In hetero-aggregates, different materials form sintered hetero-contacts, which lead to new material properties. In this study, the influence of process parameters on the formation of hetero-contacts in Double Flame Spray Pyrolysis (DFSP) was quantified. The mixing of jets was monitored by Particle Image Velocimetry (PIV) measurements as well as by thermocouples and compared to the single flame setting. Overlap of the jets was evaluated using tracers to visualize the mixing zone. Ten CuO/CeO2 model material samples were prepared with different flame inclination angles, nozzle distances and different particle volume fractions. Mixing on hetero-aggregate level was quantified using Convolutional Neural Network (CNN) evaluations of STEM images, Temperature-Programmed Reduction (TPR) and Differential Centrifugal Sedimentation (DCS) disk centrifugation to determine cluster sizes and hetero-coordination numbers as mixing quantifiers. Here, good correlations between the differently measured quantifiers were observed.