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在使用深度学习的五秒激光钻孔过程中预测洞深度

Dong-Wook Lim¹, Myeongjun Kim², Philgong Choi³

¹Department of Mechanical Engineering, Inha University, Incheon 22212, Republic of Korea.

|July 8, 2023

概括

本研究介绍了一种深度学习方法,用于估计使用2D图像在高面比激光钻探中钻孔深度. 该方法实现了高精度,有助于实时控制加工过程.

科学领域:

制造业工程制造工程
光学工程是指光学工程.
人工智能的人工智能

背景情况:

在高面积比激光钻探过程中测量钻孔深度是具有挑战性和耗时的.
精确的深度测量对于激光加工的工艺控制和质量保证至关重要.

研究的目的:

开发一种非接触式方法,用二维图像来估计钻孔深度.
为了利用深度学习来精确地预测高面比激光钻探的深度.

主要方法:

利用深度学习方法来从捕获的2D洞图像中预测洞深.
优化了激光钻探参数 (功率,周期) 和图像采集条件 (亮度,曝光率,马).
使用干扰仪提取对比数据用于图像分析.

主要成果:

对于深度高达100微米的孔,实现了5微米内的预测精度.
确定了最佳显微镜曝光时间和马值,以准确预测洞形状.
证明了使用二维成像和深度学习进行现场深度估计的可行性.

结论:

拟议的深度学习方法为估计钻孔深度提供了有效和准确的解决方案.
这种方法可以促进实时监测和控制高面比激光钻孔过程.
这些发现有助于自动化激光制造和质量检查的进步.

关键词:

一个盲孔,一个盲孔,一个盲孔.深度学习是一种深度学习.五秒激光激光器是如何使用的激光微机加工激光微机加工化是一种化.

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