Virtual scale-up of ZnO varistor sintering with a data-driven metamodel and numerical simulation

Abstract

A ZnO varistor functions as a circuit protection device against surge voltage because of its nonlinear current/voltage characteristics. Achieving the desired electrical properties in a ZnO varistor necessitates meticulous control over its microstructure, which is achieved by regulating parameters during the sintering process, such as the sintering temperature, sintering time, heating rate, and cooling rate. In this study, a metamodel was developed through machine learning using a dataset obtained from a laboratory-scale furnace by employing the design of experiment approach and incorporating the aforementioned four process variables and permittivity. A hybrid metaheuristic optimization algorithm was then applied to determine the optimal process conditions, maximizing permittivity through the metamodel. To adapt the derived optimal conditions to a scale-up sintering furnace, temperature data were collected from laboratory-scale and scale-up sintering furnaces through numerical simulation. The permittivity of the sintered ZnO varistor, which was predicted by inputting the corrected process variables into the metamodel, exhibited a 4.6% difference from the experimental value, representing the average permittivity of ZnO varistors sintered in the scale-up furnace. The application of this simulation-based virtual scale-up to ceramic processing can significantly reduce the development time required for scale-up processes.