Abstract
Accurate and efficient temperature prediction is critical for optimizing the preheating process of PET preforms in industrial microwave systems prior to blow molding. We propose a novel deep learning framework for generalized temperature prediction. Unlike traditional models that require extensive retraining for each material or design variation, our method introduces a data-efficient neural architecture that leverages transfer learning and model fusion to generalize across unseen scenarios. By pretraining specialized neural regressor on distinct conditions such as recycled PET heat capacities or varying preform geometries and integrating their representations into a unified global model, we create a system capable of learning shared thermal dynamics across heterogeneous inputs. The architecture incorporates skip connections to enhance stability and prediction accuracy. Our approach reduces the need for large simulation datasets while achieving superior performance compared to models trained from scratch. Experimental validation on two case studies material variability and geometric diversity demonstrates significant improvements in generalization, establishing a scalable ML-based solution for intelligent thermal control in manufacturing environments. Moreover, the approach highlights how data-efficient generalization strategies can extend to other industrial applications involving complex physical modeling with limited data.
Abstract (translated)
准确且高效的温度预测对于在工业微波系统中优化聚对苯二甲酸乙二醇酯（PET）预成型品吹塑前的预热过程至关重要。我们提出了一种新的深度学习框架，用于通用温度预测。与传统的需要针对每种材料或设计变化进行大量重新训练的模型不同，我们的方法引入了一个数据效率高的神经架构，利用迁移学习和模型融合来适应未见过的情景。通过在不同的条件下对专业化的神经回归器进行预训练（例如回收PET的热容量或不同的预成型几何形状），并将它们的表现形式整合到一个统一的整体模型中，我们创建了一种能够在不同输入情况下学习共享热动力学的系统。该架构包括跳过连接以增强稳定性和预测准确性。 我们的方法在减少大型模拟数据集需求的同时，实现了比从头开始训练的模型更优的性能。通过对两种案例研究（材料变化和几何多样性）进行实验验证，结果表明，在泛化能力方面有显著改进，从而为制造环境中的智能热控制提供了一种可扩展的基于机器学习的解决方案。此外，该方法展示了如何将数据效率高的通用策略应用于其他涉及复杂物理建模且数据有限的工业应用中。
URL
https://arxiv.org/abs/2510.05394
