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RESEARCH PAPER · International Journal of Minerals Metallurgy and Materials · 2022

Rapid design of secondary deformation-aging parameters for ultra-low Co content Cu-Ni-Co-Si-X alloy via Bayesian optimization machine learning

Hongtao Zhang, Huadong Fu, Yuheng Shen, Jianxin Xie

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42 direct citing works

Rapid design of secondary deformation-aging parameters for ultra-low Co content Cu-Ni-Co-Si-X alloy via Bayesian optimization machine learningOriginalRecent progress in the machine learning-assisted rational design of alloys69Microstructure and properties evolution of in-situ fiber-reinforced Ag−Cu−Ni−Ce alloy during deformation and heat treatment14Digital Model for Rapid Prediction of Die Forging Force and Forming Control of Aluminum Alloy Aviation Components0Work Hardening Behavior and Microstructure Evolution of a Cu-Ti-Cr-Mg Alloy during Room Temperature and Cryogenic Rolling23Improved data-driven performance of Charpy impact toughness via literature-assisted production data in pipeline steel16Scientific Discovery Framework Accelerating Advanced Polymeric Materials Design15Improved mechanical strength, ductility, and electrical conductivity of Cu–Ni–Si alloys after multi-pass continuous extrusion and aging processes20Precipitation behavior of a new Cu-Ni-Co-Si-Fe-Mg alloy designed with low cobalt content51Extraction and recycling technologies of cobalt from primary and secondary resources: A comprehensive review28A review on copper alloys with high strength and high electrical conductivity230Experimentally validated inverse design of multi-property Fe-Co-Ni alloys30Microstructure and mechanical properties of a Cu-Ni-Sn-Si-Al alloy produced by equal channel angular pressing5Regulation of dislocation density and precipitates to maximize the mechanical strength and electrical conductivity in continuously extruded Cu–Ni–Si alloys24Accelerating high-entropy alloy discovery: efficient exploration via active learning36Inversely optimized design of Al-Mg-Si alloys using machine learning methods26Stress-assisted corrosion mechanism of 3Ni steel by using gradient boosting decision tree machining learning method23The comparsion of different fabrication processes on the microstructure and mechanical properties of Cu-11Al-3Ni alloy2Mapping high entropy state spaces for novel material discovery5Improving mechanical and electrical properties of Cu-Ni-Si alloy via machine learning assisted optimization of two-stage aging processing40Suppression of discontinuous precipitation by Fe addition in Cu–Ti alloys26Machine Learning-Aided Examination of Energy Absorption and Mechanical Properties in Steel Lattice Structures2An intelligent screening for rare earth in Ni alloy based on machine learning and multi-objective optimisation0Rapid design of high-end copper alloy processes combining orthogonal experiments, machine learning, and Pareto analysis21Optimizing the overall performance of Cu-Ni-Si alloy via controlling nanometer-lamellar discontinuous precipitation structure20Interpretable Machine Learning Applications: A Promising Prospect of AI for Materials68A machine learning strategy to achieve dual-synchronous property improvement of aviation Al-Cu-Mg alloy30Composition optimization of Mo-based alloys with both high temperature strength and toughness based on machine learning0Evolution of the microstructure and mechanical properties of Cu strips manufactured by a novel single-step drawing and progressive rolling process4Enhanced prediction of occurrence forms of heavy metals in tailings: A systematic comparison of machine learning methods and model integration17Digital model for rapid prediction and autonomous control of die forging force for aluminum alloy aviation components8Data-driven multi-process modeling and integrated design strategy for complex high-performance alloys12The mechanisms for evolution and strengthening of precipitates in the Ni-added Cu-1.95wt%Be alloy with high strengths1Accelerated Cu-Ti alloy design under data scarcity through fine-tuned transfer learning4Pre-aging induced concurrent enhancement of mechanical properties and softening resistance performance of Cu–Ni–Co–Si alloys6Machine learning-enabled multi-objective optimization design of process parameters for a medium-entropy alloy0Data-driven discovery of copper alloys: resolving the strength-conductivity paradox1A machine learning strategy for rapid design of preparation parameters in zero-sample complex alloy1Microstructure and properties of Cu−Ni−Co−Si alloy designed by cluster formula approach3Tailoring strength–ductility synergy in Cu–Ti–Fe alloys via Ni micro-alloying: A combined experimental and crystal plasticity study0Explainable MRF-BBAPM with self-learning for predicting compressive strength of oxidized pellet0Hierarchical defect architectures enabled by Mg addition and cryogenic rolling for ultrahigh-strength Cu-Ni-Co-Si-Mg alloys0Design of a high-strength and high-temperature stress relaxation resistant copper alloy based on machine learning0Multiscale defect structure design and strength-ductility synergistic optimization of 6082 aluminum alloy based on machine learning and non-equilibrium processing0Machine learning for high-performance alloy design: A review0

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  2. Microstructure and properties evolution of in-situ fiber-reinforced Ag−Cu−Ni−Ce alloy during deformation and heat treatment

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  3. Digital Model for Rapid Prediction of Die Forging Force and Forming Control of Aluminum Alloy Aviation Components

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  4. Work Hardening Behavior and Microstructure Evolution of a Cu-Ti-Cr-Mg Alloy during Room Temperature and Cryogenic Rolling

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  5. Improved data-driven performance of Charpy impact toughness via literature-assisted production data in pipeline steel

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  6. Scientific Discovery Framework Accelerating Advanced Polymeric Materials Design

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  15. Inversely optimized design of Al-Mg-Si alloys using machine learning methods

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  16. Stress-assisted corrosion mechanism of 3Ni steel by using gradient boosting decision tree machining learning method

    International Journal of Minerals Metallurgy and Materials · 2024-05-27

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  17. The comparsion of different fabrication processes on the microstructure and mechanical properties of Cu-11Al-3Ni alloy

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  18. Mapping high entropy state spaces for novel material discovery

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  19. Improving mechanical and electrical properties of Cu-Ni-Si alloy via machine learning assisted optimization of two-stage aging processing

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  20. Suppression of discontinuous precipitation by Fe addition in Cu–Ti alloys

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  21. Machine Learning-Aided Examination of Energy Absorption and Mechanical Properties in Steel Lattice Structures

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