用AI自动分析3D打印熔池图像，精准测量尺寸与角度。
Automated Segmentation and Analysis of Microscopy Images of Laser Powder Bed Fusion Melt Tracks
- 基于U-Net架构训练分割模型，处理多源跨设备图像。
- 分类准确率超99%，F1分数超90%，测量结果稳定可靠。
- 适合材料科学家和工艺工程师优化3D打印参数。
随着金属增材制造（AM）应用增多，研究者正采用数据驱动方法优化打印条件。熔池横截面图像为调节工艺参数、建立参数扩展数据及识别缺陷提供了重要信息。本文提出一种图像分割神经网络，可自动识别并测量熔池横截面图像中的熔池尺寸。模型基于62张来自不同实验室、设备和材料的预标注图像进行训练，并结合图像增强技术。在合理调优超参数（如批次大小、学习率）后，模型分类准确率超过99%，F1分数超过90%。该模型在不同用户、设备和显微镜采集的图像上均表现出良好鲁棒性。后处理模块可提取熔池高度、宽度及润湿角。文章还探讨了提升模型性能及迁移学习的可能性，例如拓展至定向能量沉积等其他增材制造工艺。
原文摘要 · Abstract (English)
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.
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