Inspired by sequential additive manufacturing operations, we consider prediction tasks arising in processes that comprise of sequential sub-operations and propose a multi-stage inference procedure that exploits prior knowledge of the operational sequence. Our approach decomposes a data-driven model into several easier problems each corresponding to a sub-operation and then introduces a Bayesian inference procedure to quantify and propagate uncertainty across operational stages. We also complement our model with an approach to incorporate physical knowledge of the output of a sub-operation which is often more practical in reality relative to understanding the physics of the entire process. Comprehensive simulations and two case studies on additive manufacturing show that the proposed framework provides well-quantified uncertainties and superior predictive accuracy compared to a single-stage predictive approach. Note to Practitioners—This paper is motivated by sequential operations that often occur in manufacturing processes. For example, several additive manufacturing processes consist of multiple sequential steps, e.g., printing, washing, and curing in stereolithography, or printing, debinding, and sintering in binder jetting. In such settings, a complex data-driven model that blindly throws all given data into a single predictive model might not be optimal. To this end, we propose a multi-stage inference procedure that decomposes the problem into easier sub-problem using the prior knowledge of the operational sequence, and propagates uncertainty across stages using Bayesian neural networks. Here we note that even if sequential operations are not existent in reality, one may conceptually decompose a complex system into simpler pieces and exploit our procedure. Also, our approach is able to incorporate physical knowledge of the output of a sub-operation.

中文翻译：

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应用于增材制造的知识引导预测的多阶段方法


受顺序增材制造操作的启发，我们考虑由顺序子操作组成的过程中出现的预测任务，并提出一种利用操作序列先验知识的多阶段推理程序。我们的方法将数据驱动模型分解为几个更简单的问题，每个问题对应一个子操作，然后引入贝叶斯推理过程来量化和传播跨操作阶段的不确定性。我们还通过结合子操作输出的物理知识的方法来补充我们的模型，这在现实中通常比理解整个过程的物理知识更实用。关于增材制造的全面模拟和两个案例研究表明，与单阶段预测方法相比，所提出的框架提供了充分量化的不确定性和卓越的预测准确性。从业者注意事项——本文的动机是制造过程中经常发生的顺序操作。例如，一些增材制造工艺由多个连续步骤组成，例如立体光刻中的印刷、清洗和固化，或粘合剂喷射中的印刷、脱脂和烧结。在这种情况下，盲目地将所有给定数据放入单个预测模型的复杂数据驱动模型可能不是最佳的。为此，我们提出了一种多阶段推理过程，该过程使用操作序列的先验知识将问题分解为更简单的子问题，并使用贝叶斯神经网络跨阶段传播不确定性。在这里我们注意到，即使现实中不存在顺序操作，人们也可以在概念上将复杂的系统分解为更简单的部分并利用我们的过程。 此外，我们的方法能够结合子操作输出的物理知识。