Dynamic network monitoring and control of in situ image profiles from ultraprecision machining and biomanufacturing processes
Dynamic network monitoring and control of in situ image profiles from ultraprecision machining and biomanufacturing processes
复制标题
对超精密加工和生物制造过程中的原位图像轮廓进行动态网络监测和控制
DOI：
10.1002/qre.2163
复制
发表时间：
2017
影响因子：
2.3
通讯作者：
Yang, Hui
中科院分区：
文献类型：
--
作者：
Kan, Chen;Yang, Hui
在现代工业中，先进的成像技术已被越来越多地投资，以科普日益复杂的系统，以提高信息的可见性，提高运营质量和完整性。因此，大量的成像数据是容易获得的。这对过程监控和质量控制的最新实践提出了巨大挑战。传统的统计过程控制（SPC）侧重于产品或过程的关键特性，并且相当局限于处理高维成像数据的复杂结构。新的SPC方法和工具，迫切需要提取有用的信息，从现场图像的过程监测和质量控制。在这项研究中，我们开发了一种新的动态网络方案来表示，建模和控制时变图像轮廓。引入Potts模型的Hamilton方法来刻画动态网络中的社团模式和组织行为。此外，从网络社区中提取新的统计数据来表征和量化图像轮廓的动态结构。最后，我们设计和开发了一种新的控制图，即网络广义似然比图，以检测复杂过程的潜在动力学的变化点。所提出的方法的实施和评价超精密加工和生物制造过程中的真实的世界的应用。实验结果表明，该方法能够有效地表征和监测时变图像数据复杂结构的变化。新的动态网络SPC方法被证明具有很强的潜力，在一个不同的领域与原位成像数据的一般应用。
In modern industries, advanced imaging technology has been more and more invested to cope with the ever‐increasing complexity of systems, to improve the visibility of information and enhance operational quality and integrity. As a result, large amounts of imaging data are readily available. This presents great challenges on the state‐of‐the‐art practices in process monitoring and quality control. Conventional statistical process control (SPC) focuses on key characteristics of the product or process and is rather limited to handle complex structures of high‐dimensional imaging data. New SPC methods and tools are urgently needed to extract useful information from in situ image profiles for process monitoring and quality control. In this study, we developed a novel dynamic network scheme to represent, model, and control time‐varying image profiles. Potts model Hamiltonian approach is introduced to characterize community patterns and organizational behaviors in the dynamic network. Further, new statistics are extracted from network communities to characterize and quantify dynamic structures of image profiles. Finally, we design and develop a new control chart, namely, network‐generalized likelihood ratio chart, to detect the change point of the underlying dynamics of complex processes. The proposed methodology is implemented and evaluated for real‐world applications in ultraprecision machining and biomanufacturing processes. Experimental results show that the proposed approach effectively characterize and monitor the variations in complex structures of time‐varying image data. The new dynamic network SPC method is shown to have strong potentials for general applications in a diverse set of domains with in situ imaging data.
DOI：
10.1115/1.2193552
发表时间：
2006-11-01
影响因子：
4
作者：
Kim, Jihyun;Huang, Qiang;Chang, Tzyy-Shuh
通讯作者：
Chang, Tzyy-Shuh
DOI：
--
发表时间：
2010
期刊：
影响因子：
--
作者：
D. Apley
通讯作者：
D. Apley
DOI：
10.1109/tase.2014.2327029
发表时间：
2015-01-01
影响因子：
5.6
作者：
Yan, Hao;Paynabar, Kamran;Shi, Jianjun
通讯作者：
Shi, Jianjun