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激光加工的实时成像通过等离子体深度学习

James A Grant-Jacob, Ben Mills, Michalis N Zervas

|December 13, 2023

概括

深度学习分析激光诱导的等离子体,在5秒激光加工过程中对表面进行成像. 这使得实时监控,当直接观察是不可能的.

科学领域:

材料科学材料科学材料科学
光学工程是指光学工程.
人工智能的人工智能

背景情况:

激光材料处理面临着由于遮蔽等离子体而导致的实时成像挑战.
等离子体的空间结构与样品的表面形状相关,提供了一个间接的成像途径.

研究的目的:

为了展示激光加工过程中实时表面成像的深度学习.
使用等离子体图像预测表面在 femtosecond 激光脉冲之前和之后的外观.

主要方法:

使用深度学习算法来分析激光生成等离子体的摄像头图像.
将等离子体图像与底层的表面地形相关联.

主要成果:

成功地从等离子体图像中实时预测了表面的外观.
在激光切除过程中证明了间接表面成像的可行性.

结论:

深度学习为实时监控激光材料加工提供了一种可行的方法.
这种技术对于直接样本观察受到等离子体阻碍的应用至关重要.

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