Prediction Model of Laser Cutting Quality Based on Deep Learning Algorithm

As the core process of modern manufacturing industry, the quality of laser cutting is affected by the complex coupling of multiple dynamic process parameters, and the traditional prediction model is difficult to take into account the relevance of long-term and short-term characteristics. In this paper, a hybrid deep learning architecture is proposed, which combines Long Short-Term Memory Network (LSTM) and Gated Recurrent Unit (GRU) to model the macro-evolution and micro-dynamic fluctuation of process parameters through hierarchical temporal feature extraction mechanism. The model combines the advantage of long period memory of LSTM with the efficient calculation of GRU, and constructs a closed-loop optimization system of preprocessing-prediction-feedback to realize the multi-level intelligent evaluation of laser cutting quality. Experimental verification shows that the hybrid model significantly improves the adaptability and prediction robustness to complex working conditions, and provides innovative methodological support for real-time control and intelligent manufacturing transformation of high-precision laser processing technology.