Surface topography data fusion of additive manufacturing based on confocal and focus variation microscopy

In this paper, two innovative data fusion methods are proposed for reconstructing the surfaces produced by directed energy deposition (DED) additive manufacturing. The surface topographic data were obtained from confocal laser scanning microscopy (CLSM) and focus variation microscopy (FV). The first method (competitive data fusion) aims to improve the data quality by combining both the advantages of the CLSM and FV techniques, while the second method (cooperative data integration) is designed for generating a single representation that contains not only global information but also local details. The results show that both fusion methods achieved satisfactory results: in the competitive fusion, the fused data preserved the characteristics of FV data while its vertical resolution is also improved by integrating the short waves from the CLSM data; the cooperative data fusion achieved one pixel precision of the surface registration which adopted the feature-based registration method with the help of color image information. The computational complexity is reduced from O((m×n)²) to O(m×n + k). Both proposed data fusion methods provided innovative solutions for the microscopic surface reconstruction and surface representation in multiscales in the field of additive manufacturing.