The application discloses a
laser processing parameter autonomous generation
system and method based on digital twinning, relates to the field of digital twinning, and comprises the following steps: collecting and preprocessing
processing data in real time through a multi-source sensor; using
natural language processing to analyze task instructions, extract constraint conditions and form structured requirements; generating a processing parameter candidate set through a
Transformer model combined with historical
data migration learning, and performing virtual processing with the aid of multi-
physics field simulation; dynamically optimizing parameter weights by using an improved non-dominated sorting
genetic algorithm, and obtaining a globally optimal parameter combination through iterative
verification of a digital twin; after comparison between the virtual processing
verification and the task requirements, adaptively correcting the parameters, and continuously optimizing the
model parameters according to the deviation between the physical and
simulation data after actual processing. The application has the advantages that the digital twin is taken as the core, multi-source real-
time data, AI algorithms and multi-
physics field simulation are fused, and the autonomous generation, multi-objective optimization and virtual-real closed-loop iteration of
laser processing parameters are realized.