增材制造中的高循环疲劳寿命预测Inconel 718合金通过机器学习
Zongxian Song1, Jinling Peng1, Lina Zhu2
1School of Aeronautics and Astronautics, Tianjin Sino-German University of Applied Sciences, Tianjin 300350, China.
Materials (Basel, Switzerland)
|June 13, 2025
概括
生成对抗网络 (GAN) 在3D打印的Inconel 718中显著改善了对非常高周期疲劳 (VHCF) 寿命的机器学习预测. 对于具有关键缺陷的材料,GAN在提高疲劳寿命预测方面优于变化自动编码器.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 数据科学数据科学数据科学
背景情况:
- 预测增材制造 (AM) 材料的疲劳寿命,如Inconel 718,由于工艺引起的缺陷,具有挑战性.
- 非常高周期疲劳 (VHCF) 行为对微观结构特征和缺陷参数特别敏感.
研究的目的:
- 建立一个机器学习框架,在选择性激光化的Inconel 718中准确预测VHCF寿命.
- 为了比较生成对抗网络 (GAN) 和变异自动编码器 (VAE) 在疲劳寿命预测模型中数据增强的有效性.
- 量化缺陷参数对疲劳寿命的影响,并评估不同生成模型的性能.
主要方法:
- 系统地比较GAN和VAE以增加实验性疲劳数据.
- 从断裂分析中提取关键缺陷参数 (尺寸,应力幅度).
- 使用原始和增强数据集开发和验证回归模型 (ANN,随机森林,SVR).
- 超参数优化和针对独立测试集的严格验证.
主要成果:
- 由GAN生成的数据显著提高了VHCF寿命预测模型的准确性.
- 用GAN增强的模型实现了卓越的性能指标,包括更高的R2得分 (0.91-0.97) 和更低的平均绝对误差 (1.13-1.62%).
- 基于VAE的方法显示预测准确度相对较低 (R2:0.86 ± 0.87,MAE:2.00-2.64%).
结论:
- 生成对抗网络 (GAN) 代表了一种突破性的工具,用于改进增材制造材料的疲劳寿命预测.
- 该研究提供了一种可转移的方法,用于在先进合金中以缺陷为主导的故障机制的数据驱动建模.
- 机器学习,特别是基于GAN的数据增强,提供了一种强大的方法来预测复杂材料系统中的VHCF寿命.
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