基于深度学习的阶段检索,使用复杂的光束形状来进行光束形状校正
Optics express
|August 13, 2025
概括
本研究介绍了一种深度学习方法,用于在增材制造中纠正扭曲的激光束形状. 该方法准确地识别出偏差,改善光束形状校正和打印件质量.
科学领域:
- 光学和光子学 在光学和光子学.
- 添加剂制造 添加剂制造 添加剂制造
- 人工智能的人工智能
背景情况:
- 激光束塑造对于提高增材制造速度和部件质量至关重要.
- 用于光束成型的相口可以因热等环境因素而扭曲.
- 准确的相位检索对于纠正这些扭曲至关重要.
研究的目的:
- 开发一个阶段检索方法,以复杂的光束形状,以激光为基础的增材制造.
- 通过识别偏差,实现精确的光束形状校正.
- 将深度学习模型与最先进的阶段检索技术进行比较.
主要方法:
- 利用计算机视觉深度学习模型来分析扭曲的光束形状.
- 使用泽尼克系数表示光学偏差.
- 实施基于已识别的泽尼克系数的相位检索算法.
主要成果:
- 深度学习模型成功地从扭曲的光束形状中识别出偏差.
- 与最先进的技术相比,拟议的方法在异常检测方面表现优越.
- 使用基于模拟数据的深度学习方法实现了有效的光束形状校正.
结论:
- 深度学习为激光束成型中的相位检索和偏差校正提供了强大的解决方案.
- 这种方法可以显著提高增材制造工艺的可靠性和精度.
- 这些发现为更先进,更适应的基于激光的制造系统铺平了道路.
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