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.

中文翻译：

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利用数据驱动元模型和数值模拟虚拟放大 ZnO 压敏电阻烧结

ZnO压敏电阻具有非线性电流/电压特性，可作为浪涌电压的电路保护器件。要在 ZnO 压敏电阻中实现所需的电性能，需要对其微观结构进行细致的控制，这是通过调节烧结过程中的参数来实现的，例如烧结温度、烧结时间、加热速率和冷却速率。在本研究中，通过采用实验方法设计并结合上述四个过程变量和介电常数，使用从实验室规模熔炉获得的数据集，通过机器学习开发了元模型。然后应用混合元启发式优化算法来确定最佳工艺条件，通过元模型最大化介电常数。为了使得出的最佳条件适应放大烧结炉，通过数值模拟从实验室规模和放大烧结炉收集温度数据。通过将校正后的工艺变量输入元模型来预测烧结ZnO压敏电阻的介电常数，与实验值存在4.6%的差异，代表在放大炉中烧结的ZnO压敏电阻的平均介电常数。将这种基于仿真的虚拟放大技术应用于陶瓷加工可以显着减少放大工艺所需的开发时间。