The complex interaction between laser and material in Laser Wire Direct Energy Deposition (LW-DED) Additive Manufacturing (AM) benefits from process monitoring methods to ensure process stability and final part quality. Understanding the relationship between process parameters and melt pool geometrical characteristics can be used to effectively monitor and in-process control the process, as the melt pool geometrical characteristics serve as crucial indicators of process stability and quality.

This study presents a novel in-situ monitoring approach for LW-DED, utilizing process images for melt pool segmentation, melt pool geometrical characteristics estimation, process stability assessment, and bead geometry prediction. The segmentation of melt pool objects was successfully accomplished using Convolutional Neural Networks (CNN)-based models, enabling the prediction of essential parameters such as melt pool area, height, width, center of area, and the center point of the bounding box enclosing the melt pool. Multiple models were compared regarding the accuracy and processing speed using a controlled central composite design and random experiments. We used an Inconel alloy 625 wire and two distinct substrate materials for deposition, a coaxial laser welding head with a 3 kW fiber laser, and an off-axis welding camera for monitoring.

Among the CNN architectures evaluated, YOLOv8l demonstrated superior accuracy with mean Average Precision (mAP) values of 0.925 and 0.853 for Stainless Steel (SS) and low carbon steel (S355) substrates, respectively. Additionally, YOLOv8s exhibited a notable processing speed of over 114 frames per second, which indicates its suitability for real-time process control. Furthermore, the results indicate a significant correlation between process parameters and melt pool geometry variables. Notably, a clear correlation was established between melt pool characteristics and bead geometries obtained through microscopic examinations, including penetration depth and heat-affected zone. The analysis revealed a significant correlation for the bead area and width parameters. In relation to the bead height, while the correlation exhibited a lower magnitude compared to bead area and width, it remained responsive. In addition, the tensor masks derived from the developed models have proven to be highly effective in accurately predicting bead geometries.

The results demonstrate the effectiveness of YOLO-based algorithms for detecting and segmenting the melt pool. Statistical analysis confirms the significance of stabilized process data and the accuracy of melt pool geometric models. We demonstrate that integrating advanced monitoring and control techniques using artificial intelligence methods like CNN can facilitate process stability and quality control.

激光线直接能量沉积 (LW-DED) 增材制造 (AM) 中激光与材料之间的复杂相互作用得益于过程监控方法，可确保过程稳定性和最终零件质量。了解工艺参数和熔池几何特性之间的关系可以有效地监测和过程控制过程，因为熔池几何特性是工艺稳定性和质量的关键指标。

本研究提出了一种新型的 LW-DED 现场监测方法，利用过程图像进行熔池分割、熔池几何特征估计、过程稳定性评估和焊道几何形状预测。使用基于卷积神经网络 (CNN) 的模型成功完成了熔池对象的分割，能够预测熔池面积、高度、宽度、区域中心以及包围对象的边界框的中心点等基本参数。熔池。使用受控中心复合材料设计和随机实验对多个模型的准确性和处理速度进行了比较。我们使用 Inconel 合金 625 丝和两种不同的基材进行沉积，使用带有 3 kW 光纤激光器的同轴激光焊接头，以及用于监控的离轴焊接摄像机。

在评估的 CNN 架构中，YOLOv8l 表现出了卓越的准确性，对于不锈钢 (SS) 和低碳钢 (S355) 基板，平均精度 (mAP) 值分别为 0.925 和 0.853。此外，YOLOv8s 表现出每秒超过 114 帧的显着处理速度，这表明它适合实时过程控制。此外，结果表明工艺参数和熔池几何变量之间存在显着相关性。值得注意的是，熔池特性和通过显微镜检查获得的珠几何形状（包括渗透深度和热影响区）之间建立了明确的相关性。分析揭示了焊道面积和宽度参数之间的显着相关性。就珠子高度而言，虽然与珠子面积和宽度相比相关性表现出较低的幅度，但它仍然具有响应性。此外，事实证明，从开发的模型得出的张量掩模在准确预测珠子几何形状方面非常有效。

结果证明了基于 YOLO 的算法用于检测和分割熔池的有效性。统计分析证实了稳定工艺数据的重要性和熔池几何模型的准确性。我们证明，使用 CNN 等人工智能方法集成先进的监测和控制技术可以促进过程稳定性和质量控制。