一个连续的宏观过程的实时自主控制,如塑料成型所示
Shun Muroga1, Takashi Honda2, Yasuaki Miki1
1Nano Carbon Device Research Center, National Institute of Advanced Industrial Science and Technology, Tsukuba Central 5, 1-1-1, Higashi, Tsukuba, Ibaraki, 305-8565, Japan. muroga-sh@aist.go.jp.
Materials horizons
|November 7, 2024
概括
一个新的自主系统使用实时数据和主动学习来控制塑料薄膜的形成. 这种自动化过程有效地确定了特定薄膜尺寸的最佳条件,加速了制造和研究.
科学领域:
- 材料科学 材料科学 材料科学
- 工程 工程师 工程师 工程师
- 自动化 自动化 自动化
背景情况:
- 当前的研究和制造业需要更具适应性和高效的工艺.
- 连续的材料加工往往涉及复杂的,相互依存的控制因素.
研究的目的:
- 开发和演示用于材料加工的连续自主系统.
- 为了自主确定特定塑料薄膜尺寸的工艺条件.
主要方法:
- 该系统集成实时,现场表征与基于主动学习的决策处理器.
- 应用于塑料薄膜成型工艺,它自主调整控制因素 (材料供应速率,施加力,粘度).
主要成果:
- 成功确定了九个不同的薄膜尺寸的稳定工艺条件,准确度高 (宽度R2=0.87,厚度R2=0.90).
- 每个维度平均实现了11次表征调整代和19分钟的处理时间.
- 积极学习算法允许在没有初始训练数据的情况下进行探索,避免了常见的控制陷.
结论:
- 开发的自主系统有效地控制了连续塑料薄膜的形成,而无需人类干预.
- 该系统的可适应性设计对加速研究和工业材料加工进步具有广泛的影响.
- 核心概念可转移到其他连续材料加工系统.
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