在激光粉床融合中实时检测钥匙孔
Zhongshu Ren1, Lin Gao1, Samuel J Clark2
1Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA 22904, USA.
Science (New York, N.Y.)
|January 5, 2023
概括
研究人员在激光粉床融合中发现了两种钥匙孔振荡类型,使得可精确检测孔隙缺陷. 这种金属增材制造的进步有望通过防止钥匙孔的多孔性来提高零件质量.
科学领域:
- 材料科学
- 制造工程
- 物理
背景情况:
- 透性缺陷阻碍了基于激光的金属添加剂制造的广泛使用.
- 由于过多的激光能量造成不稳定的蒸汽压缩区域,
- 了解钥匙孔动态对于提高零件质量和工艺可靠性至关重要.
研究的目的:
- 研究激光粉床融合过程中钥匙孔形成和振荡的机制.
- 开发一种实时检测和预测钥匙孔多孔性的方法.
- 提高增材制造金属零件的质量和可靠性.
主要方法:
- 同时使用高速同步X射线成像和热成像.
- 我们使用多物理模拟来模拟钥匙洞的动态.
- 机器学习算法用于分析成像数据和检测多孔性事件.
主要成果:
- 在Ti-6Al-4V激光粉床融合中发现了两种不同类型的钥匙孔振荡.
- 一种机器学习方法实现了近乎完美的预测率,
- 开发的方法显示了次于毫秒的时间分辨率来检测事件.
结论:
- 这项研究提供了关键孔不稳定性和孔隙形成的基本见解.
- 开发的机器学习方法为商业增材制造系统的现场监控和缺陷预防提供了实际解决方案.
- 这项工作为更高准确性和更可靠的金属增材制造工艺铺平了道路.
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