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人工智能驱动的数字双胞胎用于自主网络紧张控制,用于滚动制造系统的滚动制造系统
Anton Nailevich Gafurov1,2, Sooyoung Lee3,4, Uzair Ali1,5
1Department of Advanced Battery Manufacturing Systems, Korea Institute of Machinery and Materials, 156 Gajeongbuk-ro, Yuseong-gu, Daejeon, 34103, Republic of Korea.
Scientific reports
|July 6, 2025
概括
这项研究介绍了一种AI数字双胞胎,用于滚滚制造中的自主网络张力控制. 优化的策略显著减少了张力变化,提高了工业应用的系统稳定性.
科学领域:
- 制造业 工程 制造工程
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 卷对卷制造使高吞吐量生产成为可能,但在精确的网状张力控制方面面临挑战.
- 网状材料和滚筒机械之间的动态相互作用使得实现稳定的张力变得复杂.
- 现有的控制方法难以应对实时卷对卷系统的复杂性.
研究的目的:
- 开发一个人工智能驱动的数字双胞胎框架,以优化自主网络张力控制.
- 为了应对在动态卷对卷流程中保持精确的网张的关键挑战.
- 通过先进的控制来提高卷对卷制造系统的效率和稳定性.
主要方法:
- 贝叶斯优化与高斯过程建模的集成,用于控制参数调整.
- 开发一个人工智能驱动的数字双胞胎,用于实时系统监控和响应改进.
- 使用客户端-服务器通信来管理滚动式系统操作.
主要成果:
- 通过实验验证证明,网络张力变化的显著减少.
- 通过AI优化的控制策略实现了系统稳定性的提高.
- 在实际的卷对卷制造系统上进行的验证证实了拟议方法的有效性.
结论:
- 与人工智能集成的数字双胞胎为自主制造提供了可扩展的解决方案.
- 拟议的框架有效地优化了卷对卷系统中的网状张力控制.
- 这种方法在需要精确控制的各种工业应用中具有广泛的潜力.
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