Univariate and multivariate control charts for monitoring dynamic-behavior processes: a case study
Abstract: The majority of
classic SPC methodologies assume a steady-state (i.e., static) process behavior
(i.e., the process mean and variance are constant) without the influence of the
dynamic behavior (i.e., an intended or unintended shift in the process mean or
variance). Traditional SPC has been successfully used in steady-state
manufacturing processes, but these approaches are not valid for use in dynamic
behavior environments. The goal of this paper is to present the process
monitoring and adjustment methodologies for addressing dynamic behavior
problems so that system performance improvement may be attained. The
methodologies will provide a scientific approach to acquire critical knowledge
of the dynamic behavior as well as improved control and quality, leading to the
enhancement of economic position. The two major developments in this paper are:
(1) the characterization of the dynamic behavior of the manufacturing process
with the appropriate monitoring procedures; and (2) the development of adaptive
monitoring procedures for the processes [for example, using trend charts (e.g.,
linear model) and time series charts (e.g., ARIMA models)] with a comparison
between univariate and multivariate control charts. To provide a realistic
environment for the development of the dynamic behavior monitoring and
adjustment procedures, the cold rolling process is adopted as a test bed.
Keywords: Statistical process
control (SPC), Dynamic behavior, EWMA control charts, MEWMA control chart,
Autocorrelation, ARIMA models, Cold rolling, Linear trend models
Author: Salah Haridy, Zhang Wu
Journal Code: jptindustrigg090027
