Abstract
An understanding of damage accumulation in structural steel materials is of vital importance to the fatigue community in both academia and industry. A novel entropy-based approach is introduced to characterize and assess the fatigue damage accumulation in Q235 steel material. The presented technique is based on acoustic emission (AE) testing taking account into the valuable signal parameters extracted from the captured AE signals in the combination of static and dynamic cyclic loading procedures. Data from AE parameters are used as inputs for a multicomponent variate D A, which provides efficient statistical description of the fatigue damage state, enabling an assessment by the entropy method. The key aspects of this investigation include (1) the AE test with a new experimental paradigm fusing static and dynamic cyclic loading procedures, (2) the establishment of a multicomponent variate D A-based AE data, and (3) the assessment of fatigue damage accumulation using entropy-based method. These results open perspectives for predicting fatigue life and real-time damage recognition in Q235 steel material.
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Acknowledgment
The authors would like to appreciate Dr. Steven F. Wayne’s kind help. Without the SEM analysis provided by him, this work couldn’t be completed so far. As a new graduate student, without my supervisor Prof. Li’s significant support, I couldn’t finish this work with only more than a half of a year’s AE learning. The same authors would like to thank Dr. Yingli Zhu (Tianjin University of Science and Technology) for his support and encouragement. The authors appreciate all the people who have offered help to this innovative work. Thanks again!
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Jia, Z., Li, J., Qi, G. (2019). An Entropy Approach for Characterization and Assessment of Fatigue Damage Accumulation in Q235 Steel Based on Acoustic Emission Testing. In: Shen, G., Zhang, J., Wu, Z. (eds) Advances in Acoustic Emission Technology. WCAE 2017. Springer Proceedings in Physics, vol 218. Springer, Cham. https://doi.org/10.1007/978-3-030-12111-2_17
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