PI-Informer-Based Multi-Step Prediction of Side-Wall Root Weld Width for Ultra-Narrow Gap Arc Welding

Multi-step prediction of side-wall root weld width is essential for intelligent monitoring and closed-loop quality control in ultra-narrow gap arc welding. To address limited physical interpretability of pure data-driven models and insufficient control response time from single-step prediction, a Physics-Informed Informer (PI-Informer) multi-step prediction model constrained by welding thermal-balance mechanisms is proposed in this work. Thermal prior knowledge is embedded into training via a physically constrained loss function to compensate for physical deficiencies in pure data-driven learning. Local transient features are extracted from welding electrical signals by a multi-scale 1D-CNN and further integrated with global temporal dependencies of molten-pool heat accumulation through the Informer encoder. Unlike the standard Informer, decoder initialization is implemented with fused features of electrical signals and process parameters, enabling multi-step prediction at three typical prediction horizons of t, t + 3, and t + 15. From ablation experiments, the optimal weight β = 0.05 is identified for the best trade-off between accuracy and physical consistency, and multi-step errors are effectively mitigated by the 1D-CNN module. Under identical conditions, PI-Informer attains lower RMSE, MAE and MAPE than vanilla Informer, GRU and LSTM across evaluation metrics, and yields the lowest batch-averaged physical residual among all compared models. A favourable balance between accuracy and physical interpretability is achieved, and a feasible solution is provided for intelligent perception and proactive quality control in ultra-narrow gap welding.