Automated Segmentation and Analysis of Microscopy Images of Laser Powder Bed Fusion Melt Tracks
This work addresses the need for efficient process parameter tuning and defect identification in metal additive manufacturing, though it is incremental as it applies an existing method to a new domain-specific dataset.
The paper tackled the problem of automatically segmenting and analyzing microscopy images of laser powder bed fusion melt tracks to optimize metal additive manufacturing, achieving over 99% classification accuracy and over 90% F1 score with a U-Net-based neural network.
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.