Abstract:

Accurate prediction of Mooney viscosity is a key link in optimizing rubber mixing process, which helps to timely control rubber quality in rubber tire production process. To this end, a Mooney viscosity prediction model based on fusion Transformer (Fusformer) is proposed to conduct targeted modeling of time series variables and static covariates data generated by rubber mixing, extract and fuse various data features, and accurately predict Mooney viscosity. For time series variables, the model introduces the concept of directed graph, proposes relative position perception layer and relative multi-head attention mechanism to fully capture time series dependency features; for static covariates, the model introduces gated linear units and proposes static feature enrichment module to extract effective static features, and finally fuses time series dependency features with effective static features and outputs predicted values. The experiment uses 200000 actual samples from a rubber factory to test the prediction performance of the model, and demonstrates the rationality of the model design through comparative experiments, ablation analysis and parameter sensitivity analysis.

Key words: Mooney viscosity, rubber mixing, multivariate time series prediction, neural network, static covariates, optimal design, algorithm

摘要：

门尼系数的精准预测是优化橡胶混炼工艺的关键一环，有助于橡胶轮胎生产过程及时把控胶料质量。为此，提出了一种基于融合Transformer的门尼系数预测模型（fusion Transformer，Fusformer），对橡胶混炼产生的时间序列变量与静态协变量数据进行针对性建模，提取并融合各类数据特征，精确预测门尼系数。针对时间序列变量，模型引入有向图的概念，提出相对位置感知层与相对多头注意力机制，充分地捕捉时序依赖特征；针对静态协变量，模型引入门控线性单元并提出了静态特征富集模块，提取有效静态特征，最终将时序依赖特征与有效静态特征融合并输出预测值。实验利用大型橡胶轮胎企业的20万条实际采样样本测试了模型的预测性能，通过对照实验、消融分析以及参数灵敏度分析论证了模型设计的合理性。

关键词: 门尼系数, 橡胶混炼, 多变量时间序列预测, 神经网络, 静态协变量, 优化设计, 算法