通过不确定性量化和强大的前端模拟,改善制造过程中的多目标决策.
Arne De Temmerman1,2, Mathias Verbeke3,4
1M-Group, Department of Computer Science, KU Leuven, Leuven, Belgium. arne.detemmerman@kuleuven.be.
Scientific reports
|April 25, 2025
概括
本研究介绍了用于制造业优化的概率图形模型,整合专家知识和数据来管理不确定性. 纳入随机的不确定性增强了决策,并为复杂的过程创造了更可靠的帕雷托前线.
科学领域:
- 工程 工程师 工程师 工程师
- 制造业科学 制造业科学
- 数据科学数据科学数据科学
背景情况:
- 制造过程具有复杂的输入-输出关系,具有挑战性的优化.
- 替代模型接近这些关系,但与不确定性作斗争.
- 制造业的不确定性可能会导致不理想的决策和不准确的预测.
研究的目的:
- 探索可能的图形模型来表示制造过程.
- 将专家知识与未知关系的数据驱动近似结合起来.
- 为了研究 aleatoric 不确定性对多目标优化的影响.
主要方法:
- 利用概率图形模型来表示制造过程.
- 综合专家知识与数据驱动的代孕模型.
- 适用于连续制造案例研究的方法.
- 采用概率替代抽样来生成设定值.
主要成果:
- 在非线性过程函数下展示了增强的帕雷托前线创建.
- 通过结合随机的不确定性,表现出更好的决策.
- 在连续制造案例中产生了更为保守的设定点.
- 验证了概率替代模型的有效性.
结论:
- 在替代模型中纳入 aleatoric 不确定性,可以提高制造业的优化.
- 概率图形模型为在不确定性下做出决策提供了一个强大的框架.
- 这种方法导致更可靠,更强大的制造优化策略.
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