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一个人工神经网络用于快速预测船舶船体板线加热形成中的3D短暂温度场
Zhe Yang1, Hua Yuan1,2,3, Zhenshuai Wei1
1School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Materials (Basel, Switzerland)
|November 13, 2025
概括
这项研究引入了用于线路加热的快速,准确的神经网络,显著优于传统模拟. 这种新方法可以快速优化制造业中先进的智能线路加热操作的参数.
科学领域:
- 材料科学与工程 材料科学与工程
- 计算科学与工程 计算科学与工程
背景情况:
- 线路加热对于制造造造船,航空航天和汽车行业的钢制部件至关重要.
- 传统的有限元法 (FEM) 模拟提供了高准确度,但由于计算时间长且需要重新组网而受到损害.
研究的目的:
- 开发一个物理意识,数据驱动的神经网络,用于在线加热过程中快速,高准确度的温度预测.
- 为了克服线路加热应用中传统FEM模拟的计算局限性.
主要方法:
- 一个轻量级的多层感知器 (MLP) 被训练在FEM生成的钢板的感应加热数据上.
- 输入特征包括热物理性质,边界条件,热源变量和时间,所有这些都是正常化的和非维化的.
- 几何特征在路径对齐的局部坐标框架中表示.
主要成果:
- 神经网络在持久测试套件上实现了MAE = 0.60 °C,RMSE = 1.27 °C和R2 = 0.995的高精度.
- 在一个集成加热和机械滚动成型 (IHMRF) 平台上的实验验证显示与热电偶测量有很强的一致性.
- 推理速度大约是FEM的10^5倍,使近乎实时的预测成为可能.
结论:
- 物理意识的神经网络为线路加热模拟提供了计算效率高,准确的FEM替代方案.
- 这种方法促进了快速的参数优化,并支持智能线路加热操作的进步.
- 该模型展示了对未见的板块大小和配置的概括能力.
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