Effect of Microalloying with Rare-Earth Lanthanum on Dynamic Recrystallization Behavior and Mechanical Properties of Ti Sheets

Yuanyuan Zhu,Yechen Deng,Yixin An,Kaige Wang,Ruoyu Liu,Lanyi Liu, Ziqi Wang,Yangzhihong Xiao,Bingfeng Wang

Materials today. Communications（2024）

Cited 0|Views15

Abstract

This study examines the influence of microalloying with rare earth lanthanum (La) on the dynamic recrystallization behavior and mechanical properties of Titanium (Ti) sheets. A distinctive wedge-shaped specimen was employed for experimental efficiency, enabling continuous gradient strains to be applied to the Ti-La alloy during hot-rolling, facilitating the assessment of changing microstructural, textural, and hardness features as strain increased from low to high levels. The hot-rolled plates were performed using analytical methods such as optical microscopy (OM), X-ray diffraction (XRD), transmission electron microscopy (TEM), and hardness testing. The results show that the hardening process can be divided into four stages under continuous gradient strain (0 ~ 0.5, 0.5 ~ 0.7, 0.7 ~ 1.2 and 1.2 ~ 1.6). During these stages, grain refinement and dislocation strengthening emerge as critical contributors to changes in hardness. Furthermore, it was determined that dynamic recrystallisation (DRX) of Ti-La alloys during hot rolling occurs at the fourth stage. Analysis of the orientation distribution function (ODF) reveals that the dominant textures during this process include {1̅21̅0}, {011̅0}, and {0001} textures. When compared with commercially pure titanium (CP-Ti), the Ti-La alloy shows significantly delayed DRX under identical conditions. The DRX grain growth appears suppressed, attributable to the presence of La-rich particles on the grain boundaries. Moreover, the texture associated with rolling deformation seems subdued, providing a desirable base for future cold rolling and heat treatment.

Translated text

Key words

Titanium alloy,Lanthanum addition,Hot rolling,Microstructure and texture,Mechanical properties

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