الفهرس 
Introductionيوجد فقط 14 صفحة متاحة للعرض العام
 |  | from | 183 |  |  |
| | | from | 183 | | |
المستخلص
Driven by global competition and evolving customer needs and expectations,
manufacturing systems today have witnessed a significant increase in dynamic behavior and
unstable state (i.e., an attempt to shift the process from one operating level to another).
The majority of SPC methodologies assume a steady-state (static) process behavior
(i.e., operating with a constant mean and constant variance) without the influence of the dynamic
behavior. Traditional SPC has been successfully used in steady-state manufacturing processes,
but recently these approaches are being reevaluated for use in dynamic behavior environments.
Quality control activities should not disturb the flow of the production process and must
cope with its nature. Hence, the use of SPC methodologies to address processes that
are in dynamic behavior mode has started to emerge.
The dynamic behavior of a manufacturing process may be represented as a system with
input variables, output variables, and a noise disturbance. An important outcome of the dynamic
behavior is the induced transition period (i.e., a temporal trend that is inherent to the dynamic
behavior) and the autocorrelation (i.e., the data is not independent), which compromises
the validity of traditional SPC for monitoring the process. Because of poor understanding and
control of the dynamic behavior, large product and pound losses often result. While much
research effort has been dedicated to the advancement of monitoring and adjustment
methodologies at steady-state process, so little attention has been given to the dynamic
manufacturing processes.
The goal of this research 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 three major developments in this research are:
I. The characterization of the dynamic behavior of the manufacturing process with
the appropriate monitoring procedures.
2. The development of adaptive monitoring procedures for the process [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.