在化增强合金中研究热变形行为,使用构成和机器学习模型
Ayoub Elajjani1,2, Yinghao Feng1,2, Wangxi Ni1,2
1School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Nanomaterials (Basel, Switzerland)
|February 13, 2025
概括
一个支持向量的回归模型准确地预测了在热压缩过程中化增强的AZ80复合材料中的流应力. 这种数据驱动的方法优化了轻质材料的制造工艺.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算机建模 计算建模
背景情况:
- 准确的流量应力预测对于优化轻质材料的制造至关重要,特别是在高温条件下.
- 化 (BN) 增强的AZ80复合材料是先进的轻质材料,需要精确的加工参数.
- 热压缩是一种关键的制造工艺,了解压力和温度下的材料行为至关重要.
研究的目的:
- 开发一个基于数据的模型,用于预测热压缩过程中BN增强的AZ80复合物的流应力.
- 用实验数据评估支持向量回归 (SVR) 模型的预测精度.
- 确定影响这种复合材料流应力的关键参数.
主要方法:
- 在热压缩测试中,用BN增强的AZ80复合物在300-400°C的温度下进行,应变速率从0.01到10s-1进行.
- 开发了一个支向量回归 (SVR) 模型,以利用温度,延展率和延展作为输入变量来预测流应力.
- 模型性能被评估使用指标,如平均平方误差 (MSE),确定系数 (R2) 和平均绝对相对误差 (AARE).
主要成果:
- 开发的SVR模型实现了流应力的高预测准确度,由低MSE,接近统一的R2和最小的AARE表示.
- 灵敏度分析显示,应变速率和温度是影响复合材料流应力最重要的因素.
- 该模型有效地捕捉了加工参数和材料的流应力行为之间的复杂关系.
结论:
- 数据驱动的SVR模型提供了一种可靠和准确的方法,用于预测BN增强的AZ80复合材料中的流应力.
- 将机器学习与实验数据集成,可以有效优化热变形过程.
- 该框架支持数据驱动的决策,以提高轻质合金的制造效率和工艺控制.
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