融合时序与非时序特征，实现注塑产品重量的高精度在线预测。
Online high-precision prediction method for injection molding product weight by integrating time series/non-time series mixed features and feature attention mechanism
- 分路提取时序与非时序特征，通过注意力机制动态加权融合。
- 预测误差仅0.0281，在0.5克波动容忍下性能优于主流模型。
- 适合工业质检场景，尤其对传感器精度敏感的生产环境。
为解决注塑质量异常检测滞后与在线监控延迟问题，本文提出混合特征注意力-人工神经网络（MFA-ANN）模型，实现产品重量的高精度在线预测。该模型结合机理分析与数据驱动，将熔体流动动力学、温度分布等时序数据与模具结构、压力设定等非时序数据解耦，分层提取特征。在跨域特征融合阶段嵌入自注意力机制，动态调节多模态特征权重，突出影响重量波动的关键因素。实验表明，该模型在0.5克重量波动容忍条件下，均方根误差（RMSE）达0.0281，相较非时序ANN提升25.1%，较LSTM提升23.0%，较SVR提升25.7%，较随机森林提升15.6%。消融实验证明，混合特征建模贡献22.4%性能提升，注意力机制贡献11.2%。此外，敏感性分析显示，低精度传感器输入使误差上升23.8%。本研究为注塑过程智能质量控制提供高效可靠方案。
原文摘要 · Abstract (English)
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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