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[1]吴和保,李晓微,龙玉阳,等.人工神经网络快速预测蠕墨铸铁的性能[J].武汉工程大学学报,2013,(10):63-67.[doi:103969/jissn16742869201310013]
　WU He\|bao,LI Xiao\|wei,LONG Yu\|yang,et al.Fast prediction of vermicular graphite cast iron property based on Back Propagation neutral network[J].Journal of Wuhan Institute of Technology,2013,(10):63-67.[doi:103969/jissn16742869201310013]
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人工神经网络快速预测蠕墨铸铁的性能(/HTML)
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《武汉工程大学学报》[ISSN:1674-2869/CN:42-1779/TQ]

卷:
期数:
2013年10期
页码:
63-67
栏目:
机电与信息工程
出版日期:
2013-11-10

文章信息/Info

Title:
Fast prediction of vermicular graphite cast iron property based on Back Propagation neutral network
文章编号:
16742869(2013)10006305
作者:
吴和保1; 李晓微1; 龙玉阳1; 张亚平1; 樊自田2; 蔡安克3; 董选普2
1.武汉工程大学机电工程学院，湖北 武汉 430074;2.华中科技大学材料科学与工程学院，湖北 武汉 430074;3.中国一拖集团有限公司工艺材料研究所，河南 洛阳 471003
Author(s):
WU He\|bao1; LI Xiao\|wei1; LONG Yu\|yang1; ZHANG Ya\|ping1; FAN Zi\|tian2; CAI An\|ke3; DONG Xuan\|pu2
1. School of Mechanical and Electrical Engineering, Wuhan Institution of Technology, Wuhan 430074, China； 2.School of materials science and engineering of Huazhong University of Science and Technology, Wuhan 430074,China； 3.Technology and Material Research Institute of Yi Tuo Group Co．，Ltd，Luoyang 471003，China
关键词:
神经网络; 蠕墨铸铁; 热分析
Keywords:
Back Propagation neural network;  compacted graphite iron;  thermal analysis
分类号:
TG255
DOI:
103969/jissn16742869201310013
文献标志码:
A
摘要:
蠕墨铸铁具有优异的力学性能、铸造性能、抗热疲劳和耐磨性能，是大功率柴油发动机缸体的理想合金材料.蠕墨铸铁容易受到生产工艺因素的影响，蠕化率难以控制，必须依靠炉前快速分析技术加以严格控制.以蠕墨铸铁的化学成分和力学性能为研究对象，在大量实验数据的基础上，采用Matlab 软件中的误差反向传播算法神经网络工具箱，通过二次开发建立了一个基于热分析的预测网络，实现蠕墨铸铁性能的炉前化学成分和力学性能的快速预测，并与理化实测数据进行分析对比.结果表明，人工神经网络能充分逼近复杂的非线性系统，准确快速地预测蠕墨铸铁的化学成分和力学性能，有利于蠕墨铸铁的蠕化率、化学成分和力学性能的炉前快速监控，确保蠕化处理效果的稳定性，提高产品质量，降低铸铁的生产成本.
Abstract:
Vermicular Graphite Cast Iron is the ideal alloy for high\|power diesel engine block for its excellent mechanical property, casting performances, thermal fatigue resistance and great wearability. The chemical composition and mechanical properties of Vermicular Graphite Cast Iron were researched. The secondary development which is involved in Back Propagation neural network toolbox of Matlab software was used to set up a thermal analysis predicting network based on experimental data. The rapid prediction of chemical composition and mechanical properties of Vermicular Graphite Cast Iron was achieved through the secondary development. Compared with physical measurement data, the results show that chemical composition and mechanical properties of Vermicular Graphite Cast Iron is quickly and accurately predicted by Back Propagation neural network which is approximate to the complex non\|linear system. So vermicular iron vermicularity, chemical composition and properties of Vermicular Graphite Cast Iron can be rapidly monitored to ensure the stability of the vermicularizing treatment, to improve the product quality and to reduce the cost of production.

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备注/Memo

备注/Memo:
收稿日期：20131014作者简介：吴和保（1963\|），男，湖北麻城人，教授，博士.研究方向：金属凝固理论及其数值模拟、金属表面处理与防护、液态金属精确成型、金属雾化制粉、材料自动化检测与控制.
更新日期/Last Update: 2013-11-11