Coupled Model for Particle Dissolution and Coarsening in Microalloyed Steels
Materials Science and Technology(2007)SCI 3区SCI 4区
Baosteel Res Inst
Abstract
The present paper presents an integrated model for predicting particle dissolution and coarsening upon heat treatment of microalloyed steels. This model considers the effect of critical particle size on the particle coarsening kinetics. It has been shown that the solute supply rejected from the dissolved particles smaller than the critical particle size is insufficient for the particle coarsening. It is, therefore, suggested that the particles assimilate solute atoms from the supersaturated matrix directly through lattice diffusion. The particle sizes predicted by the proposed model are in agreement with the experimental results in the literature.
MoreTranslated text
Key words
model,particle dissolution,particle coarsening,microalloyed steels
PDF
View via Publisher
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
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
ACTA METALLURGICA SINICA 2010
被引用57
Materials Science and Technology 2011
被引用53
Microstructural Evolution of Inverse Bainite in a Hypereutectoid Low-Alloy Steel
Metallurgical and materials transactions A, Physical metallurgy and materials science 2017
被引用4
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
GPU is busy, summary generation fails
Rerequest