基于参数转移策略的热板宽度的滚动理论引导的预测
Zishuo Dong1, Xu Li1, Feng Luan2
1The State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, People's Republic of China.
ISA transactions
|January 26, 2024
概括
本研究介绍了一种理论引导的机器学习框架,通过整合领域知识来改善热的预测. 这种新的方法确保了物理一致性,并提高了工业应用中机器学习模型的可靠性.
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:
- 机器学习模型经常产生与既定理论知识相矛盾的预测,限制了它们的实际应用.
- 将领域知识与数据驱动方法相结合,对于开发复杂工业过程中可靠的预测模型至关重要.
研究的目的:
- 提出理论引导的机器学习框架,将领域知识与数据驱动模型相结合.
- 应用这个框架来预测在经过多次通过后在热时的板宽.
- 确保机器学习模型的预测在工业用途上是物理一致和可靠的.
主要方法:
- 开发了一个新的框架,将参数传输策略与深度神经网络 (DNN) 集成在一起.
- 一个集群优化算法与理论模型一起用于生成物理一致的训练数据.
- 在模拟数据上训练DNN模型,并与实际生产数据进行微调.
主要成果:
- 拟议的理论引导机器学习模型与其他评估的模型相比,显示出优异的预测性能.
- 开发的模型的预测与滚动理论的既定原则一致.
- 该框架有助于在制造环境中快速,可靠地部署机器学习.
结论:
- 理论引导的机器学习框架有效地解决了将理论知识纳入数据驱动模型的挑战.
- 这种方法提高了机器学习对热等工艺的准确性,可靠性和工业适用性.
- 理论约束的成功整合导致了工程领域更可靠,更实用的AI解决方案.
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