| 模式识别—人工神经网络在改善汽车发动机缸体铸件力学性能方面的 … |
| |
| 引用本文： | 张兆春,吴铸,李重河,钦佩,陈念贻.模式识别—人工神经网络在改善汽车发动机缸体铸件力学性能方面的 …[J].金属学报,1998,34(10):1068-1072. |
| |
| 作者姓名： | 张兆春 吴铸 李重河 钦佩 陈念贻 |
| |
| 作者单位： | 中国科学院上海冶金研究所;上海,200050;中国科学院上海冶金研究所;上海,200050;中国科学院上海冶金研究所;上海,200050;中国科学院上海冶金研究所;上海,200050;中国科学院上海冶金研究所;上海,200050 |
| |
| 基金项目： | 福特─中国研究与发展基金!9716214 |
| |
| 摘 要： | 利用模式识别技术对影响汽车发动机缸体铸件抗拉强度和硬度的铁水化学成分以及浇注工艺参数进行了分析给出了影响铸件抗拉强度和硬度的主要因素的优化范围，利用经已知样本集训练的人工神经网络对铸件抗拉强度和硬度进行了预报，预报结果与实测值符合较好。
|
| 关 键 词： | 模式识别 人工神经网络 铸造 缸体 汽车发动机 |
| 收稿时间： | 1998-10-18 |
| 修稿时间： | 1998-10-18 |
APPLICATION OF PATTERN RECOGNITION AND ARTIFICIAL NEURAL NETWORK TO IMPROVE THE MECHANICAL PROPERTIES OF CYLINDER BLOCK CAST OF AUTOMOBILE ENGINE |
| |
| ZHANG Zhaochun,WU Zhu,LI Chonghe,QIN Pei,CHEN NianyiCorrespondent: WU Zhu,Tel: -,Fax:.APPLICATION OF PATTERN RECOGNITION AND ARTIFICIAL NEURAL NETWORK TO IMPROVE THE MECHANICAL PROPERTIES OF CYLINDER BLOCK CAST OF AUTOMOBILE ENGINE[J].Acta Metallurgica Sinica,1998,34(10):1068-1072. |
| |
| Authors: | ZHANG Zhaochun WU Zhu LI Chonghe QIN Pei CHEN NianyiCorrespondent: WU Zhu Tel: - Fax: |
| |
| Affiliation: | ZHANG Zhaochun,WU Zhu,LI Chonghe,QIN Pei,CHEN Nianyi(Shanghai Institute of Metallurgy,The Chinese Academy of Sciences,Shanghai )Correspondent: WU Zhu,Tel: ()-,Fax: () |
| |
| Abstract: | By means of pattern recoghtion technique the principal factors affecting thetensile strength and hardness of automobile cylinder block cast are discussed, and their optimizedrange determined also. Aritificial neural network, trained by known data, has been used topredict the values of both tensile strength and hardness. The predicted results agree well withthe experimental ones. It has been demonstrated that the mentioned optidsation techniqueprovides a technical approach to disprove the tensile strength and hardness of cylinder block castof automobile engine. |
| |
| Keywords: | pattern recognition multivariate analysis artificial neural network cast tensile strength hardness |
| 本文献已被 维普 等数据库收录！ |
| 点击此处可从《金属学报》浏览原始摘要信息 |
|
点击此处可从《金属学报》下载全文 |
|
