Online high-precision prediction method for injection molding product weight by integrating time series/non-time series mixed features and feature attention mechanism

• URL: http://arxiv.org/abs/2506.18950v1
• Date: Mon, 23 Jun 2025 08:40:50 GMT
• Title: Online high-precision prediction method for injection molding product weight by integrating time series/non-time series mixed features and feature attention mechanism
• Authors: Maoyuan Li, Sihong Li, Guancheng Shen, Yun Zhang, Huamin Zhou,
• Abstract summary: This study proposes a mixed feature attention-artificial neural network (MFA-ANN) model for high-precision online prediction of product weight.<n>Results demonstrate that the MFA-ANN model achieves a RMSE of 0.0281 with 0.5 g weight fluctuation tolerance, outperforming conventional benchmarks.
• Score: 3.5881814709064934
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To address the challenges of untimely detection and online monitoring lag in injection molding quality anomalies, this study proposes a mixed feature attention-artificial neural network (MFA-ANN) model for high-precision online prediction of product weight. By integrating mechanism-based with data-driven analysis, the proposed architecture decouples time series data (e.g., melt flow dynamics, thermal profiles) from non-time series data (e.g., mold features, pressure settings), enabling hierarchical feature extraction. A self-attention mechanism is strategically embedded during cross-domain feature fusion to dynamically calibrate inter-modality feature weights, thereby emphasizing critical determinants of weight variability. The results demonstrate that the MFA-ANN model achieves a RMSE of 0.0281 with 0.5 g weight fluctuation tolerance, outperforming conventional benchmarks: a 25.1% accuracy improvement over non-time series ANN models, 23.0% over LSTM networks, 25.7% over SVR, and 15.6% over RF models, respectively. Ablation studies quantitatively validate the synergistic enhancement derived from the integration of mixed feature modeling (contributing 22.4%) and the attention mechanism (contributing 11.2%), significantly enhancing the model's adaptability to varying working conditions and its resistance to noise. Moreover, critical sensitivity analyses further reveal that data resolution significantly impacts prediction reliability, low-fidelity sensor inputs degrade performance by 23.8% RMSE compared to high-precision measurements. Overall, this study provides an efficient and reliable solution for the intelligent quality control of injection molding processes.

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