工业注塑工艺的单一和多目标实时优化,通过贝叶斯的实验方法的适应性设计来实现
Mandana Kariminejad1,2,3, David Tormey1,3, Caitríona Ryan3,4,5
1Centre for Precision Engineering, Materials and Manufacturing Research (PEM Centre), Atlantic Technological University Sligo, Ash Lane, Sligo, F91 YW50, Ireland.
Scientific reports
|November 30, 2024
概括
贝叶斯适应性实验设计 (ADoE) 可有效优化注塑成型质量和循环时间. 这种新的方法显著减少了实时流程优化所需的实验,优于传统方法.
科学领域:
- 制造业 工程 制造工程
- 材料科学 材料科学 材料科学
- 过程优化 过程优化
背景情况:
- 注塑成型 (IM) 在尽量减少循环时间而不损害质量方面面临挑战.
- 传统的实验设计 (DoE) 方法需要广泛的实验,并且在搜索空间上有局限性.
- 贝叶斯适应性实验设计 (ADoE) 提供了一种代方法,通过从以前的实验结果中学习来优化流程.
研究的目的:
- 开发和评估实验贝叶斯适应性实验设计 (ADoE) 方法,用于实时注塑成型优化.
- 引入一种使用模具内传感器数据实时表征生产后收缩的新方法.
- 将ADoE方法的效率与传统的多目标优化技术进行比较.
主要方法:
- 开发了一种实验性的ADoE方法,利用贝叶斯式优化用于注塑.
- 通过模具内温度差传感器数据实现了生产后收缩的实时表征.
- 将ADoE与复合可取性函数和NSGA-II (使用RSM) 进行比较,以实现单个和多个目标的优化.
主要成果:
- 对于温度差异的单一目标优化,ADoE的实验减少了约50%.
- 与其他方法相比,ADoE减少了近30%的实验,以共同优化[公式:参见文本]和循环时间.
- 对于多目标优化,ADoE的最佳设置与NSGA-II的帕雷托最佳解决方案非常相匹配.
结论:
- 开发的贝叶斯式ADoE方法对于实时注塑成型优化非常有效.
- 这种方法通过减少所需实验的数量,显著提高了效率.
- ADoE为优化诸如注塑等复杂制造工艺提供了强大的,高效的替代方案.
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