Laser Cutting Parameter Optimization With Bayesian Closed-Loop Modeling

The patent applies preliminary action by pre-training the neural network model once with comprehensive training data to establish a robust prediction system. This initial training phase creates a reusable model that can quickly determine optimized process parameters without requiring repeated extensive training when new materials or machines are introduced, thus resolving the contradiction between reliability and time consumption.

The patent uses copying by creating a virtual model (neural network) that replicates the complex relationships between process parameters and outcomes. This virtual model can be trained once and then copied/reused multiple times for different material and machine combinations, eliminating the need for repeated physical experiments and extensive training phases, thereby reducing time and costs while maintaining reliability.

The system performs preliminary training of the neural network model in advance, creating a pre-trained model that can quickly adapt to new materials or machines. This preliminary action ensures high accuracy is achieved through comprehensive initial training, while the pre-trained state enables rapid deployment without extensive retraining, thus maintaining productivity for real-time optimization.

The patent applies parameter changes by allowing the neural network model to efficiently adjust its internal parameters (weights and biases) when adapting to new materials or machine settings. This enables the system to maintain high accuracy through proper parameter tuning while requiring minimal time for adaptation, thereby resolving the contradiction between manufacturing precision and productivity.