With the increasing adoption of metal additive manufacturing (AM), researchers and practitioners are turning to data-driven approaches to optimise printing conditions. Cross-sectional images of melt tracks provide valuable information for tuning process parameters, developing parameter scaling data, and identifying defects. Here we present an image segmentation neural network that automatically identifies and measures melt track dimensions from a cross-section image. We use a U-Net architecture to train on a data set of 62 pre-labelled images obtained from different labs, machines, and materials coupled with image augmentation. When neural network hyperparameters such as batch size and learning rate are properly tuned, the learned model shows an accuracy for classification of over 99% and an F1 score over 90%. The neural network exhibits robustness when tested on images captured by various users, printed on different machines, and acquired using different microscopes. A post-processing module extracts the height and width of the melt pool, and the wetting angles. We discuss opportunities to improve model performance and avenues for transfer learning, such as extension to other AM processes such as directed energy deposition.

翻译：随着金属增材制造技术的日益普及，研究人员和从业者正转向数据驱动方法来优化打印条件。熔道横截面图像为调整工艺参数、开发参数标定数据以及识别缺陷提供了宝贵信息。本文提出一种图像分割神经网络，能够自动从横截面图像中识别并测量熔道尺寸。我们采用U-Net架构，基于来自不同实验室、设备和材料的62张预标注图像数据集，结合图像增强技术进行训练。当批量大小和学习率等神经网络超参数经过适当调优后，学习模型在分类任务中表现出超过99%的准确率和90%以上的F1分数。该神经网络在不同用户拍摄、不同设备打印以及不同显微镜获取的图像测试中均表现出良好的鲁棒性。后处理模块可提取熔池的高度、宽度以及润湿角。我们探讨了提升模型性能的潜在途径和迁移学习的应用方向，例如将该方法扩展到定向能量沉积等其他增材制造工艺中。