The paper describes an open source computer vision-based hardware structure and software algorithm, which analyzes layer-wise 3-D printing processes, tracks printing errors, and generates appropriate printer actions to improve reliability. This approach is built upon multiple-stage monocular image examination, which allows monitoring both the external shape of the printed object and internal structure of its layers. Starting with the side-view height validation, the developed program analyzes the virtual top view for outer shell contour correspondence using the multi-template matching and iterative closest point algorithms, as well as inner layer texture quality clustering the spatial-frequency filter responses with Gaussian mixture models and segmenting structural anomalies with the agglomerative hierarchical clustering algorithm. This allows evaluation of both global and local parameters of the printing modes. The experimentally verified analysis time per layer is less than one minute, which can be considered a quasi-real-time process for large prints. The systems can work as an intelligent printing suspension tool designed to save time and material. However, the results show the algorithm provides a means to systematize in situ printing data as a first step in a fully open source failure correction algorithm for additive manufacturing.

本文介绍了一种基于开源计算机视觉的硬件结构和软件算法，该算法分析分层的3-D打印过程，跟踪打印错误并生成适当的打印机操作以提高可靠性。该方法建立在多阶段单眼图像检查的基础上，该检查可以监视打印对象的外部形状及其层的内部结构。从侧面高度验证开始，开发的程序使用多模板匹配和迭代最近点算法分析虚拟顶视图的外壳轮廓对应关系，以及使用高斯将空间频率滤波器响应聚类的内层纹理质量混合层次聚类算法的混合模型和结构异常分割。这允许评估打印模式的全局和局部参数。经实验验证的每层分析时间少于一分钟，对于大型打印件，这可以视为准实时过程。该系统可以用作智能打印悬挂工具，以节省时间和材料。但是，结果表明，该算法提供了一种将原位打印数据系统化的方法，这是用于增材制造的完全开源故障校正算法的第一步。