Monitoring Spatial Uniformity of Particle Distributions in Manufacturing Processes Using the K Function

doi:10.1109/TASE.2015.2479088

	Monitoring Spatial Uniformity of Particle Distributions in Manufacturing Processes Using the K Function
	Huang, Xiaohu 1; Zhou, Qiang 1; Zeng, Li 2; Li, Xiaodong 3
刊名	IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
	2017-04-01
卷号	14 期号:2 页码:1031-1041
关键词	Complete spatial randomness control chart Gaussian process model K function spatial point pattern
ISSN号	1545-5955
DOI	10.1109/TASE.2015.2479088
英文摘要	Data 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.
资助项目	Hong Kong Research Grants Council under ECS[138213] ; National Science Foundation[CMMI-1266225]
WOS研究方向	Automation & Control Systems
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000399347500055
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/25285]
专题	中国科学院数学与系统科学研究院
通讯作者	Zhou, Qiang
作者单位	1.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
推荐引用方式 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.