人工神经网络方法预测P110油套管钢稀土渗铬层的厚度  

Thickness Prediction of Rare Earth Chromizing Coatings on P110 Oil Casing Tube Steel Based on Artificial Neural Network

作　　者：林乃明[1] 谢发勤[1] 钟涛[1] 吴向清[1] 田伟[1,2] 

机构地区：[1]西北工业大学航空学院,西安710072 [2]中国石油天然气集团公司管材研究所,西安710065

出　　处：《中国表面工程》2009年第6期77-80,共4页China Surface Engineering

基　　金：陕西省科学技术研究发展计划项目(2008K01-31)

摘　　要：利用人工神经网络原理,建立了反映P110油套管钢(P110钢)稀土渗铬工艺参数与渗铬层厚度关系的预测模型。结果显示:该模型可以对选定工艺条件下渗铬层厚度进行良好的预测,预测结果在合理的误差范围内,建立的模型是可靠的,可以通过此模型优化工艺参数,实现P110钢稀土渗铬层厚度预测。The artificial neural network theory was used to build a prediction model aimed at reflecting the relationship between process parameters and thickness of Rare Earth （RE） chromizing coatings on PllO oil casing tube steel （P110 steel）. The results indicated that thickness of chromizing coatings was well predicted at selected process parameters by this model. The predicted error lied at rational range, the built model was reliable and could be used to optimize process parameters, which presented an advanced and effective approach to solve the thickness prediction of RE chromizing coatings on P 110 steel.

关 键 词：人工神经网络 P110钢 油套管 稀土 渗铬 预测 

分 类 号：TG156.88[金属学及工艺—热处理]