Markov Decision Process for Image-Guided Additive Manufacturing
Markov Decision Process for Image-Guided Additive Manufacturing
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DOI：
10.1109/lra.2018.2839973
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发表时间：
2018-05
影响因子：
5.2
通讯作者：
B. Yao;Farhad Imani;Hui Yang
中科院分区：
文献类型：
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作者：
B. Yao;Farhad Imani;Hui Yang
增材制造（AM）是一种依据计算机辅助设计模型逐层制造具有复杂自由形状几何结构的三维零件的工艺。然而，实时质量控制是阻碍增材制造广泛应用的主要挑战。传感系统的进步促进了增材制造的监测和控制。要充分发挥传感数据在增材制造质量控制中的潜力，在很大程度上取决于有效的分析方法和工具，这些方法和工具将处理复杂的成像数据，并提取有关缺陷状况和工艺动态的相关信息。本文考虑了增材制造零件的最优控制问题，这些零件的逐层缺陷状态可使用先进的传感系统进行监测。具体而言，我们将原位增材制造控制问题表述为马尔可夫决策过程，并利用逐层成像数据来寻找最优控制策略。我们考虑了逐层缺陷变化中的随机不确定性，并旨在在缺陷达到不可修复阶段之前减轻缺陷。最后，通过在金属增材制造应用中利用从逐层图像估计的缺陷状态信号，该模型被用于推导最优控制策略。
Additive manufacturing (AM) is a process to produce three-dimensional parts with complex and free-form geometries layer by layer from computer-aided-design models. However, real-time quality control is the main challenge that hampers the wide adoption of AM. Advancements in sensing systems facilitate AM monitoring and control. Realizing full potentials of sensing data for AM quality control depends to a great extent on effective analytical methods and tools that will handle complicated imaging data, and extract pertinent information about defect conditions and process dynamics. This letter considers the optimal control problem for AM parts whose layerwise defect states can be monitored using advanced sensing systems. Specifically, we formulate the in situ AM control problem as a Markov decision process and utilize the layerwise imaging data to find an optimal control policy. We take into account the stochastic uncertainty in the variations of layerwise defects and aim at mitigating the defects before they reach the nonrecoverable stage. Finally, the model is used to derive an optimal control policy by utilizing the defect-state signals estimated from layerwise images in a metal AM application.