الفهرس | يوجد فقط 14 صفحة متاحة للعرض العام |
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. |