Enhancing strength and electrical conductivity of Cu–Cr composite wire by two-stage rotary swaging and aging treatments

Wang, Long; Nie, Jinfeng; Zhao, Yongli

doi:10.1016/j.compositesb.2021.109567

Cited by 50 publications

(2 citation statements)

References 58 publications

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“…The aging prediction of the railway catenary is a field of great research value, which plays a significant role in promoting the railway transportation industry [1][2][3]. Many researchers use a combination of image processing and artificial intelligence to determine the degree of aging of the catenary.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Aging Prediction of Railway Catenary Based on Improved Kalman Filter

Li,

Wang,

et al. 2024

SDHM

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The aging prediction of railway catenary is of profound significance for ensuring the regular operation of electrified trains. However, in real-world scenarios, accurate predictions are challenging due to various interferences. This paper addresses this challenge by proposing a novel method for predicting the aging of railway catenary based on an improved Kalman filter (KF). The proposed method focuses on modifying the priori state estimate covariance and measurement error covariance of the KF to enhance accuracy in complex environments. By comparing the optimal displacement value with the theoretically calculated value based on the thermal expansion effect of metals, it becomes possible to ascertain the aging status of the catenary. To improve prediction accuracy, a railway catenary aging prediction model is constructed by integrating the Takagi-Sugeno (T-S) fuzzy neural network (FNN) and KF. In this model, an adaptive training method is introduced, allowing the FNN to use fewer fuzzy rules. The inputs of the model include time, temperature, and historical displacement, while the output is the predicted displacement. Furthermore, the KF is enhanced by modifying its prior state estimate covariance and measurement error covariance. These modifications contribute to more accurate predictions. Lastly, a low-power experimental platform based on FPGA is implemented to verify the effectiveness of the proposed method. The test results demonstrate that the proposed method outperforms the compared method, showcasing its superior performance.

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Section: Introductionmentioning

confidence: 99%

A Novel Method for Aging Prediction of Railway Catenary Based on Improved Kalman Filter

Li,

Wang,

et al. 2024

SDHM

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“…However, despite the success of Gr as a reinforcing phase in polymers [ 6 , 7 ] and ceramics [ 8 , 9 ], its application to metals is hampered by severe agglomeration and poor interfacial bonding with metal matrices [ 10 ]. This limits the application of Gr, especially for Gr-reinforced CuMCs, where the use of Gr often results in an enhanced strength at the cost of a reduced plasticity or EC [ 11 , 12 , 13 , 14 , 15 ]. For example, Shi et al [ 16 ] constructed a Gr network (GN) in a Cu matrix via powder metallurgy.…”

Section: Introductionmentioning

confidence: 99%

Simultaneously Enhancing the Strength, Plasticity, and Conductivity of Copper Matrix Composites with Graphene-Coated Submicron Spherical Copper

Yang

Liang

et al. 2022

Nanomaterials

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In this study, Cu matrix composites reinforced with reduced graphene oxide-coated submicron spherical Cu (SSCu@rGO) exhibiting both high-strength plastic product (UT) and high electrical conductivity (EC) were prepared. SSCu@rGO results in the formation of Cu4O3 and Cu2O nanotransition layers to optimize the interface combination. In addition, as a flow carrier, SSCu@rGO can also render graphene uniformly dispersed. The results show that SSCu@rGO has a significant strengthening effect on the Cu matrix composites. The relative density (RD) of the SSCu@rGO/Cu composites exceeds 95%, and the hardness, UT, and yield strength (YS) reach 106.8 HV, 14,455 MPa% (tensile strength (TS) 245 MPa, elongation (EL) 59%), and 119 MPa; which are 21%, 72%, and 98% higher than those of Cu, respectively. Furthermore, EC is 95% IACS (International Annealed Copper Standard), which is also higher than that of Cu. The strength mechanisms include transfer load strengthening, dislocation strengthening, and grain refinement strengthening. The plastic mechanisms include the coordinated deformation of the interface of the Cu4O3 and Cu2O nanotransition layers and the increase in the fracture energy caused by graphene during the deformation process. The optimized EC is due to SSCu@rGO constructing bridges between the large-size Cu grains, and graphene on the surface provides a fast path for electron motion. This path compensates for the negative influence of grain refinement and the sintering defects on EC. The reduced graphene oxide-reinforced Cu-matrix composites were studied, and it was found that the comprehensive performance of the SSCu@rGO/Cu composites is superior to that of the rGO/Cu composites in all aspects.

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Processing and microstructure analysis on the precision rotary swaging of inner-stepped aero-engine shaft

Tian

Cao

et al. 2022

Int J Adv Manuf Technol

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Enhancing strength and electrical conductivity of Cu–Cr composite wire by two-stage rotary swaging and aging treatments

Cited by 50 publications

References 58 publications

A Novel Method for Aging Prediction of Railway Catenary Based on Improved Kalman Filter

A Novel Method for Aging Prediction of Railway Catenary Based on Improved Kalman Filter

Simultaneously Enhancing the Strength, Plasticity, and Conductivity of Copper Matrix Composites with Graphene-Coated Submicron Spherical Copper

Processing and microstructure analysis on the precision rotary swaging of inner-stepped aero-engine shaft

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