The directed energy deposition (DED) process is attracting significant attention in high value-added industries, such as automobiles and aviation, because the process can freely manufacture components with complex shapes and directly stack them on metal substrates. However, it has a problem of degradation in reliability and poor reproducibility due to the influence of various parameters present in the process, and various defects are likely to occur inside and outside the product. To solve this problem, a proper data-driven prognostics and health management (PHM) approach is required. Therefore, this study proposes a multi-defect diagnosis algorithm for the DED process based on in situ melt-pool monitoring. First, the DED process monitoring testbed using a CCD camera and a pyrometer was established. The image pre-processing algorithms are developed for the effective extraction of region-of-interest (ROI) areas of the melt-pool and for effective quantification of internal defects, such as pores. Then, critical features of the melt-pool that are closely related to various defects—melting balls, low pores, and high pores—are extracted. Finally, the multi-defect diagnosis algorithm combining several binary classification models is developed, and it is demonstrated that support vector machine (SVM) showed the best performance, with an average accuracy of 92.7%.

定向能量沉积 (DED) 工艺在汽车和航空等高附加值行业引起了广泛关注，因为该工艺可以自由制造具有复杂形状的组件并将它们直接堆叠在金属基板上。但由于过程中存在的各种参数的影响，存在可靠性下降、再现性差的问题，产品内外容易出现各种缺陷。为了解决这个问题，需要一种适当的数据驱动的预测和健康管理 (PHM) 方法。因此，本研究提出了一种基于原位熔池监测的 DED 过程的多缺陷诊断算法。首先，建立了使用 CCD 相机和高温计的 DED 过程监控试验台。图像预处理算法是为有效提取熔池的感兴趣区域 (ROI) 区域和有效量化内部缺陷（如气孔）而开发的。然后，提取与各种缺陷（熔球、低孔隙和高孔隙）密切相关的熔池关键特征。最后，开发了结合多种二元分类模型的多缺陷诊断算法，并证明了支持向量机（SVM）表现出最好的性能，平均准确率为92.7%。