Development of multi-defect diagnosis algorithm for the directed energy deposition (DED) process with in situ melt-pool monitoring

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%.