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基于深度学习的造过程的传热模拟
Jinwu Kang1, Jiwu Wang2, Xiao Han2
1School of Materials Science and Engineering, Key Laboratory for Advanced Materials Processing Technology, Tsinghua University, Beijing, 100084, China. kangjw@tsinghua.edu.cn.
Scientific reports
|November 23, 2024
概括
深度学习模型快速预测造的固化温度,达到94.5%的准确度. 这样可以避免对复杂的造工艺进行漫长的模拟.
科学领域:
- 材料科学 材料科学 材料科学
- 计算科学 计算科学
- 人工智能的人工智能
背景情况:
- 造固化的数值模拟是计算密集且耗时的.
- 现有的方法往往需要复杂的宪法模型.
- 准确预测温度场对于优化造工艺至关重要.
研究的目的:
- 开发一种快速而准确的方法,用于在造固化过程中预测温度场.
- 为了克服传统数值模拟技术的局限性.
- 利用深度学习进行有效的造热分析.
主要方法:
- 开发了经过修改的U-net网络架构,其中包括Inception和Convolutional Block Attention Module (CBAM) 模块.
- 从200个不同的几何模型中生成训练数据,其中包括造,模具和冷却部件.
- 使用有限差异方法 (FDM) 模拟来获得训练数据的温度场.
- 训练有素的深度学习模型可以从时间ti+1的输入中预测时间ti+1的温度场.
主要成果:
- 实现了94.5%的平均预测准确度,绝对温度误差为7°C.
- 演示了快速预测能力,每次时间步骤预测只需要一秒.
- 成功处理复杂的多组件和多材料几何形状,包括造,冷却和模具.
- 模型表现出在预测不同时间点任意形状件的温度场的熟练程度.
结论:
- 深度学习模型为造固化的传统数值模拟提供了一个快速而准确的替代方案.
- 拟议的U-net架构与Inception和CBAM模块有效预测复杂的造场景中的温度场.
- 这种方法显著降低了计算成本和时间,使造过程的优化速度更快.
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