The rapidly growing technological innovation of directed energy deposition leads to an increase in part complexity as well as quality and mechanical properties of manufacturable components. However, the variety of process parameters and influencing factors still requires skilled operators, who observe the machine tools. For an unobserved use of deposition welding machines, well parametrized and validated monitoring systems have to analyze the process to detect irregularities and finally initiate a machine stop. This study focuses on nozzle adhesions that frequently occur when tool or high-speed steels are processed. This effect leads to decreasing quality or ultimately to a failure of the whole welding process. In this work, the authors present an algorithm and the corresponding parametrization to automatically detect nozzle adhesions based on images from a coaxial camera, integrated in the laser head. The algorithm is based on a detailed image analysis from which temporal and spatial patterns are derived. In particular, the algorithm calculates a nozzle adhesion indicator based on the heat intensity distribution in an experimentally derived shaped area on the inner nozzle boundary. It is parametrized in such a way that process-critical adhesions are detected. The algorithm was parametrized using an experimental setup with four materials: stainless steel (X2CrNiMo17-12-2), tool steel (X35CrMoMn7-2-1), high-speed steel (HS6-5-2C), and the nickel-based alloy NiCr19NbMo.

中文翻译：

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基于图像的粉末馈送定向能量沉积喷嘴粘附检测算法

快速发展的定向能量沉积技术创新导致零件复杂性以及可制造组件的质量和机械性能的增加。然而，工艺参数和影响因素的多样性仍然需要熟练的操作员，他们观察机床。对于未观察到的堆焊机使用，经过良好参数化和验证的监控系统必须分析过程以检测异常情况并最终启动机器停止。本研究侧重于加工工具钢或高速钢时经常发生的喷嘴粘连。这种影响会导致质量下降或最终导致整个焊接过程失败。在这项工作中，作者提出了一种算法和相应的参数化，以根据来自集成在激光头中的同轴相机的图像自动检测喷嘴粘连。该算法基于详细的图像分析，从中可以导出时间和空间模式。特别地，该算法基于内喷嘴边界上实验导出的成形区域中的热强度分布来计算喷嘴粘附指标。它以检测过程关键性粘附的方式进行参数化。该算法使用四种材料的实验装置进行参数化：不锈钢 (X2CrNiMo17-12-2)、工具钢 (X35CrMoMn7-2-1)、高速钢 (HS6-5-2C) 和镍基合金NiCr19NbMo。该算法基于详细的图像分析，从中可以导出时间和空间模式。特别地，该算法基于内喷嘴边界上实验导出的成形区域中的热强度分布来计算喷嘴粘附指标。它以检测过程关键性粘附的方式进行参数化。该算法使用四种材料的实验装置进行参数化：不锈钢 (X2CrNiMo17-12-2)、工具钢 (X35CrMoMn7-2-1)、高速钢 (HS6-5-2C) 和镍基合金NiCr19NbMo。该算法基于详细的图像分析，从中可以导出时间和空间模式。特别地，该算法基于内喷嘴边界上实验导出的成形区域中的热强度分布来计算喷嘴粘附指标。它以检测过程关键性粘附的方式进行参数化。该算法使用四种材料的实验装置进行参数化：不锈钢 (X2CrNiMo17-12-2)、工具钢 (X35CrMoMn7-2-1)、高速钢 (HS6-5-2C) 和镍基合金NiCr19NbMo。它以检测过程关键性粘附的方式进行参数化。该算法使用四种材料的实验装置进行参数化：不锈钢 (X2CrNiMo17-12-2)、工具钢 (X35CrMoMn7-2-1)、高速钢 (HS6-5-2C) 和镍基合金NiCr19NbMo。它以检测过程关键性粘附的方式进行参数化。该算法使用四种材料的实验装置进行参数化：不锈钢 (X2CrNiMo17-12-2)、工具钢 (X35CrMoMn7-2-1)、高速钢 (HS6-5-2C) 和镍基合金NiCr19NbMo。