当前位置：首页 > 过刊浏览->2024年45卷第8期
基于机器学习的激光粉末床熔融工艺参数优化、 过程监测和服役寿命预测的方法论
Methodological Approach to Optimizing Process Parameters, Monitoring, and Predicting Fatigue Life in Laser Powder Bed Fusion via Machine Learning
浏览(1067) 下载(0)
- DOI:
- 作者:
- 王信莲，李 杰，万 杰，袁睿豪，李金山，王 军
WANGXinlian, LI Jie, WAN Jie, YUAN Ruihao, LI Jinshan, WANG Jun
- 作者单位:
- 西北工业大学凝固技术国家重点实验室，陕西西安710072
State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi'an 710072, China
- 关键词:
- 参数优化；缺陷监测；服役寿命预测；数据驱动；物理驱动
parameter optimization; defect monitoring; fatigue life prediction; data-driven; physics-driven
- 摘要:
- 激光粉末床熔融工艺(LPBF)因成形精度较高、制造周期短，成为增材制造的主流方法之一，但其制造工艺
的可重复性、生产过程的可解释性和成形构件的可靠性仍面临重大挑战。 LPBF成形过程涉及的参数众多，不同工艺参
数的选择会导致构件内部产生不同类型的微观/宏观缺陷，进而影响构件的服役性能。 因此明确工艺参数、缺陷和性能
三者之间的联系是当前激光粉末床熔融制造的热点与难点。 作为大数据与人工智能发展到一定阶段的必然产物，机器
学习方法为有效处理高维物理量之间的复杂非线性关系提供了契机，在增材制造过程中工艺参数优化、缺陷监测和性
能预测等方面得到持续关注。本文介绍了常用的机器学习(ML)模型，总结了LPBF中ML的输入信息，重点分析了数据
驱动和物理驱动ML模型在LPBF各领域的应用，最后指出当前ML的局限性，并探讨了其发展趋势和技术前景。
Laser powder-bed fusion (LPBF) is recognized as a predominant method within additive manufacturing because of its high precision and shortened manufacturing cycle. However, the process still faces significant challenges regarding the repeatability of its manufacturing techniques, the interpretability of the production process, and the reliability of the formed components. The LPBF formation process involves a multitude of parameters, and the selection of different process parameters can lead to various types of micro/macrodefects within the components, thereby affecting their service performance. Therefore, clarifying the interconnections among process parameters, defects, and performance represents a current hot topic and a formidable challenge in laser powder bed fusion manufacturing. As an inevitable product of the evolution of big data and artificial intelligence, machine learning (ML) methods offer opportunities to address the complex nonlinear relationships between high-dimensional physical quantities effectively. In the realm of additive manufacturing, ML has garnered sustained interest for its applications in process parameter optimization, defect monitoring, and performance prediction. This article reviews common ML models, summarizes the input information for ML in the LPBF, and focuses on analysing the applications of data-driven and physics-driven ML models in various domains of the LPBF. Finally, it highlights the current limitations of ML and explores its development trends and technical prospects.