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刘亚良1,2,孙屹博1,2,邹丽1,2,杨鑫华1,2. 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
引用本文: 刘亚良1,2,孙屹博1,2,邹丽1,2,杨鑫华1,2. 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 Yaliang1,2, SUN Yibo1,2, ZOU Li1,2, YANG Xinhua1,2. 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
Citation: LIU Yaliang1,2, SUN Yibo1,2, ZOU Li1,2, YANG Xinhua1,2. 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

基于信息熵的铝合金焊接接头疲劳寿命分析方法

Fatigue life analysis method of aluminum alloy welded joints based on information entropy

  • 摘要: 建立了基于信息熵的铝合金焊接接头疲劳数据分析模型,通过对比分析各决策属性的信息分熵,研究了应力集中、板厚与载荷类型等因素对焊接结构疲劳破坏的定量贡献. 结果表明,三种应力类型疲劳数据分布的信息总熵分别为0.997 7,0.910 0和0.817 9,基于权重信息熵的降低的总趋势与S-N曲线标准偏差越来越小的情况存在一致性. 对于基于结构应力的疲劳数据分布,膜应力集中系数(SCFm)基于权重的信息分熵为0.675 4,对于结构应力疲劳数据分布起主要作用;对于等效结构应力,应力比的基于权重的信息分熵为0.622 3,说明应力比对于等效结构应力疲劳数据分布起重要作用.

     

    Abstract: An analysis model of aluminum alloy welded joints fatigue data based on information entropy is proposed in this paper. Through calculating and analyzing the information entropy of decision attributes, quantitative contribution of stress concentration, plate thickness and loading mode to the fatigue destruction are researched. The results show that the total information entropy of the fatigue data based on three stress types are respectively 0.997 7, 0.910 0 and 0.817 9. There is consistency between the reducing trend of the weighted information entropy and the declining standard deviation of the S-N curves. In the S-N curve based on the structure stress, the weighted information entropy of membrane stress concentration factor (SCFm) is 0.675 4, which plays a major role in the distribution of fatigue data. In the S-N curve based on the equivalent structure stress, the weighted information entropy of stress ratio is 0.622 3, which indicates that stress ratio is an important factor that influences the distribution of fatigue data.

     

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