Process-property relations are central to ICME. Engineers are often interested in using these relations to make decisions on process configurations to achieve desired properties. This is known as the inverse problem and is typically solved using forward models (physics-based or data-based) in an optimization loop, which can sometimes be expensive and error prone, especially when used on process chains with multiple unit steps. We propose a Bayesian networks-based approach for modeling process-property relations that can be used for inverse inference directly. The solutions thus found can serve as good starting points for a more detailed simulation-based search. We also discuss how unit process models can be composed to do inverse inference on the process chain as a whole. We demonstrate this in a wire-drawing process where a wire is drawn in multiple passes to achieve desired properties. We learn a Bayesian network for a unit pass and compose it multiple times to infer process parameters of all passes together.

中文翻译：

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制造过程链逆推断的贝叶斯框架

过程属性关系对于ICME至关重要。工程师通常对使用这些关系来决策过程配置以实现所需的特性感兴趣。这被称为反问题，通常在优化循环中使用正向模型（基于物理学或基于数据）来解决，这有时可能会很昂贵且容易出错，尤其是在具有多个单元步骤的过程链上使用时。我们提出了一种基于贝叶斯网络的方法来建模过程-属性关系，该方法可直接用于逆推论。因此找到的解决方案可以作为更详细的基于模拟的搜索的良好起点。我们还将讨论如何组成单元过程模型以对整个过程链进行逆向推理。我们在拉丝过程中对此进行了演示，在该过程中，多次拉伸导线以实现所需的性能。我们学习一个单位通过的贝叶斯网络，并对其进行多次组合以推断所有通路的过程参数。