Analysis of factors affecting fatigue life of welded joints based on improved conditional neighborhood entropy

ZOU Li; REN Siyuan; YANG Guang; YANG Xinhua

doi:10.12073/j.hjxb.20210323001

TRANSACTIONS OF THE CHINA WELDING INSTITUTION > 2021 > 42(11): 43-50. > DOI: 10.12073/j.hjxb.20210323001

ZOU Li, REN Siyuan, YANG Guang, YANG Xinhua. Analysis of factors affecting fatigue life of welded joints based on improved conditional neighborhood entropy[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2021, 42(11): 43-50. DOI: 10.12073/j.hjxb.20210323001

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Analysis of factors affecting fatigue life of welded joints based on improved conditional neighborhood entropy

ZOU Li^{1, 2,},
REN Siyuan^{1, 2},
YANG Guang^{1, 2},
YANG Xinhua^{1, 2, ,}

1.
Dalian Jiaotong University, Dalian, 116028, China
2.
Liaoning Key Laboratory of Welding and Reliability of Rail Transportation Equipment, Dalian，116028, China

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Received Date: March 22, 2021
Available Online: December 30, 2021

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Abstract

Abstract

The supervised strategy is introduced to improve the granule conditional neighborhood entropy, and an attribute reduction algorithm based on supervised granule conditional neighborhood entropy is proposed. Experiments are carried out on four open datasets. Experimental results show that the proposed algorithm has higher reduction rate and classification accuracy. Based on this algorithm, an analysis model of the fatigue life influencing factors of aluminum alloy welded joints was established. The coupling relationship among the fatigue life influencing factors of the welded joints is analyzed by using the mutual information theory. Analyzing results show that the stress concentration factor is most affected by the joint type and welding method, and least affected by the weld leg length. It indicates that the joint type and welding method should be major considerations when calculating the stress concentration factor. The weight of equivalent structural stress range on fatigue life is 0.461 2, and the weight of nominal stress range is 0.347 3, which indicates that after stress correction, the weight of equivalent structural stress range on fatigue life of the welded joints increases compared with nominal stress range, so it can predict fatigue life of the welded joints more accurately.
- conditional neighborhood entropy,
- welded joint,
- fatigue life,
- influencing factor

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References

刘刚, 唐柳伦, 黄一. 基于场强法的焊接接头疲劳寿命影响因素研究[J]. 船舶力学, 2014, 18(S1): 158 − 164.

Liu Gang, Tang Liulun, Huang Yi. A study on influence factors of fatigue in welded joints based on field intensity approach[J]. Journal of Ship Mechanics, 2014, 18(S1): 158 − 164.

王悦东, 葛巍, 李向伟. 焊接接头疲劳寿命评估中板厚参数的影响分析[J]. 大连交通大学学报, 2017, 38(1): 30 − 33.

Wang Yuedong, Ge Wei, Li Xiangwei. Effect analysis of plate thickness parameter in evaluation of welded joints fatigue life[J]. Journal of Dalian Jiaotong University, 2017, 38(1): 30 − 33.

Shin W S, Chang K H, Muzaffer S. Fatigue analysis of cruciform welded joint with weld penetration defects[J]. Engineering Failure Analysis, 2021, 120: 105111. doi: 10.1016/j.engfailanal.2020.105111

刘亚良, 孙屹博, 邹丽, 等. 基于信息熵的铝合金焊接接头疲劳寿命分析方法[J]. 焊接学报, 2018, 39(4): 67 − 72. doi: 10.12073/j.hjxb.2018390098

Liu Yaliang, Sun Yibo, Zou Li, et al. Fatigue life analysis method of aluminum alloy welded joints based on information entropy[J]. Transactions of the China Welding Institution, 2018, 39(4): 67 − 72. doi: 10.12073/j.hjxb.2018390098

杨龙, 杨冰, 阳光武, 等. 不锈钢车体点焊接头疲劳特性分析[J]. 焊接学报, 2020, 41(7): 18 − 24,52. doi: 10.12073/j.hjxb.20191204005

Yang Long, Yang Bing, Yang Guangwu, et al. Analysis on fatigue characteristics of spot welded joints of stainless steel car body[J]. Transactions of the China Welding Institution, 2020, 41(7): 18 − 24,52. doi: 10.12073/j.hjxb.20191204005

刘夕, 陈广冉, 孟庆禹, 等. 基于局部应变能密度的厚板T形接头焊趾和焊根疲劳性能分析[J]. 焊接学报, 2020, 41(9): 74 − 80.

Liu Xi, Chen Guangran, Meng Qingyu, et al. Fatigue performance analysis of weld toe and root of thick T-joint based on local strain energy density[J]. Transactions of the China Welding Institution, 2020, 41(9): 74 − 80.

Sinha A K, Namdev N. Feature selection and pattern recognition for different types of skin disease in human body using the rough set method[J]. Network Modeling Analysis in Health Informatics and Bioinformatics, 2020, 9(1): 1 − 11. doi: 10.1007/s13721-019-0207-3

Shylaja B S, Bhaskar R. Rough-set and machine learning-based approach for optimised virtual machine utilization in cloud computing[J]. IET Networks, 2020, 9(6): 279 − 283. doi: 10.1049/iet-net.2020.0001

Singh M, Pamula R. An outlier detection approach in large-scale data stream using rough set[J]. Neural Computing and Applications, 2020, 32(3): 9113 − 9127.

