Profile Monitoring for Autocorrelated Reflow Processes with Small Samples
AbstractThe 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. View Full-Text
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Fan, S.-K.S.; Jen, C.-H.; Lee, J.-X. Profile Monitoring for Autocorrelated Reflow Processes with Small Samples. Processes 2019, 7, 104.
Fan S-KS, Jen C-H, Lee J-X. Profile Monitoring for Autocorrelated Reflow Processes with Small Samples. Processes. 2019; 7(2):104.Chicago/Turabian Style
Fan, Shu-Kai S.; Jen, Chih-Hung; Lee, Jai-Xhing. 2019. "Profile Monitoring for Autocorrelated Reflow Processes with Small Samples." Processes 7, no. 2: 104.
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