Constrained Markov Decision Process Modeling for Sequential Optimization of Additive Manufacturing Build Quality
Constrained Markov Decision Process Modeling for Sequential Optimization of Additive Manufacturing Build Quality
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DOI：
10.1109/access.2018.2872391
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发表时间：
2018-09
期刊：
影响因子：
3.9
通讯作者：
B. Yao;Hui Yang
中科院分区：
文献类型：
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作者：
B. Yao;Hui Yang
增材制造（AM）在直接根据设计生产具有复杂几何形状的三维零件方面提供了更高的灵活性。然而，AM的广泛应用目前因工艺重复性和质量控制方面的技术挑战而受阻。为了提高加工过程中的信息可见性，越来越多地投入先进的传感技术用于增材制造过程的实时监测。原位传感数据的大量增加，要求开发分析方法以提取对层状缺陷敏感的特征，并利用有关缺陷的相关知识对增材制造构建进行加工过程中的质量控制。因此，在过去几年中，用于表征和评估层状缺陷的基于传感器的模型受到越来越多的关注并得到快速发展。然而，从基于传感器的缺陷建模到为增材制造构建的质量控制提出原位纠正措施，这方面所做的工作还很少。在本文中，我们通过约束马尔可夫决策过程（CMDP）提出了一种用于增材制造过程原位控制的新的序贯决策框架，该框架同时考虑了总成本（即能量或时间）和构建质量这两个相互冲突的目标。实验结果表明，CMDP公式为在构建完成之前执行纠正措施以修复和抵消增材制造中的初期缺陷提供了一种有效的策略。
Additive manufacturing (AM) provides a greater level of flexibility to produce a 3-D part with complex geometries directly from the design. However, the widespread application of AM is currently hampered by technical challenges in process repeatability and quality control. To enhance the in-process information visibility, advanced sensing is increasingly invested for real-time AM process monitoring. The proliferation of in situ sensing data calls for the development of analytical methods for the extraction of features sensitive to layer-wise defects, and the exploitation of pertinent knowledge about defects for in-process quality control of AM builds. As a result, there are increasing interests and rapid development of sensor-based models for the characterization and estimation of layer-wise defects in the past few years. However, very little has been done to go from sensor-based modeling of defects to the suggestion of in situ corrective actions for quality control of AM builds. In this paper, we propose a new sequential decision-making framework for in situ control of AM processes through the constrained Markov decision process (CMDP), which jointly considers the conflicting objectives of both total cost (i.e., energy or time) and build quality. Experimental results show that the CMDP formulation provides an effective policy for executing corrective actions to repair and counteract incipient defects in AM before completion of the build.