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About the Role of Interfaces on the Fatigue Crack Propagation in Laminated Metallic Composites
(2021)
The influence of gradients in hardness and elastic properties at interfaces of dissimilar materials in laminated metallic composites (LMCs) on fatigue crack propagation is investigated experimentally for three different LMC systems: Al/Al-LMCs with dissimilar yield stress and Al/Steel-LMCs as well as Al/Ti/Steel-LMCs with dissimilar yield stress and Young’s modulus, respectively. The damage tolerant fatigue behavior in Al/Al-LMCs with an alternating layer structure is enhanced significantly compared to constituent monolithic materials. The prevalent toughening mechanisms at the interfaces are identified by microscopical methods and synchrotron X-ray computed tomography. For the soft/hard transition, crack deflection mechanisms at the vicinity of the interface are observed, whereas crack bifurcation mechanisms can be seen for the hard/soft transition. The crack propagation in Al/Steel-LMCs was studied conducting in-situ scanning electron microscope (SEM) experiments in the respective low cycle fatigue (LCF) and high cycle fatigue (HCF) regimes of the laminate. The enhanced resistance against crack propagation in the LCF regime is attributed to the prevalent stress redistribution, crack deflection, and crack bridging mechanisms. The fatigue properties of different Al/Ti/Steel-LMC systems show the potential of LMCs in terms of an appropriate selection of constituents in combination with an optimized architecture. The results are also discussed under the aspect of tailored lightweight applications subjected to cyclic loading.
Distributed acoustic sensing (DAS) over tens of kilometers of fiber optic cables is well-suited for monitoring extended railway infrastructures. As DAS produces large, noisy datasets, it is important to optimize algorithms for precise tracking of train position, speed, and the number of train cars, The purpose of this study is to compare different data analysis strategies and the resulting parameter uncertainties. We present data of an ICE 4 train of the Deutsche Bahn AG, which was recorded with a commercial DAS system. We localize the train signal in the data either along the temporal or spatial direction, and a similar velocity standard deviation of less than 5 km/h for a train moving at 160 km/h is found for both analysis methods, The data can be further enhanced by peak finding as well as faster and more flexible neural network algorithms. Then, individual noise peaks due to bogie clusters become visible and individual train cars can be counted. From the time between bogie signals, the velocity can also be determined with a lower standard deviation of 0.8 km/h, The analysis methods presented here will help to establish routines for near real-time Train tracking and train integrity analysis.