The invention relates to the technical field of
data processing, in particular to a data enhancement prediction method for the thermoelastic quality factor of a micro-hemispherical
resonator, which comprises the following steps: S1, determining the
radius size of the manufactured micro-hemispherical
resonator, determining the
simulation value range of the
radius and thickness of an
anchor point of the
resonator, and determining the
simulation value range of the thickness of the resonator;
harmonic oscillators with different geometric boundary dimensions are established through high-temperature
blow molding simulation; s2, thermodynamic simulation is carried out on the
harmonic oscillator appearance obtained through
blow molding simulation, and a
harmonic oscillator thermoelastic quality factor
data set is established; s3, performing data enhancement on the thermoelastic quality factor
data set by adopting a cubic polynomial fitting method; and S4, training the enhanced thermoelastic quality factor
data set by using a
deep learning model to obtain a
harmonic oscillator thermoelastic quality factor prediction model. According to the method, the
bottleneck of scarcity of high-precision simulation data is broken through, unification of high precision and high efficiency is realized, the robustness and reliability of a prediction model are improved, and the method has wide
engineering applicability and popularization value.