纯铁的滚动和再结晶纹理的机器学习辅助分析,具有不同的冷减比和冷方向
Takumi Sumida1, Keiya Sugiura1, Toshio Ogawa2
1Department of Materials Design Innovation Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan.
Materials (Basel, Switzerland)
|July 27, 2024
概括
机器学习模型准确地预测在和炼过程中纯铁的质感. 热温度是Goss谷物核化的关键,冷方向的影响最小.
科学领域:
- 材料科学 材料科学 材料科学
- 金工业是金工业的一个方面.
- 计算材料科学科学 计算材料科学
背景情况:
- 了解纯铁的纹理演变对于预测材料性质至关重要.
- 冷和冷是影响微观结构和纹理的关键过程.
- 机器学习为分析复杂物质现象提供了新的方法.
研究的目的:
- 为了研究冷减速率和滚动方向对纯铁纹理的影响.
- 使用机器学习开发用于滚动和再结晶纹理的准确预测模型.
- 为了确定控制化过程中Goss粒核化的主导因素.
主要方法:
- 准备五种样本类型,具有不同的冷减比和滚动方向.
- 滚动和再结晶纹理的实验分析.
- 机器学习模型 (人工神经网络,XGBoost) 的开发,使用处理条件作为输入,Goss方向作为输出.
主要成果:
- 双向冷显示了最小的纹理效果,除了60%的减少.
- 冷缩减比和滚动方向在冷和短期回火过程中对纹理产生了轻微的影响.
- 长期回火导致异常的谷物生长,具有不同的晶体方向.
- 机器学习模型在预测戈斯定向发展方面取得了很高的准确性.
- 热温度被确定为Goss谷物核化的主要因素.
结论:
- 冷参数对质地演变的影响有限,与热温度相比.
- 机器学习为预测和理解金属质感发展提供了强大的工具.
- 优化热温度对于控制纯铁中Goss粒形成至关重要.
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