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

TL;DR

This paper introduces a novel neural network model that combines time series and non-time series features with an attention mechanism for precise, real-time prediction of injection molding product weight, improving quality control.

Contribution

It proposes a mixed feature attention neural network that decouples and hierarchically processes diverse data types, enhancing prediction accuracy and robustness in injection molding quality monitoring.

Findings

Achieves RMSE of 0.0281, surpassing benchmarks by over 23%.

Integrating mixed features and attention improves model adaptability and noise resistance.

Data resolution critically affects prediction reliability, with low-fidelity sensors degrading performance.

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.