WeChat Mini Program
Old Version Features

    • The Effect of Γ' and Δ Phases Evolution on the Mechanical Properties of High-Entropy Cocu0.5fenita0.1 Alloy During Heat Treatment

    Zhengyuan Yuan,Min Zhang,Zhunli Tan,Wenbo Yu

    Journal of Materials Research and Technology（2024）

    Beijing Jiaotong Univ

    Cited 0|Views14
    Abstract
    To simultaneously improve the mechanical strength and plasticity of the CoCu0.5FeNiTa0.1 high entropy alloy, we adopted the heat treatment and investigated the evolution of different phases containing gamma ', delta and gamma phases. Two different gamma ' phases, the Cu-rich phase and the Ta-rich phase, were detected in the as-cast alloy. During heat treatment, Cu outwards diffused from gamma '(Cu-rich) into gamma matrix, gamma '(Ta-rich) phase and delta phase. The accumulation of Cu and Ta into the delta phase strongly promoted its growth. One significant increase was measured in the compressive yield strength from 1040 MPa to 1370 MPa, ultimate compressive strength from 2020 MPa to 2735 MPa, compressive strain from 34% to 50.2% and hardness from 3.854 GPa to 4.315 GPa. The substantial enhancement in ductility mainly resulted from the distance increase among gamma ' particles in gamma matrix, the reduction of lattice distortion and the decrease in aspect ratio of delta phases. The yield strengthening mechanisms were mainly from solid strengthening and Orowan strengthening.
    More
    Translated text
    Key words
    High entropy alloys,Mechanical properties,Microstructure,Heat treatment
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined