Effects of Fe on Microstructure and Mechanical Properties of CoCrNiFeAl Multi-Principle Elements Alloys

Huijie Wei,Ping Huang,Shengwu Guo,Xueling Fan,Fei Wang

Journal of Materials Research and Technology（2024）

State Key Laboratory for Strength and Vibration of Mechanical Structures | Xi An Jiao Tong Univ

Cited 1|Views32

Abstract

Precipitation and twinning strengthening mechanisms have been extensively investigated to improve the yield strength and ductility of face-centered cubic (FCC) multi-principle elements alloys (MPEAs). Compared with well-studied Al0.3CoCrFeNi in CoCrFeNi system, the microstructure and mechanical properties of a new designed CoCrNiFe0.62Al0.38 MPEA with lower Fe content were evaluated by transmission electron microscopy and tensile tests, respectively. Higher volume fraction of B2 nanoprecipitates and early deformation twining within FCC matrix were proposed to play key roles in improving the synergy strength and ductility of the AlCoCrFeNi system. The experimental results presented herein not only provide insight into how transition metal element affect precipitation and twining process of B2 phase reinforced FCC MPEAs, but also demonstrate useful guidance for the development of precipitation hardened CoCrFeNi system other than adjusting conventional elements such as Al, Ti, Ta, etc.

Translated text

Key words

Multi-principle elements alloys,Precipitation,Stacking fault energy,Deformation twinning,Microstructure

求助PDF

上传PDF

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