Thesis (Ph. D.)--Auburn University, 2008. / Abstract. Vita. Written in two volumes. Volume one is public and contains non-proprietary data and results. Volume two contains all proprietary data, and will be available only to the dissertation committee and industrial sponsor. Includes bibliographical references (p. 161-166).

There are a large number of Group Technology (GT) based cell formation techniques in the literature, but their applications rare. It is hypothesized that the reason behind the lack of applications of these techniques in practice, is "fear of the unknown”. There have been a very limited number of attempts to determine the performance of any of the cell formation techniques. This thesis attempts to demonstrate a method to determine the performance of cell formation techniques by measuring the physical performance of the manufacturing cell. The methodology involves a manual evaluative approach to determine the cell performance from the data given for the system. The methodology presents selection of important Performance Measures (PMs), data requirement for the measurement of PMs and cell formation technique analysis. The performance measures to determine the performance of these techniques were selected according to their importance to the productivity of the manufacturing cell and their significance among GT principles. The cell formation techniques selected to demonstrate the method are Rank Order Clustering algorithm (ROC) and Production Flow Analysis (PFA). Using ROC and PFA, part families and machines groups were formed creating cell layouts. From the given data, performance measure values were calculated for a functional layout as well as ROC and PFA layouts. Performance of ROC and PFA layouts were compared to each other and to the functional layout. Results from the example show that performance improvement can be achieved by the two cell formation techniques in all the performance measures category except in flexibility. Performance of ROC and PFA are the same in the categories of setup time, machine utilization. and flexibility. The reason being, similar machine groupings and part families were achieved by both techniques for this example. Material handling performance and flexibility are dependent largely on machine grouping, whereas setup time is dependent on part families. Machine utilization and work-in-process are dependent on machine groups as well as part families. It appears PFA would have better performance in cases of complex problems having large number of machines and parts due to its comprehensiveness and ability to group machines according to the parts’ processing similarities. The advantage of ROC is mainly in its ease of application and rather elegant way of handling bottleneck machines and exceptional parts. Due to the lack of flexibility in GT layouts, system design and operation planning should be done carefully. / Master of Science

The research for this dissertation is focused on the selection of machining parameters for a job shop using expert systems and algorithms. The machining processes are analyzed in detail and rule based expert systems are developed for the analysis of process plans based on operation and work-material compatibility, the selection of machines, cutting tools, cutting fluids, and tool angles. Data base design is examined for this problem. Algorithms are developed to evaluate the selection of machines and cutting tools based on cost considerations. An algorithm for optimizing cutting conditions in turning operations has been developed. Data framework and evaluation procedures are developed for other machining operations involving different types of machines and tools. / Ph. D.