| 研究生: |
蘇保鳴 Su, Bau-Ming |
|---|---|
| 論文名稱: |
熱軋低碳鋼之相變態數學模型 Modeling Phase Transformation in Hot Rolled Low Carbon Steel |
| 指導教授: |
郭瑞昭
Kuo, Jui-Chao |
| 學位類別: |
碩士 Master |
| 系所名稱: |
工學院 - 材料科學及工程學系 Department of Materials Science and Engineering |
| 論文出版年: | 2011 |
| 畢業學年度: | 99 |
| 語文別: | 中文 |
| 論文頁數: | 85 |
| 中文關鍵詞: | 熱力學模型 、相變態模型 、熱膨脹 、電子背向散射 |
| 外文關鍵詞: | Thermodynamic model, Phase transformation, Dilatometer, EBSD |
| 相關次數: | 點閱:91 下載:3 |
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本研究以數學模型預測熱軋低碳鋼經冷卻後相變態顯微組織,再與實驗所獲得相比例以及晶粒大小結果比對。將數學模型分為兩部分,第一為加熱過程的沃斯田鐵晶粒大小之預測,及第二為冷卻過程中相變態之預測,其中考慮加工應變量即差排密度之因素。
加熱過程的沃斯田鐵晶粒大小之預測,其晶粒大小的數學模型預測採用與時間和溫度有關的恆溫成長公式。而冷卻過程中相變態之預測,相比例的模擬透過熱力學機制以OE(Ortho-Equilibrium)、PE(Para- Equilibrium)兩種平衡條件計算相轉換溫度,再經由動力學機制進行相變態率的累加,而獲得相變態後相比例;晶粒大小的模擬則利用半經驗式進行預測。
實驗則分別由光學顯微鏡及電子背向散射(EBSD)觀察相變態後的沃斯田鐵、變韌鐵、波來鐵和麻田散鐵。以光學顯微鏡可以觀察及計算肥粒鐵的相比例和晶粒大小,但對於沃斯田鐵、變韌鐵、波來鐵和麻田散鐵的比例則無法明確辨識。藉由EBSD的相鑑定分辨出肥粒鐵、沃斯田鐵和麻田散鐵,而變韌鐵和波來鐵則可透過IQ(Image Quality)方式與肥粒鐵區別出來。
模擬結果與實驗比較,沃斯田鐵晶粒成長公式以成長指數n為2最接近實驗結果;相比例的預測結果PE比OE更符合實驗結果;晶粒大小預測式以Suehiro公式最符合低碳鋼的預測;應變對低冷卻速率的相變態率影響不大。
In this study a mathematical model was developed to predict phase transformation of hot rolled low-carbon steels after cooling. The mathematical model is used to predict the grain size of austenite during heating and the phase transformation under cooling. The estimation of grain size is calculated using a semi-empirical equation. The computation of phase transformation is based on ortho-equilibrium (OE) and para- equilibrium (PE). The results predicted by the mathematical model were compared with those results of experiments.
The microstructures of austenite, ferrite pearlite, bainite and martensite were characterized using optical microscope (OM) and electron backscatter diffraction (EBSD). The image quality (IQ) in EBSD was used to distinguish between bainite and ferrite.
The index n of 2 was employed for calculating the grain size of austenite. In comparison with experimental results the results of PE model is better than these of the OE model for predicting phase fractions. The mathematical model proposed in this study was successfully to predict phase transformation during cooling for hot rolled low-carbon steels. In addition the image quality (IQ) in EBSD was employed to identify ferrite, pearlite and bainite.
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