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：由于热固性材料固化过程中热膨胀系数不匹配及基体收缩，纤维增强体与聚合物基体对制造条件的响应存在差异。这些非均匀性在多尺度上产生残余应力，其部分释放导致工艺诱导变形（PID），需通过优化的非等温固化周期进行精准预测与缓解。本研究采用单向AS4碳纤维/胺基双官能环氧预浸料，基于双机制框架（热膨胀/收缩与固化收缩）对PID进行建模。通过制造试验验证模型以确定初始条件和边界条件，进而生成不同非等温固化周期（时间-温度曲线）下的PID响应。基于此物理基础模型，我们开发了基于深度算子网络（DeepONets）的数据驱动代理模型。通过融合高保真仿真与针对性PID实验测量的数据集训练DeepONet，并将其扩展为特征线性调制（FiLM）DeepONet——通过外部参数（包括初始固化度）调制分支网络特征，从而预测固化度、粘度及形变的时间历程。鉴于实验数据仅在有限时间点（如最终形变）可用，我们采用迁移学习策略：固定经仿真训练的干支网络，仅用实测最终形变更新最终层。最后，结合集合卡尔曼反演（EKI）框架量化实验条件下的不确定性，为优化复合材料固化周期以降低PID提供支持。