| This thesis describes the application of the stochastic-flow-modeling (SFM) approach to represent the quality behavior of a manufacturing system. Initially, a simple, one-product type SFM is discussed and then a more complex multiple-product manufacturing system is developed. This quality SFM-based model has aggregation by station, product, and operational shift. Subsequently, potential supervisory control architectures that could be used in conjunction with this quality-based SFM are discussed and developed. Distribution parameter fitting is explored using static and adaptive approaches and a comparison between these two approaches is given. Then, the accuracy of the SFM modeling technique is demonstrated using two simulation examples.;Effective equipment maintenance is essential for a manufacturing plant seeking to produce high quality products. The impact of equipment reliability and quality on throughput have been well established, but the relationship between maintenance and quality is not always clear nor direct. Therefore, after developing a SFM to represent the quality of a manufacturing system, the focus of this work shifts towards identifying correlations between maintenance and quality. This thesis describes a statistical modeling method that makes use of a Kalman filter to identify correlations between independent sets of maintenance and quality data. With such a method, maintenance efforts can be better prioritized to satisfy both production and quality requirements. In addition, this method is used to compare results from the theoretical maintenance-quality model to data from an actual manufacturing system. Results of the analysis indicate the potential for this method to be applied to preventive, as well as reactive maintenance decisions, since ageing aspects of equipment are also considered in the model. |
