集成计算加速了新型标记钢的设计和性能控制
Shixing Chen1,2, Jingchuan Zhu1, Tingyao Liu2
1School of Material Science and Engineering, Harbin Institute of Technology, Harbin 150001, China.
Materials (Basel, Switzerland)
|June 28, 2023
概括
机器学习加速了新型马氏体老化钢的设计. 这种方法实现了高预测准确度,并确定了超高抗拉强度和优良可塑性的最佳组合.
科学领域:
- 材料科学 材料科学 材料科学
- 计算材料工程 计算材料工程
- 金工业是金工业的一个方面.
背景情况:
- 马氏体老化钢对于高性能应用至关重要.
- 了解合金元素相互作用和沉强化是优化钢材性能的关键.
- 综合计算材料工程 (ICME) 为材料设计提供了强大的工具.
研究的目的:
- 用先进的计算和实验方法加速新型马氏体老化钢的设计和开发.
- 研究合金元素和沉阶段对钢材性能的影响.
- 为了优化构成和加工参数,以获得卓越的机械性能.
主要方法:
- 利用数据库技术,机器学习 (ML),热力学计算和实验验证.
- 采用ML进行建模和参数优化,达到高达98.58%的预测准确度.
- 进行了相关性测试,以分析多视角元素影响和用于相位分析的热力学计算.
主要成果:
- 通过真空弧炼开发并验证了一种新的马氏体老化钢材等级.
- 实现了卓越的机械性能,包括1887 MPa的屈服强度,1907 MPa的抗拉强度,58 HRC的硬度和7.8%的延长.
- 确定了最佳的三元组合过程参数,在组合和性能方面具有很高的对比度.
结论:
- 用ML驱动的方法加速设计超高拉伸力钢的方法是可以通用和可靠的.
- 基于相位图的热力学计算提供了基于相位图的热处理信息,从而成功开发了钢材等级.
- 这项研究表明了设计先进的高强度钢的强大方法.
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