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基于注意力增强的残余卷积神经网络的热源参数识别.

Hao Jiang1, Xinyu Liu1, Zhenfei Guo2

  • 1College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China.

Materials (Basel, Switzerland)
|September 13, 2025
PubMed
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准确的热源参数识别对于可靠的接模拟至关重要. 本研究介绍了HSPINet,这是一种新的深度学习模型,用于高效和精确地识别这些关键接参数.

科学领域:

  • 材料科学与工程 材料科学与工程
  • 计算力学 计算力学 计算力学
  • 人工智能在制造业中的应用

背景情况:

  • 接热分析的准确性在很大程度上取决于热源参数.
  • 不准确的参数损害了温度分布,扭曲和残余应力的预测.
  • 现有的识别方法与复杂的工业环境作斗争.

研究的目的:

  • 开发一种智能模型,用于准确识别热源参数.
  • 为了提高安全关键结构的接热模拟的可靠性.
  • 为复杂的工业接应用提供一个可适应的工具.

主要方法:

  • 提出了热源参数识别网络 (HSPINet) 模型.
  • 使用了残余卷积神经网络 (ResNet) 架构.
  • 整合了一个注意力机制,用于从T关节接形态中提取特征.

主要成果:

  • HSPINet能够高效准确地识别热源的参数.
  • 该模型有效地提取了关键特征,考虑了过程参数和关节尺寸.
  • 在热源参数识别方面表现出更好的智能.

结论:

关键词:
热源参数 热源参数参数反转的参数反转.剩余卷积神经网络的神经网络.接模拟 接模拟

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  • HSPINet为接模拟提供了一个实用,智能化的解决方案.
  • 该模型提高了工业环境中热场评估的准确性.
  • 显示显著的理论价值和在激光处理和制造中的广泛应用.