An unsupervised approach towards promptable defect segmentation in
  laser-based additive manufacturing by Segment Anything
Israt Zarin Era Imtiaz Ahmed Zhichao Liu Srinjoy Das
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.