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The motion of line defects (dislocations), the primary driver of plasticity, has been studied for almost a century but one of the most fundamental questions remains unsolved: what defines the maximum speed at which dislocations can propagate? Early interpretations based on elasticity theory suggest that dislocation motion should not exceed the transverse wave speed, but recent models and atomistic simulations predict that transverse wave speed is a forbidden speed but not the upper limit. We use femtosecond x-ray radiography to observe how dislocations in shock-compressed single-crystalline diamond travel with the plastic shock wavefront. The observed dislocation motions in the diamond show that dislocations can move faster than the transverse wave speed. As the ultrafast motion of dislocations causes unique behavior by which solids strengthen or fail, understanding the upper limit of dislocation mobility is critical to accurately model, predict, and control the mechanical properties of materials under extreme conditions.

In the process of imbalanced dataset classification, traditional oversampling technologies do not take into the account of the distribution characteristics of data sets, which easily results in low classification accuracy of positive samples. In order to improve the recognition rate of positive samples, the algorithm combining the K-means clustering with the MAHAKIL oversampling is proposed in this article. Firstly, the K-means clustering algorithm is used to divide the positive samples into K clusters. The samples in each cluster are then divided into two initial populations according to the magnitude of the Mahalanobis distance. Finally, the crossover operator of the genetic algorithm is used to continuously synthesize new data samples to balance the data set. The experimental results show that when K-means MAHAKIL oversampling algorithm is combined with different classification algorithms, it can achieve better classification performance, which verifies the effectiveness of the algorithm.

The extreme and repeated temperature variation during additive manufacturing of metal parts has a large effect on the resulting material microstructure and properties. The ability to accurately predict this temperature field in detail, and relate it quantitatively to structure and properties, is a key step in predicting part performance and optimizing process design. In this work, a finite element simulation of the Directed Energy Deposition (DED) process is used to predict the space- and time-dependent temperature field during the multi-layer build process for Inconel 718 walls. The thermal model is validated using the dynamic infrared (IR) images captured in situ during the DED builds, showing good agreement with experimental measurements. The relationship between predicted cooling rate, microstructural features, and mechanical properties is examined, and cooling rate alone is found to be insufficient in giving quantitative property predictions. Because machine learning offers an efficient way to identify important features from series data, we apply a 1D convolutional neural network (CNN) data-driven framework to automatically extract the dominant predictive features from simulated temperature history. The relationship between the CNN-extracted features and the mechanical properties is studied. To interpret how CNN performs in intermediate layers, we visualize the extracted features produced on each convolutional layer by a trained CNN. Our results show that the results predicted by the CNN agree well with experimental measurements and give insights into physical mechanisms of microstructure evolution and mechanical properties.

With the continuous increase of the running mileage and speed of the train, more and more wheels become out-of-roundness due to the collision and friction between the wheels and track. It has great significance to detect wheel polygon in order to ensure the safe operation of trains. The wheel out-of-roundness detection method based on POVMD and multinuclear LS-SVM is investigated by using POVMD algorithm to decompose the vibration signal, and then PSO to get optimal parameters which takes VMD algorithm into consideration. Such a method extracts some features from IMF components. Finally, Gaussian kernel function and directed acyclic graph classification method are chosen to build multinuclear classifier to detect wheel out-of-roundness. The experiment results show that the proposed method is effective to analyze wheel out-of-roundness.

Aiming at the fault of the urban rail train caused by the excessive abrasion of the pantograph slide plate, a pantograph sliding plate abrasion monitoring algorithm based on sub-pixel edge extraction is designed in this paper. This paper mainly analyzes the process of the pantograph slide’s wear monitoring algorithm. The experimental results show that the method proposed in this paper can effectively detect the abrasion curve of the pantograph slider and get the minimum residual abrasion value.

Aiming at the optimization problem of subway train energy conservation, this paper proposes a train energy-saving optimization method based on parallel immune particle swarm optimization. The parallel immune particle swarm optimization algorithm is used to optimize the train energy saving in two stages: firstly, the running time of each interval is fixed, the algorithm is used to search for the optimal working condition switching point, and then, the train running time is optimized under the premise of constant running time. Finally, using the real data of Beijing Yizhuang line to simulate, verify the feasibility of the model and algorithm.