WeChat Mini Program
Old Version Features

Effect of In-Situ Layer-by-layer Rolling on the Microstructure, Mechanical Properties, and Corrosion Resistance of a Directed Energy Deposited 316L Stainless Steel

Additive manufacturing(2022)

Monash Ctr Addit Mfg

Cited 34|Views24
Abstract
In this study a novel micro-rolling set-up was installed on a directed energy deposition additive manufacturing system to achieve in-situ grain refinement and porosity closure. A 316 L stainless steel was investigated and it was found that even low micro-rolling loads were capable of increasing the part density from 99.7% to 99.98%. Martensitic transformations as a result of micro-rolling were not detected. Additionally, using a combination of X-ray diffraction and optical, scanning, and transmission electron microscopy techniques, it was found that micro-rolling resulted in a finer internal grain substructure, twinning, an increase in dislocation density, a reduction of grain sizes, a refinement of the cellular structures, and a randomization of the crystallographic orientation distribution. This resulted in an increase in hardness by up to - 23% and in yield strength by up to 31%. More importantly, due to pore closure, improved mechanical properties were achieved without compromising ductility. As the segregation of solute elements to cell boundaries was also not affected by micro-rolling, the excellent corrosion resistance of the alloy was generally retained. Thus, this approach allows the fabrication of denser, stronger and harder 316 L parts without the need for additional post-processing steps such as hot isostatic pressing or thermo-mechanical treatments, while also maintaining ductility and corrosion performance.
More
Translated text
Key words
316L stainless steel,Rolling,Porosity closure,Direct energy deposition,Hybrid manufacturing
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