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基于视觉估计的力量平衡近悬浮的融化池的垂垂和崩的预测预测
Longxi Luo1, Enze Qian1, Tao Lu1
1School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100080, China.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
这项研究使用计算机视觉来监测池稳定性在线弧增材制造 (WAAM) 的格子结构. 开发的方法准确地预测池状态,防止缺陷并提高印刷质量.
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:
- 电弧增材制造 (WAAM) 提供了高材料利用率和成本效益.
- 通过WAAM制造的格子结构存在精度问题,例如断杆和表面缺陷.
- 现有的方法在复杂的几何形状下缺乏精确控制池动态.
研究的目的:
- 在WAAM中开发一个可视监测系统,用于融化池的力量平衡.
- 预测和防止格子结构中形成不准确性.
- 提高WAAM流程的质量和可靠性.
主要方法:
- 使用优化的U-net模型进行融化池细分.
- 融化池应激理论的应用与视觉感知相结合.
- 实时力量平衡估计和状态预测 (正常,松,崩).
- 实验测试与计算机视觉算法的集成.
主要成果:
- 优化的U-net实现了高池细分精度 (98.18%的精度,96.64%的MIOU,98.34%的召回).
- 拟议的方法表明,在测试组中,池状态的预测率为90%.
- 在倾斜杆形成过程中成功监测和预测融化池的行为.
结论:
- 开发的视觉感知方法有效地监测和预测池状态,防止断杆缺陷.
- 这种方法有助于调整工艺参数并改善WAAM中的接质量.
- 这些发现支持智能无人WAAM和基于AI的制造监控系统的发展.
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