积极学习用于预测材料挤出增材制造的拉伸性质
Tahamina Nasrin1, Masoumeh Pourali1, Farhad Pourkamali-Anaraki2
1Department of Plastics Engineering, University of Massachusetts Lowell, Lowell, MA, USA.
Scientific reports
|July 15, 2023
概括
机器学习使用有限的数据准确预测3D打印部件的拉伸性能. 适应式学习技术,如高斯过程回归,提高材料挤出制造的预测效率.
科学领域:
- 材料科学 材料科学 材料科学
- 增材制造 增材制造 增材制造
- 机器学习 机器学习
背景情况:
- 基于材料挤出的增材制造 (AM) 是一个不断发展的领域.
- 预测材料特性对于AM部件的设计和性能至关重要.
- 有限的培训数据往往阻碍了AM中准确的预测模型的开发.
研究的目的:
- 使用机器学习预测增材制造部件的拉伸性能.
- 评估适应性数据生成技术对有限数据场景的有效性.
- 为了比较不同机器学习模型在预测材料特性方面的性能.
主要方法:
- 利用机器学习 (ML) 模型,包括线性回归,回归,高斯过程回归 (GPR) 和K-最近邻居.
- 采用基于GPR的积极学习过程,用于自适应的数据生成.
- 使用Technomelt PA 6910 (热粘合剂) 制造的零件具有不同的加工参数.
- 基于有限的训练数据的预测准确度评估模型性能.
主要成果:
- 线性回归和回归在56%的预测中实现了<10%的误差.
- K-最近的邻居的准确性较低,预测的32%的误差为<10%.
- 尽管整体准确性较低,但高斯过程回归证明了自适应数据生成的最大好处.
结论:
- 机器学习模型,特别是具有自适应数据生成的机器学习模型,可以有效地预测增材制造材料的特性.
- 积极学习策略是有效的建立准确的预测模型与最小的数据.
- 这种方法加速了增材制造中的材料表征和优化.
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