...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Arabian Journal for Science and Engineering >Engineering of Novel Fe‑Based Bulk Metallic Glasses Using a Machine Learning‑Based Approach
【24h】

Engineering of Novel Fe‑Based Bulk Metallic Glasses Using a Machine Learning‑Based Approach

机译：基于机器学习方法的新型Fe系散装金属眼镜的工程

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

A broad range of potential chemical compositions makes difficult design of novel bulk metallic glasses (BMGs) withoutperforming expensive experimentations. To overcome this problem, it is very important to establish predictive modelsbased on artificial intelligence. In this work, a machine learning (ML) approach was proposed for predicting glass formationin numerous alloying compositions and designing novel glassy alloys. The results showed that our ML model accuratelypredicted the glass formation and critical thickness of MGs. As a case study, the ternary Fe–B–Co system was selected andeffects of minor additions of Cr, Nb and Y with different atomic percentages were evaluated. It was found that the minoraddition of Nb and Y leads to the significant improvement of glass-forming ability (GFA) in the Fe–B–Co system; however,a shift in the optimized alloying composition was occurred. The experimental results on selective alloying compositionsalso confirmed the capability of our ML model for designing novel Fe-based BMGs.
机译：广泛的潜在化学成分使得新型散装金属玻璃（BMGS）难以设计执行昂贵的实验。为了克服这个问题，建立预测模型非常重要基于人工智能。在这项工作中，提出了一种用于预测玻璃形成的机器学习（ML）方法在许多合金化组合物中和设计新型玻璃合金。结果表明我们的ML模型准确预测MGS的玻璃形成和临界厚度。作为一个案例研究，选择了三元Fe-B-Co系统评价次次添加Cr，Nb和Y以不同原子百分比的影响。发现未成年人添加Nb和Y导致Fe-B-Co系统中的玻璃形成能力（GFA）的显着提高;然而，发生优化合金化组合物的换档。对选择性合金组合物的实验结果还确认了我们ML模型设计新型Fe基BMG的能力。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号