Abstract: In complex process manufacturing, high process coupling, intricate multi-step coordination, and significant nonlinear relationships between product quality and process parameters pose challenges for quality control. To address these issues, this study proposes a segmented multi-process quality prediction method integrating multi-layer neural networks and ensemble learning. The approach first establishes an overall prediction model and segmented prediction models. The overall model employs Random Forest (RF), LightGBM, and KNN algorithms, overcoming the limitations of single-model generalization through ensemble learning strategies while leveraging multi-algorithm differences to extract multidimensional data features. The segmented model utilizes LSTM-KAN networks, where Long Short-Term Memory (LSTM) captures long-term dependencies between process quality and feature variables, while Kolmogorov-Arnold Networks (KAN) enhance nonlinear mapping capabilities. Subsequently, the XGBoost ensemble learning algorithm integrates both models to achieve complementary advantages. Finally, a case study of predicting the moisture content of materials at the exit of the tobacco dryer in tobacco production is conducted for verification. As a core quality characterization indicator in tobacco primary processing, the stability of the exit material moisture content is directly related to the material softening effect of loose conditioning, the liquid absorption efficiency of leaf moistening and feeding, and the drying uniformity of thin-plate tobacco drying. Comprehensive control of the multi-process quality can be achieved through the accurate prediction of this single indicator. The results show that the fusion model is significantly superior to traditional single models and comparative models in key indicators such as mean absolute error (MAE=0.0072), root mean square error (RMSE=0.0096), mean absolute percentage error (MAPE=0.0566%), and goodness of fit (R²=0.9890). This verifies the effectiveness of the proposed method in handling nonlinear relationships and time-series characteristics, as well as its advantages in prediction accuracy and generalization performance, making it suitable for complex multi-process scenarios in tobacco primary processing.

摘要： 在复杂流程制造生产中，工艺耦合度高、多工序联动复杂，且产品质量与工艺参数间存在显著的强非线性关系，这给工艺质量控制带来了挑战。为此，本研究提出了一种结合多层神经网络与集成学习的分段式多工序工艺质量预测方法。该方法首先构建了整体预测模型和分段预测模型。整体模型采用随机森林（RF）、LightGBM和KNN算法，通过集成学习策略克服了单一模型泛化能力不足的缺陷，并利用多算法间的差异挖掘数据的多维度特征。分段模型则采用LSTM-KAN网络，利用长短期记忆网络（LSTM）捕捉各工序质量与特征变量的长时序依赖关系，并借助Kolmogorov-Arnold网络（KAN）增强非线性映射能力。接着，通过XGBoost集成学习算法将两种模型融合，以实现优势互补。最后，以烟丝生产中烘丝机出口物料含水率预测为例进行验证。出口物料含水率作为烟草制丝生产的核心质量表征指标，其稳定性直接关联松散回潮的物料软化效果、润叶加料的料液吸收效率及薄板烘丝的干燥均匀度，可通过该单一指标的精准预测实现对多工序工艺质量的综合管控。结果表明，融合模型在平均绝对误差（MAE=0.0072）、均方根误差（RMSE=0.0096）、平均绝对百分比误差（MAPE=0.0566%）及拟合优度（R²=0.9890）等关键指标上均显著优于传统单模型及对比模型，验证了该方法在处理非线性关系和时序特征方面的有效性，以及在预测精度和泛化性能上的优越性，使其适用于烟草制丝多工序复杂生产场景。