A soft scanning electron microscopy for efficient segmentation of alloy microstructures based on a new self-supervised pre-training deep learning network

Abstract

To provide an on-site metallographic segmentation using only optical microscopy images, sSEM-Net, a soft scanning electron microscopy network, is developed based on a self-supervised pre-training deep learning framework. During model training, only a sparse collection of SEM images is necessary for annotation assistance. By integrating CNN and Transformer, sSEM-Net efficiently utilizes global context information while mitigating data dependency and computational resource constraints. Using only readily available optical microscopy images as input, sSEM-Net achieves metallographic segmentation comparable to SEM images, catering to rapid and cost-effective industrial needs. This methodology leverages non-destructive inspection attributes, catering to rapid and cost-sensitive industrial requirements. The efficacy of the proposed sSEM-Net is demonstrated through metallographic structure analysis of TC4 titanium alloy, with potential extensions to other alloy types.