  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
Laser Additive Manufacturing (LAM) 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. Porosity is a very common defect in LAM due to lack of
fusion, entrapped gas, and keyholes, directly affecting mechanical properties
like tensile strength, stiffness, and hardness, thereby compromising the
quality of the final product. 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. Utilizing our framework we
perform porosity segmentation in a case study of laser-based powder bed fusion
(L-PBF) and obtain high accuracy without using any labeled data to guide the
prompt tuning process. By capitalizing on lightweight foundation model
inference combined with unsupervised prompt generation, we envision
constructing a real-time anomaly detection pipeline that could revolutionize
current laser additive manufacturing processes, thereby facilitating the shift
towards Industry 4.0 and promoting defect-free production along with
operational efficiency.