Qu J, Bai X, Gu J, et al. Assessment of rough set theory in relation to risks regarding hydraulic engineering investment decisions[J]. Mathematics, 2020, 8(8): 1308. doi: 10.3390/math8081308

Xie J, Shen X, Liu H, et al. Research on an incremental attribute reduction based on relative positive region[J]. Journal of Software, 2013, 9(16): 6621 − 6628.

Wang C, Ou F. An attribute reduction algorithm in rough set theory based on information entropy [C]//2008 International Symposium on Computational Intelligence and Design. IEEE, 2008: 3-6.

王春生, 邹丽, 杨鑫华. 基于邻域粗糙集的铝合金焊接接头疲劳寿命影响因素分析[J]. 吉林大学学报(工学版), 2017, 47(6): 1848 − 1853.

Wang Chunsheng, Zou Li, Yang Xinhua. Analysis of fatigue life factors of aluminum alloy welded joints based on neighborhood rough set theory[J]. Journal of Jilin University (Engineering and Technology Edition), 2017, 47(6): 1848 − 1853.

邹丽, 杨鑫华, 孙屹博, 等. 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.

Zou Li, Yang Xinhua, Sun Yibo, et al. 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.

Liu Y, Zou L, Sun Y, et al. Evaluation model of aluminum alloy welded joint low-cycle fatigue data based on information entropy[J]. Entropy, 2017, 19(1): 37. doi: 10.3390/e19010037

Zou L, Yang X, Tan J, et al. Fatigue life prediction of 5083 and 5A06 aluminum alloy T-welded joints based on the fatigue characteristics domain[J]. Fracture and Structural Integrity, 2018, 12(45): 53 − 66.

Zou L, Sun Y, Yang X. An entropy-based neighborhood rough set and PSO-SVRM model for fatigue life prediction of titanium alloy welded joints[J]. Entropy, 2019, 21(2): 117. doi: 10.3390/e21020117

Zou L, Li H, Jiang W, et al. An improved fish swarm algorithm for neighborhood rough set reduction and its application[J]. IEEE Access, 2019, 7: 90277 − 90288.

Zou L, Ren S, Li H, et al. An optimization of master s-n curve fitting method based on improved neighborhood rough set[J]. IEEE Access, 2021, 9: 8404 − 8420.

Jiang Z, Liu K, Yang X, et al. Accelerator for supervised neighborhood based attribute reduction[J]. International Journal of Approximate Reasoning, 2020, 119: 122 − 150. doi: 10.1016/j.ijar.2019.12.013

周艳红, 张贤勇, 莫智文. 粒化单调的条件邻域熵及其相关属性约简[J]. 计算机研究与发展, 2018, 55(11): 2395 − 2405. doi: 10.7544/issn1000-1239.2018.20170607

Zhou Yanhong, Zhang Xianyong, Mo Zhiwen. Conditional neighborhood entropy with granulation monotonicity and its relevant attribute reduction[J]. Journal of Computer Research and Development, 2018, 55(11): 2395 − 2405. doi: 10.7544/issn1000-1239.2018.20170607

胡清华, 于达仁, 谢宗霞. 基于邻域粒化和粗糙逼近的数值属性约简[J]. 软件学报, 2008(3): 640 − 649.

Hu Qinghua, Yu Daren, Xie Zongxia. Numerical attribute reduction based on neighborhood granulation and rough approximation[J]. Journal of Software, 2008(3): 640 − 649.

Qian W, Long X, Wang Y, et al. Multi-label feature selection based on label distribution and feature complementarity[J]. Applied Soft Computing Journal, 2020, 90: 106167. doi: 10.1016/j.asoc.2020.106167

Cruz J, Costa J, Borrego L, et al. Fatigue life prediction in AlMgSi1 lap joint weldments[J]. International Journal of Fatigue, 2000, 22(7): 601 − 610. doi: 10.1016/S0142-1123(00)00023-2

Beretta S, Sala G. A model for fatigue strength of welded lap joints[J]. Fatigue & Fracture of Engineering Materials & Structures, 2005, 28(1-2): 257 − 264.

Sidhom N, Laamouri A, Fathallah R, et al. Fatigue strength improvement of 5083 H11 Al-alloy T-welded joints by shot peening: experimental characterization and predictive approach[J]. International Journal of Fatigue, 2005, 27(7): 729 − 745. doi: 10.1016/j.ijfatigue.2005.02.001

Sidhom N, Braham C, Lieurade H P. Fatigue life evaluation of shot peened Al-alloys 5083 H11 T-welded joints by experimental and numerical approaches[J]. Welding in the World, 2007, 51(1-2): 50 − 57. doi: 10.1007/BF03266548

邹丽. 焊接接头疲劳寿命智能评价方法及其应用研究[D]. 大连: 大连交通大学, 2017.

Zou Li. Intelligent evaluation method for fatigue life of welded joints and its application[D]. Dalian: Dalian Jiaotong University, 2017.

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