The invention relates to the technical field of
samarium-iron-
nitrogen permanent
magnet material manufacturing, and discloses a digital twinning
samarium-iron-
nitrogen injection molding process parameter optimization method and
system. According to the method, dynamic optimization is realized by integrating the digital twin and the physical
production line. And collecting and preprocessing
production line real-time sensor data. A digital twin comprising a particle layer, a melt layer and a pole layer is constructed and updated based thereon. By using the twin, the aggregation degree and orientation degree of the
samarium-iron-
nitrogen particles are predicted in a particle layer, the temperature, shear and
magnetic flux distribution are simulated in a melt layer, and the magnetic
performance index is evaluated in a magnetic pole layer. According to the prediction and evaluation results, the material temperature, the injection speed, the pulse vibration
magnetic field waveform and the pressure maintaining curve are optimized through an
artificial intelligence agent model. And finally, the optimized parameters are issued to a
production line for execution through the
programmable logic controller. According to the method, online self-adaptive adjustment of process parameters is achieved, and the
magnet forming quality and efficiency are improved.