The invention discloses a mechanical product multi-
field simulation and
prediction system based on digital twinning, and relates to the technical field of thermal error digital twinning. A main shaft load current, a rotating speed, a bearing temperature, a workpiece surface temperature and a cooling liquid parameter are obtained in real time; rapidly predicting a first thermal error according to the load current and the rotating speed by using a preset multi-layer feed-forward neural network, dynamically calculating a
thermal inertia time coefficient in combination with the cooling liquid efficiency and the
thermal state of a workpiece, and filtering and extracting a slowly-changing
thermal state from the temperature of a main shaft bearing according to the
thermal inertia time coefficient, thereby obtaining second thermal error prediction; a fusion weight is generated according to the load current change rate and the
thermal inertia time coefficient, weighted fusion is carried out on the two predicted values, and a compensation instruction is output; and by monitoring the stability of the fusion weight, the thermal
inertia time is reversely and dynamically adjusted to suppress oscillation. According to the method, high-robustness and self-adaptive accurate compensation of complex thermal errors is realized in field
processing with changeable environments and working conditions, and the long-term stability of
processing precision is remarkably improved.