基于机器学习的重截面柔性铁断裂性的预测
Liang Song1, Hongcheng Zhang2, Junxing Zhang2
1School of Intelligent Manufacturing and Automotive Engineering, Luzhou Vocational & Technical College, Luzhou, 646000, China. songliang@lzy.edu.cn.
Scientific reports
|February 27, 2024
概括
研究人员开发了机器学习模型,以预测重型性铁的断裂性. 一个袋装模型实现了高精度,为材料设计提供了具有成本效益的解决方案.
科学领域:
- 材料科学与工程 材料科学与工程
- 金工业是一种金工业.
- 计算材料科学科学 计算材料科学
背景情况:
- 沉重截面软钢的断裂性至关重要,但由于复杂的组成和冷却速率相互作用,难以预测.
- 评估断裂性的传统方法往往耗时且昂贵,特别是在大型件中.
研究的目的:
- 研究碳 (C), (Si) 和 (Mn) 含量以及冷却速度对重型软钢的断裂性的影响.
- 开发准确和高效的机器学习模型来预测这种材料的断裂性.
主要方法:
- 准备18个立方体物理模拟试验块 (400毫米壁厚) 的C,Si和Mn含量不同.
- 分析了每块四个冷却速度位置的72个标本.
- 使用C含量,Si含量,Mn含量和冷却速率作为输入来预测断裂性的六个机器学习模型的构建.
主要成果:
- 一个袋装机器学习模型在预测断裂性方面表现出极高的准确性.
- 包装模型实现了0.9990的确定系数 (R2) 和0.2373.3的根平均平方误差 (RMSE).
- 这表明它具有高度可靠的预测能力,用于重截面柔性铁的断裂性.
结论:
- 机器学习,特别是包装模型,提供了一个非常准确和高效的方法来预测重型软钢的断裂性.
- 开发的模型可以帮助优化组合设计和处理参数,减少开发时间和成本.
- 这种方法为重型柔性铁组件的材料设计和质量控制提供了有价值的工具.
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