Fiber reinforcement and polymer matrix respond differently to manufacturing conditions due to mismatch in coefficient of thermal expansion and matrix shrinkage during curing of thermosets. These heterogeneities generate residual stresses over multiple length scales, whose partial release leads to process-induced deformation (PID), requiring accurate prediction and mitigation via optimized non-isothermal cure cycles. This study considers a unidirectional AS4 carbon fiber/amine bi-functional epoxy prepreg and models PID using a two-mechanism framework that accounts for thermal expansion/shrinkage and cure shrinkage. The model is validated against manufacturing trials to identify initial and boundary conditions, then used to generate PID responses for a diverse set of non-isothermal cure cycles (time-temperature profiles). Building on this physics-based foundation, we develop a data-driven surrogate based on Deep Operator Networks (DeepONets). A DeepONet is trained on a dataset combining high-fidelity simulations with targeted experimental measurements of PID. We extend this to a Feature-wise Linear Modulation (FiLM) DeepONet, where branch-network features are modulated by external parameters, including the initial degree of cure, enabling prediction of time histories of degree of cure, viscosity, and deformation. Because experimental data are available only at limited time instances (for example, final deformation), we use transfer learning: simulation-trained trunk and branch networks are fixed and only the final layer is updated using measured final deformation. Finally, we augment the framework with Ensemble Kalman Inversion (EKI) to quantify uncertainty under experimental conditions and to support optimization of cure schedules for reduced PID in composites.

翻译：由于热膨胀系数失配以及热固性树脂固化过程中的基体收缩，纤维增强体与聚合物基体对制造条件的响应存在差异。这些异质性在多个尺度上产生残余应力，其部分释放导致工艺诱导变形，需要通过优化的非等温固化工艺曲线进行精确预测与调控。本研究以单向AS4碳纤维/胺基双官能环氧树脂预浸料为对象，采用包含热膨胀/收缩与固化收缩的双机制框架对工艺诱导变形进行建模。通过制造试验验证模型以确定初始与边界条件，进而生成多样化非等温固化工艺曲线（时间-温度分布）下的变形响应。在此物理模型基础上，我们开发了基于深度算子网络的数据驱动代理模型。该网络通过结合高保真仿真与定向实验测量的变形数据集进行训练。我们进一步拓展至特征线性调制深度算子网络架构，其中分支网络特征受初始固化度等外部参数调制，可实现固化度、粘度和变形时程的预测。鉴于实验数据仅能在有限时间点获取（如最终变形量），我们采用迁移学习策略：固定仿真训练的干线网络与分支网络，仅通过实测最终变形数据更新末层网络参数。最后，我们集成集合卡尔曼反演框架以量化实验条件下的不确定性，并为优化降低复合材料工艺诱导变形的固化工艺方案提供支持。