An increasing number of laser powder bed fusion machines use off-axis infrared cameras to improve online monitoring and data-driven control capabilities. However, there is still a severe lack of algorithmic solutions to properly process the infrared images from these cameras that has led to several key limitations: a lack of online monitoring capabilities for the laser tracks, insufficient pre-processing of the infrared images for data-driven methods, and large memory requirements for storing the infrared images. To address these limitations, we study over 30 segmentation algorithms that segment each infrared image into a foreground and background. By evaluating each algorithm based on its segmentation accuracy, computational speed, and spatter detection characteristics, we identify promising algorithmic solutions. The identified algorithms can be readily applied to the laser powder bed fusion machines to address each of the above limitations and thus, significantly improve process control.
翻译:越来越多的激光粉床聚变机使用离轴红外摄像头来改进在线监测和数据驱动控制能力,然而,仍然严重缺乏适当处理这些照相机提供的红外图象的算法解决办法,导致若干关键的局限性:激光轨迹缺乏在线监测能力,数据驱动方法红外图象的预处理不足,以及存储红外图象需要大量记忆。为了解决这些局限性,我们研究了30多个分离算法,将每个红外图象分为一个前台和背景。我们根据每个算法的分解精度、计算速度和飞溅探测特性来评估每一种算法,我们确定了有希望的算法解决办法。所查明的算法可以很容易地应用于激光粉床聚变机,以解决上述每一种限制,从而大大改进过程控制。