Mechanism investigation on high-performance Cu-Cr-Ti alloy via integrated computational materials engineering

Huang, Zhaokuo; Shi, Renhai; Xiao, Xingyu; Fu, Huadong; Chen, Qing; Xie, Jun

doi:10.1016/j.mtcomm.2021.102378

Cited by 6 publications

(7 citation statements)

References 38 publications

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“…The formula for calculating the strength of copper alloys σ b is [ 36 ]:

where σ gs is strengthening by grain size reduction, σ ss is solid-solution strengthening, σ os is Orowan enhancement, and σ dis is dislocation strengthening. The SLM process has a high cooling rate, and as-built samples are supersaturated solid solutions, so the solid-solution strengthening and dislocation strengthening effects of samples manufactured using different process parameters are basically the same.…”

Section: Resultsmentioning

confidence: 99%

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Effect of Process Parameters on the Microstructure and Properties of Cu–Cr–Nb–Ti Alloy Manufactured by Selective Laser Melting

Liu

Zhou

et al. 2023

Materials

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The fabrication of high-performance copper alloys by selective laser melting (SLM) is challenging, and establishing relationships between the process parameters and microstructures is necessary. In this study, Cu–Cr–Nb–Ti alloy is manufactured by SLM, and the microstructures of the alloy are investigated by X-ray diffraction (XRD), scanning electron microscope (SEM), and electron backscatter diffraction (EBSD). The effects of processing parameters such as laser power and scanning speed on the relative density, defects, microstructures, mechanical properties, and electrical conductivity of the Cu–Cr–Nb–Ti alloy are studied. The optimal processing window for fabricating Cu–Cr–Nb–Ti alloy by SLM is determined. Face-centered cubic (FCC) Cu diffraction peaks shifting to small angles are observed, and there are no diffraction peaks related to the second phase. The grains of XY planes have a bimodal distribution with an average grain size of 24–55 μm. Fine second phases with sizes of less than 50 nm are obtained. The microhardness, tensile strength, and elongation of the Cu–Cr–Nb–Ti alloy manufactured using the optimum processing parameters, laser power of 325 W and scanning speed of 800 mm/s, are 139 HV0.2, 416 MPa, and 27.8%, respectively, and the electrical conductivity is 15.6% IACS (International Annealed Copper Standard). This study provides a feasible scheme for preparing copper alloys with excellent performance and complex geometries.

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“…The formula for calculating the strength of copper alloys σ b is [ 36 ]:

Section: Resultsmentioning

confidence: 99%

“…Studies have shown that Ti can effectively improve the hardness and strength of Cu–Cr [ 36 ] and Cr–Nb [ 37 ] alloys. In this study, a new high-strength and high-conductivity Cu–Cr–Nb–Ti alloy based on Cu–Cr–Nb alloys was developed using the SLM process.…”

Section: Introductionmentioning

confidence: 99%

Effect of Process Parameters on the Microstructure and Properties of Cu–Cr–Nb–Ti Alloy Manufactured by Selective Laser Melting

Liu

Zhou

et al. 2023

Materials

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“…The applicable alloy features machine learning method has been successfully used to design new alloy. [21][22][23][24][25] In this study, the alloy features-based machine learning process was conducted to develop a model for predicting the low cycle fatigue life of AISI 304, AISI 310, AISI 316, and AISI 316FR with similar series designation chemical compositions at various elevated temperatures. Additionally, eight algorithms were employed to examine the impact of the algorithms in the accuracy of constructed models.…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning method has been extensively employed in different field over the past 5 years 18 . It has been successfully used as an extremely powerful tool to optimize the chemical compositions of materials to achieve desired properties, 19,20 for instance, developing high strength and high ductility Al alloy 21 and high strength with high conductivity Cu alloy 22–25 . Additionally, in mechanical science, machine learning method is used to predict fatigue life under uniaxial or multiaxial loading, 26–31 fatigue limit, 32,33 and short fatigue crack propagation behavior 34,35 .…”

Section: Introductionmentioning

confidence: 99%

Fatigue life evaluation model for various austenitic stainless steels at elevated temperatures via alloy features‐based machine learning approach

Wei

et al. 2022

Fatigue Fract Eng Mat Struct

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An alloy features-based and chemical compositions-based machine learning method was used to examine the low cycle fatigue life of austenitic stainless steels at different elevated temperatures employing one model. Furthermore, eight algorithms were used to examine the impact of algorithms on the precision of constructed models. As input, physicochemical features of elements were transformed from chemical compositions. After being conducted by the feature screening process, electronegativity deviation (E2.sd), ionization energy deviation (E6.sd), testing conditions, and tensile strength were chosen as input.The results show that algorithms affect accuracy and the models with the highest accuracy are SVR and ANN for alloy features and chemical compositionsbased method, respectively. Chemical composites-based model demonstrates relatively lower precision than the alloy feature model. Almost all testing data distribute within two-factor band lines predicted by alloying feature-based model. The validation testing results indicate that 83% data plots distribute within two-factor band lines.

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“…In addition to numerical methods, the continuous development of artificial intelligence technology in recent years has generated considerable interest in applying machine learning methods for conducting data processing in materials research, [21][22][23][24][25][26][27][28][29][30] the prediction of material performance, [31] and the development of graphical diagnosis systems. [32] Moreover, machine learning has been applied in the analysis of electrochemical corrosion processes for various materials.…”

mentioning

confidence: 99%

Machine learning‐based analysis and prediction of the interfacial corrosion processes of copper cathode plates during the electrolytic production of copper powders

Zhou

Lin

et al. 2022

Materials & Corrosion

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Present efforts to support the essential industrial‐scale electrolytic production of copper‐based metal powders urgently require approaches to the real‐time predicting of corrosion of copper cathodes employed in electrolytic production processes. However, current approaches are extremely limited owing to the difficulty of accurately modeling the complex cathode corrosion process. In this study, the corrosion process under different parameters was analyzed by a self‐designed continuous electrolytic corrosion experimental device, clarify the influence mechanism of current density on the corrosion of the solid–liquid–gas interface area, and addresses this issue by applying a random forest machine learning approach based on three process parameters, including the electrolyte temperature, liquid‐level fluctuation cycle period, and current density. The dataset employed in the model is obtained using a novel experimental corrosion test method based on electrode arrays. The experimental results include the corrosion rates of copper cathode plates at different positions relative to the liquid electrolyte level during the electrolysis process. The resulting stochastic model is demonstrated to obtain a high prediction accuracy of 97% for the various regions of copper cathode plates defined according to liquid electrolyte level.

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Mechanism investigation on high-performance Cu-Cr-Ti alloy via integrated computational materials engineering

Cited by 6 publications

References 38 publications

Effect of Process Parameters on the Microstructure and Properties of Cu–Cr–Nb–Ti Alloy Manufactured by Selective Laser Melting

Effect of Process Parameters on the Microstructure and Properties of Cu–Cr–Nb–Ti Alloy Manufactured by Selective Laser Melting

Fatigue life evaluation model for various austenitic stainless steels at elevated temperatures via alloy features‐based machine learning approach

Machine learning‐based analysis and prediction of the interfacial corrosion processes of copper cathode plates during the electrolytic production of copper powders

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