论文标题
朝在线监视和数据驱动控制：激光粉床融合过程的分割算法的研究
Towards Online Monitoring and Data-driven Control: A Study of Segmentation Algorithms for Laser Powder Bed Fusion Processes
论文作者
论文摘要
越来越多的激光粉床融合机使用离轴红外摄像头来改善在线监控和数据驱动的控制功能。但是，仍然存在严重缺乏算法解决方案来正确处理这些相机中的红外图像，这导致了几个关键局限性：缺乏针对激光轨道的在线监视功能，对于数据驱动的方法的红外图像不足，以及用于存储基础图像的大型内存要求。为了解决这些局限性，我们研究了30多种分割算法，将每个红外图像分割为前景和背景。通过根据每种算法的分割精度，计算速度和溅射检测特性评估每个算法，我们确定了有希望的算法解决方案。鉴定出的算法可以容易地应用于激光粉末床融合机以解决上述每个局限性，从而显着改善了过程控制。
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.
