Manufacturing processes--Data processing

This thesis presents a mathematical model for the sensitivity analysis of machine cell formation. Computer programs in C were developed. A statistical simulation model is developed to test and verify the mathematical model. The data for machine cell formation in cellular manufacturing is organized in a machine component chart representing the machining requirements of parts in the product mix. The existing machine cell formation models treat the product mix as deterministic. To study the probabilistic nature of the cellular manufacturing, a sensitivity analysis model is presented. The model optimizes the formation of intercellular material handling cost for the machine cell within the constrains of the probability of the product mixture. The results of the mathematical model is compared with the results of the simulation model. It shows that the probabilistic product mix has a influence on the efficiency of the machine cell and the associated total cost.

The costs associated with the design and manufacture of machined components can be significantly reduced by the ability to identify and group similar parts. This activity is generally accomplished by assigning each part a Group Technology code number based on its most significant characteristics. Attempts to accomplish this are hindered by: the relatively small amount of information that can be encoded in a code of manageable length, inconsistencies in human interpretation of design and manufacturing data, the commitment of resources required to review and encode all candidate components at a facility, and the heuristic nature of determining what constitutes significant similarity for any particular application. These problems are addressed by the development of a system that assists in the determination of similarity by comparing CAD (Computer Aided Design) files, rather than Group Technology codes, in a manufacturing oriented frame-based system.