An unsupervised approach towards promptable porosity segmentation In laser powder bed fusion by segment anything

X-ray Computed Tomography (XCT) plays a vital role in characterizing internal porosity in laser Powder Bed Fusion (PBF-L) parts, where defects critically impact mechanical properties. Traditional methods for porosity segmentation rely on supervised machine learning, which requires large labeled datasets and high computational resources, limiting scalability. Foundation Models, pre-trained on diverse datasets, offer a flexible alternative by enabling high accuracy through user-provided prompts with minimal fine-tuning. This study develops a novel unsupervised porosity segmentation framework based on the Segment Anything Model (SAM), a Vision Transformer-based Foundation Model. Utilizing a multi-point prompt generation scheme with unsupervised clustering, it achieves a Dice Similarity Coefficient (DSC) of over 80%, enabling efficient defect segmentation without labeled data. Additionally, the framework’s performance is validated across varied prompt sets by bootstrapping for uncertainty quantification. By addressing scalability and automation challenges, this work highlights the transformative potential of Foundation Models in enhancing XCT-based porosity characterization for PBF-L parts.