Abstract
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.
Highlights
Statistical process control (SPC) has globally been applied for dealing with process monitoring in a variety of manufacturing processes [1]
The main objective of this research is to construct a monitoring system that can compensate for the one-step-ahead residuals, for the reflow process with small samples in surface mount technology (SMT)
In terms of the simulation results, the analysis of profile monitoring can be done in three parts: (i) making a comparison of the fitting performance between the polynomial regression with AR(1), the modified sum of sine functions with AR(1), and the nonlinear mixed effects model (NLME)
Summary
Statistical process control (SPC) has globally been applied for dealing with process monitoring in a variety of manufacturing processes [1]. Several productive processes (e.g., reflow oven, heat treatment, etc.) have proven difficult to manage with a traditional SPC operation. The difficulty in these cases arises because a quality characteristic cannot be suitably characterized. If the quality characteristic of a product or process can be represented by a functional form between the quality characteristic and the input variable, effective monitoring can be established. This scenario is the so-called “profile monitoring”
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