State Evolution and Anomaly Prediction for Selective Laser Melting Multi-Systems Based on Decoupled Spatiotemporal Graph Autoregressive Networks

The long-term reliability of selective laser melting (SLM) equipment is constrained by the latent degradation and coupled faults of multiple underlying hardware systems under continuous high-load operations. Transitioning from passive defect detection to proactive prognostics and health management (PHM) is crucial to overcome bottlenecks in industrial applications. Accurately inferring future multi-system hardware states faces three major challenges: feature alignment difficulties under variable-length printing cycles, graph topology collapse under strong industrial noise, and dimensional conflicts along with error cascading divergence during the joint optimization of microscopic physical trajectories (continuous regression) and macroscopic system anomalies (discrete classification) in end-to-end prediction. To address these issues, this paper proposes a decoupled spatiotemporal graph autoregressive network (DS-GAN). First, a multi-scale feature pooling and degradation gating injection mechanism is constructed to align highly variable-length high-frequency sequences and adaptively integrate macroscopic health priors. This approach achieves feature decoupling under physical boundary constraints. Second, a restricted residual graph evolution mechanism is introduced to regulate dynamic coupling drift based on static physical topologies, effectively suppressing feature divergence in the spatial dimension. Finally, a heterogeneous multi-task autoregressive decoder based on homoscedastic uncertainty is designed; this decoder helps reduce the impact of accumulated errors along the time axis during multi-step forecasting. Long-sequence forward inference on a real SLM continuous printing dataset demonstrates that DS-GAN achieves an overall accuracy of 97.48% for multi-system anomalies while strictly limiting the global false alarm rate to 1.85%. Furthermore, quantitative results reveal the physical inertia mechanism within the prediction horizon, demonstrating that the model maintains high fidelity for physical trajectories with a Macro-RMSE of 0.062, even under a maximum predictive horizon of 20 s. This study provides a reliable theoretical and engineering framework for dynamic coupling correlation analysis and proactive fault warning of multi-systems in complex industrial equipment.