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Monitoring Spatial Uniformity of Particle Distributions in Manufacturing Processes Using the K Function
Huang, Xiaohu1; Zhou, Qiang1; Zeng, Li2; Li, Xiaodong3
2017-04-01
Source PublicationIEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
ISSN1545-5955
Volume14Issue:2Pages:1031-1041
AbstractData in the form of spatial point patterns are frequently encountered in some manufacturing processes such as the nanoparticle reinforced composite materials and defects on semiconductor wafers. Their spatial characteristics contain rich information about the fabrication processes and are often strongly related to the product quality. The distributional characteristics of a spatial point pattern can be summarized by functional profiles such as the popular Ripley's K function. By analyzing the K function, we can effectively monitor the distributional behaviors of the spatial point data. In this study, statistical properties of the K function are investigated, and a Gaussian process is found to be appropriate in characterizing its behavior under complete spatial randomness. A control chart is proposed based on the results to monitor the uniformity of point patterns. Our proposed approach has been compared with existing methods through numerical simulations and shown superior performances. Note to Practitioners-In some manufacturing processes, the spatial distribution of "points," such as defects, dead pixels, and particles, is often highly related with final product quality. It is often beneficial to analyze the point distribution pattern to monitor the processes and identify potential process variations. In the current practice, experience-based and error-prone visual inspection is still common, particularly in new processes such as fabrication of nanoparticle-reinforced materials. This work aims to automate this monitoring process by developing a highly effective algorithm for assessing the uniformity of point distributions, based on rigorous statistical properties from spatial point distribution theories. Our simulation results show the proposed method is much more efficient than some typical existing methods in detecting nonrandomness of the point pattern. To implement this method, locations of all points need to be known and extracted by pre-processing the inspection data which are often grayscale images.
KeywordComplete spatial randomness control chart Gaussian process model K function spatial point pattern
DOI10.1109/TASE.2015.2479088
Language英语
Funding ProjectHong Kong Research Grants Council under ECS[138213] ; National Science Foundation[CMMI-1266225]
WOS Research AreaAutomation & Control Systems
WOS SubjectAutomation & Control Systems
WOS IDWOS:000399347500055
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/25286
Collection中国科学院数学与系统科学研究院
Corresponding AuthorZhou, Qiang
Affiliation1.City Univ Hong Kong, Dept Syst Engn & Engn Management, Kowloon Tong, Hong Kong, Peoples R China
2.Texas A&M Univ, Dept Ind & Syst Engn, College Stn, TX 77843 USA
3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100864, Peoples R China
Recommended Citation
GB/T 7714
Huang, Xiaohu,Zhou, Qiang,Zeng, Li,et al. Monitoring Spatial Uniformity of Particle Distributions in Manufacturing Processes Using the K Function[J]. IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING,2017,14(2):1031-1041.
APA Huang, Xiaohu,Zhou, Qiang,Zeng, Li,&Li, Xiaodong.(2017).Monitoring Spatial Uniformity of Particle Distributions in Manufacturing Processes Using the K Function.IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING,14(2),1031-1041.
MLA Huang, Xiaohu,et al."Monitoring Spatial Uniformity of Particle Distributions in Manufacturing Processes Using the K Function".IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 14.2(2017):1031-1041.
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