A Hybrid Transformer–xLSTM Predictive Framework for Resilient Resin Level Regulation in Stereolithography

Accurate liquid level regulation is critical for ensuring printing quality and process stability in stereolithography (SLA) 3D printing. However, traditional liquid level control methods often suffer from insufficient prediction accuracy, poor disturbance rejection capability, and limited adaptability under dynamic printing conditions. To address these challenges, this paper proposes an enhanced Transformer-based time series prediction model integrated with an xLSTM module for SLA liquid level prediction and adaptive control. By embedding the xLSTM structure into the Transformer encoder, the proposed model combines the global dependency modeling capability of self-attention mechanisms with the local temporal feature extraction capability of recurrent memory units, thereby improving the prediction accuracy and robustness of liquid level sequences. Experimental datasets were collected from an actual SLA printing platform, including multiple process-related features such as layer height, laser power, platform position, and vacuum pressure. Comparative experiments were conducted against conventional Transformer, LSTM, xLSTM, GRU, TCN, and PID-based methods. The results demonstrate that the proposed model achieves the best prediction performance, with an MAE of 0.174, RMSE of 0.222, and R² of 0.9903. Compared with the original Transformer model, the proposed approach significantly reduces prediction error and improves fitting stability. In disturbance rejection experiments, the proposed strategy effectively suppresses liquid level fluctuations under sudden pulse interference conditions, exhibiting superior robustness and dynamic response capability compared with traditional PID control. Furthermore, physical printing experiments verify that the proposed method can improve surface quality, contour accuracy, and structural stability of printed parts. Overall, the proposed Transformer–xLSTM framework provides an effective intelligent prediction and control solution for SLA liquid level regulation, offering significant potential for high-precision and intelligent additive manufacturing applications.