Microstructure and Precipitate's Characterization of the Cu-Ni-Si-P Alloy

Zhang, Yi; Tian, Baohong; Volinsky, Alex A.; Sun, Haoliang; Chai, Zhe; Liu, Ping; Chen, Xiaohong; Liu, Yong

doi:10.1007/s11665-016-1987-6

Cited by 39 publications

(11 citation statements)

References 24 publications

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“…To solve this contradiction, the scheme recommended by MLDS is to increase the concentration of P element, as the trace P element can purify the matrix and promote the precipitation of the second phase of Cu-Ni-Si alloys, which is beneficial to improving the EC of Cu-Ni-Si alloys. 39,40 Thus, the results obtained by using MLDS model for alloy composition design conform to the basic principles of metallurgy, and further confirm the feasibility of alloy composition design by MLDS method based on machine learning.…”

Section: Construction and Application Of A Property-oriented Mldssupporting

confidence: 70%

A property-oriented design strategy for high performance copper alloys via machine learning

et al. 2019

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Traditional strategies for designing new materials with targeted property including methods such as trial and error, and experiences of domain experts, are time and cost consuming. In the present study, we propose a machine learning design system involving three features of machine learning modeling, compositional design and property prediction, which can accelerate the discovery of new materials. We demonstrate better efficiency of on a rapid compositional design of high-performance copper alloys with a targeted ultimate tensile strength of 600-950 MPa and an electrical conductivity of 50.0% international annealed copper standard. There exists a good consistency between the predicted and measured values for three alloys from literatures and two newly made alloys with designed compositions. Our results provide a new recipe to realize the property-oriented compositional design for highperformance complex alloys via machine learning.

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Section: Construction and Application Of A Property-oriented Mldssupporting

confidence: 70%

A property-oriented design strategy for high performance copper alloys via machine learning

et al. 2019

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“…In order to investigate the effect of a fourth element in the precipitation process, simulations were performed with 2.5 at.% Ni and 1.25 at.% Si and different concentrations of Cr, Fe, Al, or Mg. The concentration of Ni and Si were chosen by averaging over some experimentally investigated materials . As explained in Section , cohesive energy, vacancy formation energy, vacancy migration energy, and diffusion constant of copper were used for all elements in the alloy.…”

Section: Resultsmentioning

confidence: 99%

Precipitation in a copper matrix modeled byab initiocalculations and atomistic kinetic Monte Carlo simulations

Sajadi

Hocker

Mora

et al. 2016

Phys. Status Solidi B

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A kinetic Monte Carlo approach is used to study the influence of Cr, Fe, Al, or Mg addition on the precipitation in a Cu–Ni–Si alloy. The simulation method is based on a vacancy diffusion model. The crucial parameters of this method are the pairwise mixing energies of all contained elements which are determined by ab initio calculations. The number of dissolved atoms in equilibrium state is used to estimate the influence of Cr, Fe, Al, or Mg on the electrical conductivity. In order to estimate the influence of the alloying elements on strength ab initio calculations of misfit strain at the normalCu/italicNi3Si interface are performed. The atomistic kinetic Monte Carlo (AKMC) simulations reveal that Cr, Fe, Al, and Mg atoms are located at different positions: Mg and Al atoms are preferably located at the interface of Ni–Si precipitates and Cu matrix, Cr atoms diffuse into Ni–Si precipitates, and Fe atoms form Fe clusters surrounded by Ni–Si shells. Cr addition leads to a significantly reduced fraction of atoms dissolved in the matrix which indicates an increase of electrical conductivity, whereas Mg addition results in a high misfit strain at the normalCu/italicNi3Si interface which can contribute to increased strength.

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“…The results showed that the desired combination of the 690.13 MPa and 67%IACS were obtained after a primary 30% thickness reduction, aging at 450°C for 2 h and a secondary 60% thickness reduction at the cryogenic temperature ( Figure 2). Zhang et al [31] prepared Cu-0.4Cr-0.15Zr-0.05Ce (wt.%) alloy by cold rolling and aging. They found that the hardness and electrical conductivity of the alloy could reach 170 HV and 66%IACS, respectively, after 80% cold rolling and aging for 16 h at 300°C ( Figure 3).…”

Section: Research Progresses In Hshc Cu Alloys 31 Cu-cr-zr Systemmentioning

confidence: 99%

“…Color online) Cold deformation effect on (a) hardness and (b) conductivity of the Cu-Cr-Zr-Ce alloy aged at 300°C[31].…”

mentioning

confidence: 99%

High strength and high conductivity Cu alloys: A review

Yang

Lei

et al. 2020

Sci. China Technol. Sci.

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High strength and high conductivity (HSHC) Cu alloys are widely used in many fields, such as high-speed electric railway contact wires and integrated circuit lead frames. Pure Cu is well known to have excellent electrical conductivity but rather low strength. The main concern of HSHC Cu alloys is how to strengthen the alloy efficiently. However, when the Cu alloys are strengthened by a certain method, their electrical conductivity will inevitably decrease to a certain extent. This review introduces the strengthening methods of HSHC Cu alloys. Then the research progress of some typical HSHC Cu alloys such as Cu-Cr-Zr, Cu-Ni-Si, Cu-Ag, Cu-Mg is reviewed according to different alloy systems. Finally, the development trend of HSHC Cu alloys is forecasted. It is pointed out that precipitation and micro-alloying are effective ways to improve the performance of HSHC Cu alloys. At the same time, the production of HSHC Cu alloys also needs to comply with the large-scale, low-cost development trend of industrialization in the future.

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Microstructure and Precipitate's Characterization of the Cu-Ni-Si-P Alloy

Cited by 39 publications

References 24 publications

A property-oriented design strategy for high performance copper alloys via machine learning

A property-oriented design strategy for high performance copper alloys via machine learning

Precipitation in a copper matrix modeled byab initiocalculations and atomistic kinetic Monte Carlo simulations

High strength and high conductivity Cu alloys: A review

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