用图神经网络加速冷锻壁厚预测，实现生产闭环应用。
Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach
- 设计图神经网络融合工艺信息，构建冷锻过程的替代模型。
- 新指标ABTC验证显示预测精度高，响应速度快于传统模拟。
- 适合制造业实时质量控制与智能产线集成场景。
本研究提出一种新型方法，用于预测冷锻工艺中管材在镦粗过程中的壁厚变化。首先系统分析了镦粗工艺及其影响参数，并建立有限元法（FEM）仿真以深入研究工艺参数的影响。然而传统FEM虽准确但计算耗时，难以满足实时应用需求。为此，本文提出基于定制图神经网络的代理模型框架，通过引入不同类型的边及对应编码器，直接嵌入镦粗工艺信息，以建模对象间相互作用。该改进显著提升模型精度，为在闭环生产过程中部署高精度代理模型提供可能。采用新评估指标面积间厚度曲线（ABTC）进行测试，结果表明该方法表现优异，凸显神经网络作为代理模型在预测镦粗过程壁厚变化方面的潜力。
原文摘要 · Abstract (English)
This study presents a novel approach for predicting wall thickness changes in tubes during the nosing process. Specifically, we first provide a thorough analysis of nosing processes and the influencing parameters. We further set-up a Finite Element Method (FEM) simulation to better analyse the effects of varying process parameters. As however traditional FEM simulations, while accurate, are time-consuming and computationally intensive, which renders them inapplicable for real-time application, we present a novel modeling framework based on specifically designed graph neural networks as surrogate models. To this end, we extend the neural network architecture by directly incorporating information about the nosing process by adding different types of edges and their corresponding encoders to model object interactions. This augmentation enhances model accuracy and opens the possibility for employing precise surrogate models within closed-loop production processes. The proposed approach is evaluated using a new evaluation metric termed area between thickness curves (ABTC). The results demonstrate promising performance and highlight the potential of neural networks as surrogate models in predicting wall thickness changes during nosing forging processes.
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