An On-Line System for High Temperature Dielectric Property Measurement of Microwave-Assisted Sintering Materials

Abstract

Microwave-assisted sintering materials have been proven to deliver improvements in the mechanical and physicochemical properties of the materials, compared with conventional sintering methods. Accurate values of dielectric properties of materials under high temperatures are essential for microwave-assisted sintering. In view of this, this paper, proposes an on-line system to measure the high temperature dielectric properties of materials under microwave processing at a frequency of 2450 MHz. A custom-designed ridge waveguide is utilized, where samples are heated and measured simultaneously. An artificial neural network (ANN) trained with the corresponding simulation data is integrated into this system to reverse the permittivity of the measured materials. This whole system is tested at room temperature with different materials. Accuracies of measuring dielectric property with an error lower than 9% with respect to theoretical data have been achieved even for high loss media. The functionality of the dielectric measurement system has also been demonstrated by heating and measuring Macor and Duran ceramic glass samples up to 800 °C. All the preliminary experiments prove the feasibility of this system. It provides another method for dielectric property measurement and improves the understanding of the mechanism between microwave and media under high temperatures, which is helpful for optimizing the microwave-assisted sintering of materials.

Design of High Temperature Complex Dielectric Properties Measuring System Based on XGBoost Algorithm

“…As shown in Table 4, the relative complex permittivity of methanol and ethanol and the relative complex permittivity of mixed solutions of different chemical solvents were tested using the system. The reference values are obtained by other experiments at the frequency of 2450 MHz [32], and the values of mixtures can be obtained by the Bruggeman formula [33]. The measurement errors are all within 8%, which basically meets the general measurement accuracy requirements.…”

Deep neural network for retrieving material's permittivity from S‐parameters

“…9 For instance, a simple ANN-based approach was used with a coplanar waveguide-based system to characterize the dielectric sample while the material was sintering. 10 Mattsson et al 11 also employed a neural network model to analyze biological materials' dielectric properties. More specifically, Nov et al 12 presented a deep neural network (DNN), that is, which is a multilayer feedforward model built on a generic ANN model, to characterize the properties of a solid sample in a broad frequency from 1 to 10 GHz.…”

“…To circumvent this constraint, numerical solutions based on artificial neural networks (ANNs) were widely investigated 9 . For instance, a simple ANN‐based approach was used with a coplanar waveguide‐based system to characterize the dielectric sample while the material was sintering 10 . Mattsson et al 11 also employed a neural network model to analyze biological materials' dielectric properties.…”