Electrically-assisted double-sided incremental forming (EA-DSIF) is a flexible forming method suitable for processing hard-to-form materials and complex-shaped parts. A challenge in EA-DSIF experiments is temperature measurement. Since the localized forming zone is blocked by the tools, it is not possible to measure the actual forming temperature distribution in the forming zone. To address this issue, we propose an artificial neural network (ANN) framework for predicting the forming temperature using measurements of the surrounding temperature and toolpath features. The ANN model was trained using the temperature outputs of finite element models. A simplified EA-DSIF simulation model was developed for computational efficiency needed for synthetic data generation. Model simplifications were justified in multiple cases and validated with experimental data by comparing the temperatures from positions that is visible to an infrared camera. The feasibility of applying the developed ANN model to untrained geometries and in practical applications was demonstrated. The findings generated from this study are crucial for selecting optimum process parameters, estimating the forming force, and predicting microstructure evolution during EA-DSIF.

电辅助双面增量成型（EA-DSIF）是一种灵活的成型方法，适用于加工难成型材料和形状复杂的零件。EA-DSIF 实验中的一个挑战是温度测量。由于局部成形区被工具挡住，因此无法测量成形区内的实际成形温度分布。为了解决这个问题，我们提出了一种人工神经网络 (ANN) 框架，用于使用周围温度和刀具路径特征的测量来预测成形温度。ANN 模型使用有限元模型的温度输出进行训练。开发了一个简化的 EA-DSIF 仿真模型，以提高合成数据生成所需的计算效率。模型简化在多种情况下都是合理的，并通过比较红外摄像机可见位置的温度来验证实验数据。证明了将开发的 ANN 模型应用于未经训练的几何形状和实际应用的可行性。这项研究的结果对于选择最佳工艺参数、估计成形力和预测 EA-DSIF 期间的微观结构演变至关重要。