An unsupervised approach towards promptable defect segmentation in laser-based additive manufacturing by Segment Anything

Foundation models are currently driving a paradigm shift in computer vision tasks for various fields including biology, astronomy, and robotics among others, leveraging user-generated prompts to enhance their performance. In the manufacturing domain, accurate image-based defect segmentation is imperative to ensure product quality and facilitate real-time process control. However, such tasks are often characterized by multiple challenges including the absence of labels and the requirement for low latency inference among others. To address these issues, we construct a framework for image segmentation using a state-of-the-art Vision Transformer (ViT) based Foundation model (Segment Anything Model) with a novel multi-point prompt generation scheme using unsupervised clustering. We apply our framework to perform real-time porosity segmentation in a case study of laser base powder bed fusion (L-PBF) and obtain high Dice Similarity Coefficients (DSC) without the necessity for any supervised fine-tuning in the model. Using such lightweight foundation model inference in conjunction with unsupervised prompt generation, we envision the construction of a real-time anomaly detection pipeline that has the potential to revolutionize the current laser-based additive manufacturing processes, thereby facilitating the shift towards Industry 4.0 and promoting defect-free production along with operational efficiency.