选择性激光化的多层过程控制:一种强化学习方法
Stylianos Vagenas1, Taha Al-Saadi1, George Panoutsos1
1Department of Automatic Control & Systems Engineering, University of Sheffield, Sheffield, UK.
概括
强化学习增强了粉床融合 (PBF) 3D打印的控制. 这种数据驱动的方法有效地管理了选择性激光融中的复杂热问题,从而可以更好地控制完整的3D部件.
科学领域:
- 添加剂制造 添加剂制造 添加剂制造
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 粉床融合 (PBF) 是3D零件的关键增材制造技术.
- 在PBF中,复杂的物理现象阻碍了分析建模和以控制为导向的3D模型.
- 由于这些建模挑战,目前的PBF过程控制落后于其他制造业.
研究的目的:
- 引入强化学习 (RL) 框架,用于PBF的先进过程控制.
- 为了解决PBF中缺乏集成的,以控制为导向的模型.
- 为了证明RL在控制多层PBF工艺,特别是选择性激光化的有效性.
主要方法:
- 利用强化学习 (RL) 框架,一种适用于复杂或未知的过程模型的数据驱动方法.
- 专注于选择性激光化,一种基于激光的PBF工艺.
- 强调了在PBF应用中RL培训稳定的重要性.
主要成果:
- 成功展示了RL过程控制框架对完整3D部件 (多层) 的好处.
- 展示了PBF固有的热积累问题的有效管理.
- 证实了框架能够实现强大的整体流程控制的能力.
结论:
- 强化学习为粉末床融合中先进的过程控制提供了可行的解决方案.
- 该RL框架有效地解决了热管理挑战,改善了对复杂3D部件制造的控制.
- 这项研究为通过智能控制策略提高PBF过程稳定性和质量开辟了新的途径.
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