基于Segment Anything的无监督方法实现激光增材制造中的可提示缺陷分割
An unsupervised approach towards promptable defect segmentation in laser-based additive manufacturing by Segment Anything
AI总结：
本研究提出一种基于Segment Anything模型与无监督聚类提示生成的无标签缺陷分割框架，在激光粉末床熔融中实现高精度孔隙分割，助力实时检测与工业4.0。
摘要：
基础模型目前正在推动包括生物学、天文学和机器人学等各个领域在内的计算机视觉任务的范式转变，利用用户生成的提示来增强其性能。在激光增材制造（LAM）领域，基于图像的准确缺陷分割对于确保产品质量和促进实时过程控制至关重要。然而，此类任务通常面临多重挑战，包括缺乏标签和需要低延迟推理等。孔隙是LAM中一种非常常见的缺陷，其成因包括未熔合、气体截留和匙孔，直接影响抗拉强度、刚度和硬度等力学性能，从而损害最终产品的质量。为解决这些问题，我们构建了一个图像分割框架，使用基于最先进的视觉Transformer（ViT）的基础模型（Segment Anything Model），并结合一种使用无监督聚类的新的多点提示生成方案。利用我们的框架，我们在激光粉末床熔融（L-PBF）的案例研究中进行了孔隙分割，并在不使用任何标记数据来引导提示调整过程的情况下获得了高精度。通过利用轻量级基础模型推理与无监督提示生成的结合，我们设想构建一个实时异常检测流水线，该流水线可能彻底改变当前的激光增材制造工艺，从而促进向工业4.0的转变，并推动无缺陷生产以及运营效率的提升。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
