机器视觉为同轴电线定向能量沉积过程提供池稳定性的定量分析
Braden McLain1, Remy Mathenia1, Todd Sparks2
1Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA.
Materials (Basel, Switzerland)
|November 9, 2024
概括
本研究介绍了一种计算机视觉技术,用于监测增材制造中的电线稳定性. 非机器学习模型准确地检测过程稳定性,从而实现实时控制,以改进金属制造.
科学领域:
- 材料科学 材料科学 材料科学
- 制造业 工程 制造工程
- 计算机视觉 计算机视觉
背景情况:
- 基于线材的增材制造 (AM),特别是定向能量沉积 (DED),为复杂的金属部件提供了可扩展性和高沉积率.
- 在线电DED过程中保持工艺稳定性至关重要,因为能量不足会导致电线断裂和池流,而过量的能量会导致过热和液体滴水.
研究的目的:
- 提出和评估计算机视觉技术,用于实时状态和事件检测DED中的电线稳定性.
- 开发基于非机器学习的流程监控模型,允许用户解释和调整.
主要方法:
- 使用强度变化和对差异计算来评估电线稳定性,开发了一个计算机视觉模型.
- 该模型的性能通过一维激光功率实验得到验证,产生了各种稳定性条件.
- 通过将模型预测与沉积珠的3D几何数据进行比较来评估准确性.
主要成果:
- 拟议的计算机视觉模型有效地检测了在线DED过程中过程稳定的各种状态.
- 该模型在识别稳定和不稳定的沉积条件方面表现出高度准确性.
- 该系统证明能够根据视觉线索区分最佳,不足和过度的能量输入.
结论:
- 开发的计算机视觉技术是监测DED中的电线稳定性的有能力和准确的系统.
- 非机器学习方法允许操作人员进行可解释性和潜在的手动调整.
- 这种方法显示了作为实时控制系统在有线AM中实施的巨大潜力.
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