Perhaps the most defining feature of field-assisted sintering technology (FAST) is the application of an electric current, in addition to the uniaxial pressure, to create resistive heating in and around the sample region. However, with a few exceptions, most research takes this as an unchangeable part of the process. Here, this current flow has been directed to specific regions within the toolset, using boron nitride as electrically insulating material. This caused the heating to occur in differing regions within the Ti-6Al-4V sample and mould over four insulating configurations, with the shift in current density resulting in an extreme disparity in the final microstructures. The samples were imaged and analysed with deep learning in MIPAR, alongside comparisons with finite element analysis (FEA) models for 20 s and 5 min dwell times, to provide the technique with predictive capabilities for grain size and microstructure. The results gathered imply significant potential for this concept to improve the flexibility of FAST, and reduce negative effects such as undesirable temperature profiles in size scaling sintering for industry.

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