Laser Processing Simulation Using Deep Learning for Ablation Prediction

The patent introduces machine learning models as an intermediary between laser processing parameters and ablation outcomes. The learning unit acquires datasets of laser processing conditions and ablation results, then uses neural networks or other ML algorithms to learn the non-linear relationships. This intermediary system translates complex non-linear parameter interactions into accurate predictions of ablation degree, resolving the contradiction by handling complexity internally while providing precise output.

The patent transforms the approach by changing from direct parameter control to parameter learning. Instead of relying on predefined linear relationships between parameters, the system collects extensive processing data and uses machine learning to automatically discover optimal parameter settings. The learning unit adjusts model parameters based on training data, enabling accurate prediction of ablation outcomes without manually modeling complex non-linear relationships.

The patent applies preliminary action by capturing and analyzing processing state information during laser irradiation, not just after completion. The system acquires datasets that include processing degree information at various stages of irradiation, allowing the machine learning model to learn how processing evolves over time. This enables prediction of intermediate processing states and better control of the irradiation process itself.

The patent implements feedback mechanisms where the learning unit continuously refines its understanding of processing degree based on acquired data. By feeding back processing intermediate results and adjusting the model accordingly, the system learns to predict not only final outcomes but also the progression of ablation during irradiation. This feedback loop preserves information about processing degree at different stages, enabling more informed control decisions.