A Multi-Stage Approach for Knowledge-Guided Predictions With Application to Additive Manufacturing
A Multi-Stage Approach for Knowledge-Guided Predictions With Application to Additive Manufacturing
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DOI：
10.1109/tase.2022.3160420
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发表时间：
2022-07
影响因子：
5.6
通讯作者：
Seokhyun Chung;Cheng-Hao Chou;Xiaozhu Fang;Raed Al Kontar;C. Okwudire
中科院分区：
文献类型：
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作者：
Seokhyun Chung;Cheng-Hao Chou;Xiaozhu Fang;Raed Al Kontar;C. Okwudire
受顺序加法制造操作的启发，我们考虑了由顺序子操作组成的过程中产生的预测任务，并提出了一种利用操作序列的先验知识的多阶段推理过程。我们的方法将数据驱动的模型分解成几个更简单的问题，每个问题对应于一个子操作，然后引入贝叶斯推理过程来量化和传播操作阶段的不确定性。我们还用一种方法来补充我们的模型，该方法并入对子操作输出的物理知识，这在现实中通常比理解整个过程的物理更实际。综合仿真和两个关于加法制造的案例研究表明，与单阶段预测方法相比，所提出的框架提供了量化的不确定性和更高的预测精度。从业人员请注意--这篇文章的动机是制造过程中经常发生的顺序操作。例如，几种添加剂制造工艺由多个连续步骤组成，例如，在立体平版印刷中的印刷、洗涤和固化，或在粘结剂喷射中的印刷、脱脂和烧结。在这种情况下，盲目地将所有给定数据放入单一预测模型的复杂数据驱动模型可能并不是最优的。为此，我们提出了一个多阶段推理过程，利用操作序列的先验知识将问题分解为更容易的子问题，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，即使在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成更简单的部分，并利用我们的过程。此外，我们的方法能够并入对子操作输出的物理知识。
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.