Accurate temperature prediction is essential for optimizing the microwave preheating of PET preforms prior to blow molding. A key challenge in this context is the strong dependence of electromagnetic field distributions and thermal responses on preform geometry, which varies substantially across product lines. Conventional neural network models trained on specific geometric configurations typically fail to generalize to unseen preform designs, requiring costly retraining for each new geometry. This work proposes a unified geometry-aware deep learning framework that predicts spatial temperature distributions across multiple preform designs using a single neural network model. The approach reformulates temperature prediction as a coordinate-level regression task conditioned on spatial location, geometric descriptors, process parameters, and structural region labels. A domain-bounded training strategy based on extreme feasible preform geometries is introduced, ensuring that predictions for intermediate designs remain within the interpolation regime of the network. The framework is evaluated on six distinct preform geometries, demonstrating that a single model can generalize reliably to new, unseen preform designs when their geometric parameters fall within the bounds of the training data. This is achieved through a domain-bounded training strategy that constructs datasets from the extreme feasible geometries, thereby converting the prediction of any intermediate design into an interpolation task. Since neural networks are inherently limited in their ability to extrapolate beyond the training domain, this formulation is essential for ensuring stable and accurate predictions across the full range of industrially relevant preform configurations. The proposed methodology provides a foundation for geometry-informed surrogate modeling in thermal process control and can be extended to other manufacturing systems characterized by strong geometric variability.
Alsheikh et al. (Sun,) studied this question.