LAPSE:2019.0593
Published Article
LAPSE:2019.0593
Profile Monitoring for Autocorrelated Reflow Processes with Small Samples
Shu-Kai S. Fan, Chih-Hung Jen, Jai-Xhing Lee
June 10, 2019
The methodology of profile monitoring combines both the model fitting and statistical process control (SPC) techniques. Over the past ten years, a variety of profile monitoring methods have been proposed and extensively investigated in terms of different process profiles. However, monitoring tasks still exhibit a primary problem in that the errors surrounding the functional relationship are frequently assumed to be independent within every single profile. However, the assumption of independence is an unrealistic assumption in many practical instances. In particular, within-profile autocorrelation often occurs in the profile data. To mitigate the within-profile autocorrelation, a monitoring method incorporating an autoregressive (AR)(1) model to cope with autocorrelation is proposed. In this paper, the reflow process with small samples in surface mount technology (SMT) is investigated. In Phase I, three different process models are compared in combination with the first-order autoregressive model, while an appropriate profile model is sought. The Hotelling T² and exponentially weighted moving average (EWMA) control charts are used together to monitor the parameter estimates (i.e., profile shape) and residuals (i.e., profile variability), respectively.
Keywords
EWMA control chart, Hotelling’s T2 control chart, polynomial regression model, profile monitoring, sum of sine function
Suggested Citation
Fan SKS, Jen CH, Lee JX. Profile Monitoring for Autocorrelated Reflow Processes with Small Samples. (2019). LAPSE:2019.0593
Author Affiliations
Fan SKS: Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei City 10608, Taiwan [ORCID]
Jen CH: Department of Information Management, Lunghwa University of Science and Technology, Guishan, Taoyuan County 33306, Taiwan
Lee JX: Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei City 10608, Taiwan
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Journal Name
Processes
Volume
7
Issue
2
Article Number
E104
Year
2019
Publication Date
2019-02-15
Published Version
ISSN
2227-9717
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PII: pr7020104, Publication Type: Journal Article
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doi:10.3390/pr7020104
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Jun 10, 2019
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