In dairy enterprises, predicting product quality attributes that are influenced by operating parameters is a major task. To reduce quality loss in production, a prediction-based quality control method is proposed in this study. In particular, a serial neural network was designed, and an innovative quality risk prediction methodology based on the integration of SNN and domain knowledge was created. The methodology involves three steps: (1) the processing steps at each unit operation are mapped to a layer of a back propagation network, (2) the branch networks are connected by key quality attributes, and (3) the model is trained with preprocessed data. The experiment was conducted based on milk powder production, demonstrating that the proposed methodology has a higher accuracy and shorter response time compared with those of existing methods. In addition, the practical value of the prediction methodology in actual dairy companies was discussed.

中文翻译：

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基于领域知识涉及的串行神经网络的奶粉生产厂质量风险预测方法


在乳品企业中，预测受操作参数影响的产品质量属性是一项主要任务。为了减少生产中的质量损失，本研究提出了一种基于预测的质量控制方法。特别是，设计了一个串行神经网络，并创建了一种基于 SNN 和领域知识集成的创新质量风险预测方法。该方法包括三个步骤：（1） 每个单元操作的处理步骤映射到反向传播网络的一层，（2） 分支网络通过关键质量属性连接，以及 （3） 使用预处理的数据训练模型。该实验基于奶粉生产进行，表明所提出的方法与现有方法相比具有更高的准确性和更短的响应时间。此外，还讨论了预测方法在实际乳制品公司中的实用价值。