WeChat Mini Program
Old Version Features

微量润滑技术在切削加工中的应用

Mechanical & Electrical Engineering Technology(2017)

Cited 7|Views3
Abstract
微量润滑(MQL)技术作为一种绿色可持续加工技术,在汽车制造、航空航天、模具制造等方面越来越受到人们的关注.介绍了传统MQL以及新型MQL(外冷复合喷雾(EOoW)、内冷复合喷雾(IOoW)和低温冷风复合喷雾(CAOoW)等)的相关原理及常见设备;MQL应用技术的关键性问题:喷雾场、油雾粒度、润滑油种类以及喷嘴位置研究情况;典型难加工材料:钛合金、复合材料、不锈钢以及铸铁MQL技术切削加工应用研究情况;MQL技术未来的研究目标.
More
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