Prediction of the Casting Strip Thickness and Organized Grain during Inverse Solidification Process
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摘要: 基于人工神經網絡建立了反向凝固過程中的性能預測模型,實現了對鑄帶厚度和新相層晶粒度的全面預測;探討了凝固過程中的主要工藝參數對上述性能的綜合影響,為反向凝固性能的綜合預測提供了簡便的新手段.研究表明,新生相晶粒度隨鋼水過熱度、母帶厚度、浸入時間變化對其影響不顯著,而鋼水過熱度、母帶厚度、浸入時間變化對鑄帶厚度的影響較大.該模型的預測結果與實測的結果較為接近.Abstract: The artificial natural net can used to predict the property of the strip formed in the molten steel during inverse casting. The property including in casting thickness and new organized grain are comprehensively forecasted. The influences on the property are discussed by the main operated factors during inverse solidification. The new method to predict the property is provided. The new organized grain little changes with molten steel superheat, mother sheet thickness and dip time,but the cast sheet thickness greatly changes with these main operated factors..The predicted result of the model corresponds to the experienced result.
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Key words:
- inversion casting /
- strip thickness /
- grain /
- natural net
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