Microstructure and Fracture Behavior of Multi-Elements Strengthened CoCrNi Alloy Produced by Laser-Directed Energy Deposition

Lijia Chen,Sihao Zou,Hao Zhang,Shang Sui, Lichao Cao, Shangxiong Huangfu,Dake Zhao, Jie Chen,Wenjun Lu,Guijun Bi

JOURNAL OF ALLOYS AND COMPOUNDS（2025）

Cited 0|Views0

Abstract

Addition of single non-constituent element to strengthen equiatomic CoCrNi alloy has proven effective but faces limitations due to the solubility constraints of these additives. Excessive addition of single element causes the precipitation of brittle phases and deteriorates the mechanical properties of CoCrNi alloy. This study investigates the phase composition and mechanical properties of CoCrNi alloy strengthened by more than two elements simultaneously. Using laser-directed energy deposition (LDED), a series of CoCrNi(Al0.6TiFe)x alloys were rapidly fabricated by feeding both CoCrNi and CoCrNiAl0.6TiFe powders simultaneously. Microstructural analysis revealed cellular structures in all alloys, with coarsening observed at higher additive contents. Besides, increasing Al, Ti, Fe content in the alloy led to the precipitation of a BCC phase at cellular boundaries, causing stress concentration and resulting in a transition from ductile fracture to cleavage fracture. Among these alloys, one deposited with 70 % CoCrNi and 30 % CoCrNiAl0.6TiFe powders, exhibited remarkable mechanical properties, with a yield strength of 689.3 MPa, ultimate tensile strength of 1004.3 MPa, and elongation of 30.2%, representing a 41.8 % increase in yield strength compared to the CoCrNi alloy. The improved properties are attributed to a high initial dislocation density of 2.50 x 1015 m-2 caused by the high solubility of Al, Ti, and Fe elements about 14.82 at%. These findings highlight a novel strategy for developing high-performance alloys by incorporating multiple non-constituent elements via LDED.

Translated text

Key words

Laser-directed energy deposition,Multi-principal element alloy,Multi-elements strengthening,Fracture mechanisms,Microstructures

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