Computation-Driven Microstructure Optimization of Carbon Ceramic Resistor for Enhanced Energy Endurance

The non-uniform microstructure of Carbon Ceramic Resistor (CCR) critically limits their energy endurance, posing a significant bottleneck in high-power applications. To overcome this, this work introduces a computation-driven paradigm to quantitatively re-engineer the material’s internal architecture. Drawing inspiration from AI-based image processing, we apply convolutional operators to a digitized microstructure map, transforming the random conductive network into a highly uniform and predictable structure. This targeted optimization is validated through both simulation and experiment, demonstrating a nearly threefold enhancement in energy endurance density, from 421.63 J/cm3 to an impressive 1300.56 J/cm3. Microstructural and electro-thermal analyses reveal the underlying mechanism: the engineered uniform conductive network effectively suppresses current channeling and the formation of destructive local hotspots. This work not only presents a validated solution for CCR but also establishes an efficient, predictable, and systematic design framework to supersede traditional approaches in developing high-performance composite materials.