A hybrid mechanism and data-driven optimization method of process parameters in laser cutting

Laser cutting quality is directly influenced by process parameters, which govern the formation of burrs and the extent of the heat-affected zone. Consequently, selecting and optimizing these parameters is crucial for achieving high-quality laser cutting results. Machine learning techniques have proven effective in process parameter optimization by establishing surrogate models that link process parameters with quality indicators. However, these models often overlook critical temperature field information generated during laser cutting, which provides valuable mechanistic insights. To overcome this limitation, a hybrid mechanism and data-driven optimization method is proposed. First, a laser cutting experimental platform is developed, and the full-factorial design with five factors at three levels is employed for data collection. Detailed laser-cutting physical models are then established to simulate key temperature field information, compensating for the scarcity of such data in real-world scenarios. Subsequently, a novel physics-informed neural network is designed with dual input branches to handle low-dimensional process parameters and high-dimensional temperature field data. Besides, the physics-informed neural network includes a focused fusion layer with an attention mechanism to selectively integrate the most relevant mechanistic features with process parameters. To further optimize the trained physics-informed neural network model, a clustering-assisted multi-objective evolutionary algorithm is developed, which leverages the clustering strategy to select and retrieve historical mechanistic data that best match candidate process parameters, ensuring valid surrogate model inputs and improving optimization efficiency. Experimental validation demonstrates that the proposed hybrid method significantly outperforms conventional machine learning approaches, delivering superior accuracy and reliability in laser cutting process parameter optimization.