| 基于RS_RBFNN的钛合金焊接接头疲劳寿命预测 |
| |
| 引用本文： | 邹丽,杨鑫华,孙屹博,邓武. 基于RS_RBFNN的钛合金焊接接头疲劳寿命预测[J]. 焊接学报, 2015, 36(4): 25-29,78 |
| |
| 作者姓名： | 邹丽 杨鑫华 孙屹博 邓武 |
| |
| 作者单位： | 1.大连交通大学软件学院, 旅顺 116052 |
| |
| 基金项目： | 国家自然科学基金资助项目(5117504);教育部科学技术研究重点项目资助(210045);辽宁省科学技术计划项目资助(2011220039);重庆市重点实验室开放基金项目(CQ-LCI-2013-05);过程装备与控制工程四川省高校重点实验室开放基金项目(GK201405) |
| |
| 摘 要： | 建立了基于RS与RBF神经网络集成的钛合金焊接接头疲劳寿命预测模型(RS_RBFNN),该模型首先基于熵的连续属性离散化算法离散化疲劳数据并应用遗传算法约简疲劳寿命评价指标;基于最小约简指标提取焊接结构疲劳寿命分类判别规则以及对RBF神经网络进行训练;最后使用粗糙集理论判别与规则库匹配的检验样本疲劳寿命等级,使用RBF神经网络判别不与规则库任何规则匹配的检验样本疲劳寿命等级.基于钛合金疲劳试验数据的实证分析结果表明,RS_RBFNN模型容错性较好、精度较高,对钛合金焊接结构疲劳寿命预测具有一定的实际指导意义.
|
| 关 键 词： | 粗糙集 神经网络 焊接 疲劳 |
| 收稿时间： | 2013-09-29 |
Prediction of fatigue life of titanium alloy welded joints based on RS_RBFNN |
| |
| ZOU Li,YANG Xinhu,SUN Yibo and DENG Wu. Prediction of fatigue life of titanium alloy welded joints based on RS_RBFNN[J]. Transactions of The China Welding Institution, 2015, 36(4): 25-29,78 |
| |
| Authors: | ZOU Li YANG Xinhu SUN Yibo DENG Wu |
| |
| Affiliation: | 1.Software Institute, Dalian Jiaotong University, Lvshun 116052, China2.School of Materials Science and Engineering, Dalian Jiaotong University, Dalian 1160283.Software Institute, Dalian Jiaotong University, Lvshun 116052, China;Chongqing Key Laboratory of Computational Intelligence, Chongqing 400065, China |
| |
| Abstract: | An integrated model of rough set and neural network (RS_RBFNN) was proposed for predicting fatigue life of titanium alloy welded joints. The fatigue data were discretized by using the entropy-based algorithm, and the fatigue evaluation indices were reduced without information loss through a genetic algorithm. The reduced indices were used to develop the rules for fatigue life of welded joints and to train the RBF neural network. The rough set theory was used to determine the category of fatigue life for the test samples which matched the rules in the rule-base. The neural network was applied to those test samples which did not match any rules in the rule-base. Experimental results based on the fatigue data of titanium alloy show that the RS_RBFNN model for fatigue analysis of welded joints had improved fault tolerance and precision. Therefore this model is of practical significance for predicting fatigue life of titanium alloy welded joints. |
| |
| Keywords: | @@ |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《焊接学报》浏览原始摘要信息 |
|
点击此处可从《焊接学报》下载免费的PDF全文 |
|
