通过晶体可塑性模拟和机器学习来预测应力-应变曲线
Dmitry S Bulgarevich1, Makoto Watanabe2
1National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki, 305-0047, Japan. bulgarevich.dmitry@nims.go.jp.
Scientific reports
|November 28, 2024
概括
预测激光粉床聚变 (LPBF) 金属的应力-应变曲线是复杂的. 决策树机器学习模型使用实验条件有效预测温度依赖曲线,优于Hastelloy X的其他方法.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算材料科学科学 计算材料科学
背景情况:
- 通过激光粉床聚变 (LPBF) 预测增材制造的金属的应力-应变曲线 (SSC) 是计算密集的.
- 传统方法涉及复杂的微观结构重建和晶体可塑性模拟,这些都耗时.
- 机器学习 (ML) 为加速SSC预测提供了一个潜在的解决方案.
研究的目的:
- 开发和评估各种ML方法来预测LPBF制造的Hastelloy X.的温度依赖的SSC.
- 建立实验条件和预测的SSC之间的直接联系,绕过微观结构重建.
- 为了确定这个特定应用程序的最有效的ML方法.
主要方法:
- 利用几种机器学习 (ML) 方法来预测SSCs.
- 专注于直接将实验条件与SSC联系起来,不包括微观结构数据.
- 训练并比较不同的ML模型,包括基于决策树的回归器和人工神经网络 (ANN).
主要成果:
- 与其他流行的ML方法相比,基于决策树的ML回归者表现出优异的性能.
- 开发的模型成功地预测了Hastelloy X.的SSC的温度依赖性.
- 将实验条件直接链接到SSC的方法被证明是有效的,特别是在较小的数据集.
结论:
- 基于决策树的ML模型对于预测LPBF Hastelloy X的温度依赖SSC非常有效,特别是在使用实验条件作为输入时.
- 这种ML方法为传统模拟方法提供了一个计算效率高的替代方案.
- 该研究强调了ML在加速材料表征和增材制造合金设计方面的潜力.
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