Evaluation Model of Aluminum Alloy Welded Joint Low-Cycle Fatigue Data Based on Information Entropy
AbstractAn evaluation model of aluminum alloy welded joint low-cycle fatigue data based on information entropy is proposed. 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. Results reveal that the total information entropy of the fatigue data based on nominal stress, structural stress and equivalent structural stress are, respectively, 0.9702, 0.8881, and 0.8294. There is consistency between the reducing trend of the weight-based information entropy and the smaller and smaller standard deviation of the S-N curves. In the structural stress based S-N curve, total stress concentration factor is crucial for the distribution of the fatigue data and the weight based information entropy of membrane stress concentration factor is 0.6754, which illustrates that stress concentration is a key issue of welded structure to which ought to be attached great importance. Subsequently, in the equivalent structural stress-based S-N curve, the weight based information entropy of stress ratio is 0.5759, which plays an important role in the distribution of fatigue data. With the importance level of the attributes on the S-N curves investigated, the correction of R in the equivalent structural stress based master S-N curve method should be carried out to make the welding fatigue prediction much more accurate. View Full-Text
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Liu, Y.; Zou, L.; Sun, Y.; Yang, X. Evaluation Model of Aluminum Alloy Welded Joint Low-Cycle Fatigue Data Based on Information Entropy. Entropy 2017, 19, 37.
Liu Y, Zou L, Sun Y, Yang X. Evaluation Model of Aluminum Alloy Welded Joint Low-Cycle Fatigue Data Based on Information Entropy. Entropy. 2017; 19(1):37.Chicago/Turabian Style
Liu, Yaliang; Zou, Li; Sun, Yibo; Yang, Xinhua. 2017. "Evaluation Model of Aluminum Alloy Welded Joint Low-Cycle Fatigue Data Based on Information Entropy." Entropy 19, no. 1: 37.
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