2026-09-19T01:13:57Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1572432024-10-10T04:03:44Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Anthony, Brian W.
Sun, Brandon Christopher
Massachusetts Institute of Technology. Department of Mechanical Engineering
2024-10-09T18:30:48Z
2024-10-09T18:30:48Z
2024-09
2024-09-25T15:54:14.665Z
https://hdl.handle.net/1721.1/157243
The demand for the product under investigation exceeds the available manufacturing capacity, with the CNC milling workcell identified as the bottleneck operation. This research, conducted in an active, high-mix, low-volume production environment, focuses on evaluating and implementing improvements to CNC machining parameters to enhance the workcell's capacity. Key areas of investigation include machining speeds and feeds, depth of cut, machine settings, toolpath strategies, stepover percentages, and alternative tooling. The study specifically targeted the initial roughing operation, which uses a feed mill and is the longest milling process. Addressing the challenges of high mix and low volume, the research successfully optimized machining and CNC programming parameters, reducing total machining cycle times by 25% and resulting in a 33% increase in throughput. Additionally, the methodologies and findings from this work have provided a framework for implementing further milling process improvements outside of the roughing operation, demonstrating their applicability to similar production scenarios.
M.Eng.
Massachusetts Institute of Technology
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Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
Cycle Time Reduction for CNC Machining Workcells in High-Mix Low-Volume Manufacturing
Thesis
application/pdf
Master
Master of Engineering in Advanced Manufacturing and Design
Publication
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<Abstract>The demand for the product under investigation exceeds the available manufacturing capacity, with the CNC milling workcell identified as the bottleneck operation. This research, conducted in an active, high-mix, low-volume production environment, focuses on evaluating and implementing improvements to CNC machining parameters to enhance the workcell's capacity. Key areas of investigation include machining speeds and feeds, depth of cut, machine settings, toolpath strategies, stepover percentages, and alternative tooling. The study specifically targeted the initial roughing operation, which uses a feed mill and is the longest milling process. Addressing the challenges of high mix and low volume, the research successfully optimized machining and CNC programming parameters, reducing total machining cycle times by 25% and resulting in a 33% increase in throughput. Additionally, the methodologies and findings from this work have provided a framework for implementing further milling process improvements outside of the roughing operation, demonstrating their applicability to similar production scenarios.</Abstract>
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