Analysis of fatigue life factors of aluminum alloy welded joints based on neighborhood rough set theory

被引:0
|
作者
Wang C.-S. [1 ,2 ]
Zou L. [3 ]
Yang X.-H. [2 ]
机构
[1] Engineering and Technology Center, CRRC Changchun Railway Vehicles Co., Ltd., Changchun
[2] School of Materials Science and Engineering, Dalian Jiaotong University, Dalian
[3] Software Institute, Dalian Jiaotong University, Dalian
关键词
Fatigue life; Material synthesis and processing technology; Neighborhood rough set; Welded joints;
D O I
10.13229/j.cnki.jdxbgxb201706024
中图分类号
学科分类号
摘要
To sole the problem that classical rough set theory can only deal with continuous data, neighborhood rough set theory is employed to analyze the factors that influence the fatigue life of the welded joints. A unified neighborhood rough set model is proposed, which can handle both symbolic and numeric attributes. Attribute reduction algorithm combining forward greedy algorithm and backward pruning algorithm is used to obtain the key factors and to quantitatively calculate the weights of the factors influencing the fatigue life of the welded joints. Experiment results of aluminum alloy welded joints show that the proposed neighborhood rough set model can choose a small number of features and obtain an objective evaluation of various influence factors from the sample data of the aluminum alloy welded joints without any priori knowledge. © 2017, Editorial Board of Jilin University. All right reserved.
引用
收藏
页码:1848 / 1853
页数:5
相关论文
共 17 条
  • [1] Iqbal M., Shaikht M.A., Ahmad M., Et al., Ageing effect on hardness and microstructure of Al-Zn-Mg alloys, Journal of Materials Science and Technology, 16, 3, pp. 319-322, (2000)
  • [2] Shimizu K., Torii T., Ma Y.L., Crack opening sliding morphology and stress intensity factor of slant fatigue crack, Key Engineering Materials, 297-300, pp. 697-702, (2005)
  • [3] Meng G.-W., Li F., Zhao Y.-L., Reliability analysis of fatigue and fracture based on stochastic finite element method, Journal of Jilin University(Engineering and Technology Edition), 36, pp. 16-19, (2006)
  • [4] Yan C.-L., Hao Y.-X., Liu K.-G., Fatigue life prediction of materials based on BP neural networks optimized by genetic algorithm, Journal of Jilin University (Engineering and Technology Edition), 44, 6, pp. 1710-1715, (2014)
  • [5] Zou L., Yang X.-H., Sun Y.-B., Et al., Fatigue life prediction of aluminum alloy welded joint based on variable precision rough set, Transactions of the China Welding Institution, 34, 4, pp. 65-68, (2014)
  • [6] Yang X.-H., Zou L., Deng W., Fatigue life prediction for welding components based on hybrid intelligent technique, Material Science and Engineering A, 642, pp. 235-261, (2015)
  • [7] Pawlak Z., Rough sets, International Journal of Computer and Information Sciences, 11, 5, pp. 341-356, (1982)
  • [8] Wang G.-Y., Yao Y.-Y., Yu H., A survey on rough set theory and applications, Chinese Journal of Computers, 32, 7, pp. 1229-1246, (2009)
  • [9] Wang G.-Y., Zhang Q.-H., Ma X.-A., Et al., Granular computing models for knowledge uncertainty, Journal of Software, 22, 4, pp. 676-694, (2011)
  • [10] He Q., Wu C.X., Chen D.G., Et al., Fuzzy rough set based attribute reduction for information system with fuzzy decisions, Knowledge-Based Systems, 24, 5, pp. 689-696, (2011)