在注射成型中使用基于LSTM的时间序列模型监测聚合物融稳定性
Pedro Costa1, Sílvio Priem Mendes1,2, Paulo Loureiro1,2
1School of Technology and Management, Polytechnic University of Leiria, 2411-901 Leiria, Portugal.
Polymers
|January 10, 2026
概括
本研究引入了使用长短期记忆 (LSTM) 模型的数据驱动框架,用于在注塑中早期检测聚合物融不稳定性. 该系统在缺陷出现之前就能识别工艺漂移,从而能够主动调整并减少废弃物.
科学领域:
- 材料科学 材料科学 材料科学
- 制造业 工程 制造工程
- 数据科学数据科学数据科学
背景情况:
- 聚合物融不稳定性在注塑成型中往往逐渐发展,使得传统的监测方法不足以进行早期检测.
- 现有的基于值的系统难以捕捉微妙的转变,导致质量退化和废弃率增加.
- 实时监控工艺参数对于保持热塑制造中的稳定性和产品质量至关重要.
研究的目的:
- 开发一个数据驱动的框架,用于在工业注塑中早期检测聚合物融不稳定性.
- 利用长期短期记忆 (LSTM) 时间序列模型来识别质量问题之前的逐渐漂移条件.
- 通过在缺陷出现前几分钟检测不稳定性来实现主动的工艺调整和减少废弃物.
主要方法:
- 使用了六个月的连续生产数据 (约. 28万个周期) 来自热塑性注射线.
- 应用长期短期记忆 (LSTM) 时间序列模型来分析扭矩,压力,温度和风湿学数据中的时间模式.
- 实施了基于物理信息的标签策略,使用不合格部分之前的挥发性区域进行监督学习,缺陷注释稀少.
主要成果:
- 该框架成功建模了时间模式,以确定暗示融不稳定的漂移条件.
- 基于物理信息的标签策略使有效的监督学习成为可能,在实际缺陷发生前几分钟检测不稳定窗口.
- 该系统展示了使用标准机器信号而不需要额外的传感器来识别不稳定的能力.
结论:
- 数据驱动的时间深度学习模型,特别是LSTM,提供了一种强大的方法来增强注射成型中的实时监控.
- 拟议的框架促进了积极的过程调整,从而提高了稳定性和减少了废弃物.
- 这种方法通过先进的不稳定性检测,有助于更强大的和适应性的制造业务.
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