通过增材制造工艺,通过机器学习来预测316L不钢的拉力强度
Qing Gao1,2,3, Congyu Wang2,3,4, Jiayan Hu1
1State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310030, China.
Micromachines
|February 27, 2026
概括
这项研究提出了一个新的模型,将CNN和RF集成在一起,以预测SLM制造的316L不钢部件的抗拉强度. 与单独使用CNN相比,综合模型显著提高了预测准确性.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 添加剂制造 添加剂制造 添加剂制造
背景情况:
- 预测增材制造组件的抗拉强度对于工程应用至关重要.
- 增材制造 (AM) 中的工艺参数使得拉伸强度预测具有挑战性.
研究的目的:
- 开发一个集结卷积神经网络 (CNN) 和随机森林 (RF) 的协同模型,用于预测通过选择性激光化 (SLM) 制造的316L不钢部件的抗拉强度.
- 对实验数据评估模型的性能,并与现有方法进行比较.
主要方法:
- 使用CNN和RF算法的组合开发了一个预测模型.
- 在42个数据集上训练模型,并使用12个实验数据集进行验证.
- 使用平均平方误差 (MSE) 和平均绝对误差 (MAE) 量化预测性能.
主要成果:
- 集成的CNN-RF模型实现了0.00295的MSE和0.0344.0的MAE.
- 这比单独的CNN显著改善,MSE (3.28%) 和MAE (31.88%) 的减少.
- 达到0.9576的高相关系数,表明准确的预测,即使是小数据集.
结论:
- 协同效应的CNN-RF模型为SLM制造的316L不钢提供了高精度的抗拉强度预测,单独优于CNN.
- 该模型即使在有限的数据中也证明了有效性,为预测各种材料的机械性质提供了一个框架.
- 添加相对密度和维克尔硬度并没有提高相同样本大小的预测准确性.
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