Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 2003-2022 |
| Number of pages | 20 |
| Journal | Quality and Reliability Engineering International |
| Volume | 33 |
| Issue number | 8 |
| DOIs | |
| State | Published - Dec 1 2017 |
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All Science Journal Classification (ASJC) codes
- Safety, Risk, Reliability and Quality
- Management Science and Operations Research
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Dynamic network monitoring and control of in situ image profiles from ultraprecision machining and biomanufacturing processes. / Kan, Chen; Yang, Hui.
In: Quality and Reliability Engineering International, Vol. 33, No. 8, 01.12.2017, p. 2003-2022.Research output: Contribution to journal › Article
TY - JOUR
T1 - Dynamic network monitoring and control of in situ image profiles from ultraprecision machining and biomanufacturing processes
AU - Kan, Chen
AU - Yang, Hui
PY - 2017/12/1
Y1 - 2017/12/1
N2 - 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.
AB - 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.
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U2 - 10.1002/qre.2163
DO - 10.1002/qre.2163
M3 - Article
AN - SCOPUS:85019348176
VL - 33
SP - 2003
EP - 2022
JO - Quality and Reliability Engineering International
JF - Quality and Reliability Engineering International
SN - 0748-8017
IS - 8
ER -