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Titanium alloys are widely used in the manufacture of aero-engine blisks due to their low density, high strength, and excellent performance at medium and high temperatures. However, there are few studies on dynamic mechanical properties and adiabatic shear sensitivities of titanium alloy forgings for blisks. In this work, the dynamic mechanical properties of forged TC17 and TC4 alloys at high strain rates were examined by SHPB apparatus, and OM, SEM, EBSD were used to study the adiabatic shear behaviors of the two kinds of alloy. As the strain rate increases, the strength of both alloys increases, thus exhibiting the strain rate strengthening effect. At the same strain rate, TC4 alloy exhibits higher plastic strain and dynamic absorbed energy than those of TC17 alloy. TC17 alloy obtains a basket-weave microstructure after β forging, in which lath α-phases and residual β phases form more phase interfaces. ASBs tend to form at phase interfaces, which lead to a tendency for ASBs to bifurcate during propagating processes. TC4 alloy obtains a bimodal microstructure after α+β forging, and equiaxed primary α-phases show good ductility, which improve the dynamic plastic deformation ability. The regular arrangement of secondary α-phases results in fewer phase interfaces, leading to the difficulty in bifurcation of ASBs during propagating processes. Under dynamic interrupted compression conditions, ASBs in TC17 alloy are occurred earlier, and the localization energy of ASBs is low. Therefore, TC17 alloy has higher adiabatic shear sensitivity, and adiabatic shear sensitivities of both alloys increase with the increase of strain rates.

α+β titanium alloys have a wide application prospect in aero engine blisk manufacturing, owing to their high specific strength and good high-temperature performance. Whereas there are few studies on mechanical properties and plastic deformation modes of large-size blisk specimens at variable strain rate conditions. In this work, the effects of different strain rates on the mechanical properties of forged TC17 and TC4 alloys were examined by tensile testing machine, and Vickers hardness tester, OM, SEM, and EBSD were used to study the Vickers hardness, deformation microstructures and fracture morphologies. As increasing strain rate, the strengths of both alloys increase and the elongation decrease, showing positive strain rate sensitivities of flow stress, and the strain rate sensitivity coefficients decrease with true strain. Besides the solid solution strengthening of alloying elements, lath α-phases and residual β-phases in TC17 alloy with basket-weave microstructure are interweaved with each other, resulting in more phase interfaces. Phase interfaces are effective in blocking dislocations motion and lead to pile-up of dislocations, improving the strength of TC17 alloy. In addition, the voids are easier to nucleate at the phase interfaces and form dimples. Therefore, a lot of small dimples are observed on the fracture surface, which make the poorer ductility of TC17 alloy. Fewer phase interfaces exist in TC4 alloy with bimodal microstructure, which is related to the better plastic deformation capacity of equiaxed primary α-phases and the regular arrangement of secondary α-phases in transformed β-phases. The fewer phase interfaces increase the mean free path of dislocations and form fewer large dimples, which lead to a decrease in strength but an increase in ductility of TC4 alloy.

To quickly evaluate the safety of damaged bridge structures in the service process of cranes, a real-time prediction method for the fatigue life of bridge structures based on the optimal additive agent model is proposed. Based on the measured load data, the randomness of the relevant parameters of the service conditions is characterized by the combination of the working cycle process. In line with the most unfavorable service conditions, the stress weak position of the bridge structure is determined through finite element statics analysis and failure case statistics. And the structure finite element model is established for fatigue damage and life prediction. The MSC. Fatigue′s S-N module and crack propagation module are employed to simulate the residual life of the structure under random working conditions, and the influence of different crack depths on the residual life of the structure is analyzed. The Kriging model for the life prediction of the bridge structure that is optimized by the Pelican Optimization Algorithm and additive point criterion is created by the Latin Hypercube Sampling method and the black box theory. The real-time fatigue life prediction of bridge structures during trolley movement with load by Miner linear cumulative damage theory. The bridge structure of QD20/10 t × 43 m × 12 m general bridge crane is taken as an example to verify the feasibility of the proposed method. Comparing the proposed method with the traditional fatigue life calculation method based on finite element simulation, the results show that it can accurately assess the remaining life of the damaged bridge structure with fewer simulation calls and shorter simulation time, which can provide some reference significance for crane maintenance cycle formulation and scrapping decisions.

Stochastic computing is a kind of logic calculation that converts binary into probabilistic coded digital pulse stream. At the cost of computing power and time delay, it has the computing advantages of low power consumption and high energy efficiency. In this paper, the basic concept of stochastic computing is explained, and a stochastic computing circuit with single-channel or multi-channel is designed to improve the speed and accuracy. Based on the stochastic computing circuit, the stochastic pulse neuron is designed, and the reconfigurable computing architecture of neural network, BUAA-ChouSuan, is realized. The design is implemented with KINTEX-7 (FPGA), the logic resource (lookup table, LUT) of stochastic MAC (multiply accumulate) is 80% lower than that of traditional MAC. In SCNN (stochastic convolutional neural network) experiment, LeNet and AlexNet are tested. Under the condition of 350 MHz clock frequency, the average energy efficiency can reach 0.536 TSOPS/W, and the utilization rate of processing unit (PE) can reach more than 90%.

In the separation of scrap metal based on dual-energy X-ray transmission technology, the recognition effect is largely affected by the features of the selected materials. The features used in the existing scrap metal material recognition algorithms are not comprehensive enough, and the redundancy among feature data is large, so the recognition accuracy is not high. To solve this problem, this paper gives full consideration to the redundancy between features and the correlation between features and categories, and puts forward a correlation-based feature selection method(CFS) to determine the optimal feature subset consisting of features IH, IL, x TML and TMH among many material features. Comparative experiments were carried out by using different material feature combination methods, and K nearest neighbor (KNN) under the optimal feature subset was selected as the optimal classification model. The experimental results show that; compared with the existing classification methods, the identification and classification method of waste nonferrous metals materials based on the correlation-based feature selection and K nearest neighbor (CFS-KNN) has higher identification accuracy (96.13%) with relatively few features. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the effects of different mass fractions of Chickpea Protein Isolate (CPI) on the emulsifying property of pork myofibrillar protein. The results showed that the EAI, ESI and rheological storage modulus G' of pork myofibrillar protein emulsion increased gradually with the increase of the addition of CPI. When the addition of CPI was 1. 2%, the EAI and storage modulus G' of pork myofibrillar protein emulsion reached the maximum, and the total stable index and emulsification both reached the minimum. The results indicated that adding 1. 2% CPI could effectively improve the emulsification properties of pork myofibrillar protein, and enhance the stability of pork myofibrillar protein emulsion. [ABSTRACT FROM AUTHOR]