聚合物注塑成型产品质量的动态监测方法基于操作条件漂移检测和增量学习
Guancheng Shen1,2, Sihong Li2, Yun Zhang2
1Xi'an Modern Chemistry Research Institute, Xi'an 710065, China.
Polymers
|November 27, 2025
概括
这项研究引入了一个漂移意识框架,用于预测聚合物注塑成型质量. 它通过检测和适应不断变化的操作条件来提高准确性,提高工艺稳定性.
科学领域:
- 制造业 工程 制造工程
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
背景情况:
- 聚合物注塑成型质量预测模型随着操作条件的变化而恶化.
- 化温度,冷却和机器参数的变化导致模型漂移.
研究的目的:
- 开发一个漂移意识的动态质量监测框架,以实现强大的在线预测.
- 在不断变化的工业环境中,应对预测模型退化的挑战.
主要方法:
- 集成混合功能自动编码器 (HFAE) 用于漂移检测.
- 使用了滑动窗口重建错误分析.
- 使用混合功能的人工神经网络 (ANN) 进行在线质量预测.
- 实施了一个适应性方案,包括漂移事件响应和模型更新.
主要成果:
- 与静态模型相比,整体精度提高了35.4%.
- 在增量更新后,平均平方根误差减少了42.3%.
- 异常检测率显著下降,从0.86降至0.09.
- 有效地缩小了培训和测试套件之间的分布差距.
结论:
- 拟议的框架在动态条件下保持高准确度的质量预测.
- 能够减少再加工,提高工艺稳定性,降低工业生产中的采样频率.
- 证明了对大规模聚合物注塑成型的实际相关性.
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