Date of Award
Master of Science in Engineering
Industrial and Entrepreneurial Engineering and Engineering Management
Dr. Azim Houshyar
Dr. Steven Butt
Dr. Bob White
Masters Thesis-Campus Only
My research examines the selection of the tool design and cutting parameters for rough milling of metal in CNC machining operations. A selection of cutting tools from three manufacturers, a specific work piece material and a specific machine tool are used for the research. The optimum cutting tool is defined as the tool that gives the user the largest number of combinations of cutter parameters that are close to the maximum metal removal rate that can be achieved with the cutting tool. This cutting tool gives the user options to adjust cutting parameters on the machine tool to compensate for work piece, machine tool and other factors while maintaining a metal removal rate that is near the optimum for all tools considered. Deterministic and probabilistic methodologies are evaluated to find the best method to determine the combinations of cutting parameters for each tool that yield the highest metal removal rate. The method which selects the combination of cutter parameters that yields the highest metal removal rate is a probabilistic implementation of the TABU search method.
Rivera, Mark A., "Selection of Milling Cutter Design and Cutting Parameters for Rough Milling Applications" (2011). Master's Theses. 413.