Nickel-based filler metals are frequently used in high temperature vacuum diffusion brazing for austenitic stainless-steel joints when components are subjected to high static or dynamic loads, corrosive environments and elevated temperatures. Due to melting point depressing metalloids such as silicon and boron, hard and brittle intermetallic phases are formed during the brazing process depending on the diffusion mechanisms. These brittle phases significantly affect mechanical and corrosive properties of the compounds. To quantify the influences of their amount, morphology and distribution, deep learning image segmentation was applied to segment these phases of the athermal solidification zone and the diffusion zone. Subsequently, characteristic microstructure parameters were calculated from these. The parameters of six different brazed joint variations were compared with their experimental characterization of mechanical and corrosive properties so that several correlations could be identified. Finally, a layer-by-layer removal of a brazed joint was performed using a focused ion beam, and a 3D model was reconstructed from the generated images to gain a mechanism-based understanding beyond the previous 2D investigations.
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