WeChat Mini Program
Old Version Features

Sample Size Effect on Creep in Bending: an Interplay Between Strain Gradient and Surface Proximity Effects

SSRN Electronic Journal(2022)

Indian Institute of Science

Cited 1|Views16
Abstract
Miniature cantilevers are associated with amplification in strain gradient and dislocation recovery. These contrasting phenomena interplay to impart strengthening via the injection of geometrically necessary dislocations because of strain gradients and softening due to the surface proximity effect. In this work, creep studies on Al cantilevers of thicknesses ranging from 0.070 to 7 mm demonstrate this interplay. The 2D strain field and dislocation substructure were examined to understand the steady-state creep response in miniaturized cantilevers. The average creep response of the cantilever strengthens with decreasing cantilever thickness; however, regimes of hardening, softening, and bulk-like behavior were observed while traveling along the length of the cantilever. Consistently, refinement in the steady-state dislocation substructure was observed in the regions of large strain gradients. The locations in a miniaturized cantilever with insignificant strain gradients registered surface proximity effect-induced enhanced creep rates. An analytical model has been developed to describe the strain gradient-surface proximity interplay.
More
Translated text
Key words
High throughput testing,Strain gradient effect during creep,Sample size effect,Surface proximity effect (creep softening),High-temperature DIC
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
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
Summary is being generated by the instructions you defined