Crossover from Mechanical Stabilization of Martensite to Spanning Avalanche-Type Reverse Transformation of Deformed Cu-Al-Ni Shape Memory Crystals
Journal of Alloys and Compounds(2023)
Univ Illes Balears | Ioffe Inst | Shandong Univ | Univ Leuven KU Leuven
Abstract
A new interpretation of factors controlling the kinetics of reverse transformation of martensites stabilized by prestrain is suggested. We investigate the effect of amount of prestrain in the β1´martensite and in the β-phase on temperature and kinetics of the reverse martensitic transformation in Cu-Al-Ni shape memory alloy single crystals. Calorimetry and video recording of the jumping samples, provoked by the burst strain recovery during temperature-induced reverse transformation, are used in experiments. For crystals deformed both in the β´1 martensite and in the β-phase, we report the existence of a sharp transition (crossover) from the mechanical stabilization of martensite to the burst reverse martensitic transformation which proceeds as a single avalanche. The crossover occurs for prestrain values close to the maximum transformation strain. The specific energy of the jump during burst strain recovery depends on the maximum stress applied during prestraining the sample. The crossover from a broad transformation range to an infinite or spanning avalanche is predicted by 3D random field Ising model for disordered systems undergoing the first order phase transition. Based on this model, we explain the crossover in deformed Cu-Al-Ni crystals by a rapid decrease of structural disorder towards the critical one at the final stage of the formation of detwinned γ´1martensite. A simple solution is obtained for the critical transformation rate which produces jumping of the sample during burst reverse transformation after prestrain.
MoreTranslated text
Key words
Martensite transformation,Deformation,Avalanche,Kinetics,Ising model
求助PDF
上传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
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
Journal of Materials Research and Technology 2024
被引用1
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