Mn錳含量)之資料數據 93
圖A-22 最佳沃斯回火時間(tmax)輸入特徵x4(Mo鎘含量)之資料數據 94
圖A-23 最佳沃斯回火時間(tmax)輸入特徵x5(Ni鎳含量)之資料數據 95
圖A-24 最佳沃斯回火時間(tmax)輸入特徵x6(Cu銅含量)之資料數據 96
附錄二 97
表B-1 殘留沃斯田鐵體積率(Vmax)資料集六個原始特徵值的範圍 97
表B-2 殘留沃斯田鐵體積率(Vmax)資料集正規化程序所使用的參數 97
表B-3 製成窗口(t90R)資料集六個原始特徵值的範圍 98
表B-4 製成窗口(t90R)資料集正規化程序所使用的參數 98
表B-5 製成窗口(t90L)資料集六個原始特徵值的範圍 99
表B-6 製成窗口(t90L)資料集正規化程序所使用的參數 99
表B-7 最佳沃斯回火時間(tmax)資料集六個原始特徵值的範圍 100
表B-8 最佳沃斯回火時間(tmax)資料集正規化程序所使用的參數 100
自 傳 101
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