| The face milling process creates precise flat surfaces with a high degree of flatness, finish and minimal surface errors. One of the main elements, which is not often studied but is extremely significant, burr formation, is studied here in detail. For aluminum-silicon alloys, which are the primary focus in this study, burr formation is the primary reason for tool change and is one of the serious defects requiring subsequent processing after face milling. Edge quality control is a way of improving the face milling process. However, burr minimization cannot be performed in isolation. The process outcomes are coupled and improper planning can lead to increased defect rates or in the worst case unexpected defects and unscheduled maintenance. The maximum benefits for process optimization are claimed by introducing them at the process planning stage by coupling with detailed process models (e.g. cutting forces and tool wear) and with all specifications detailed in the design. A new tool path planning scheme based on a novel feasible region approach is developed to address the edge quality issues in face milling components like those found in an automotive power train. Several approaches for reducing cycle times to enable the use of complicated tool paths in production were developed and tested. The interdependency of individual axis drive speeds for a tool path segment are analyzed. There exists an optimum work angle relative to the feed drive axes that reduces losses and increases allowable feeds for particular segments, saving valuable cycle time and balancing feed drive loads. A new process design scheme, Probabilistic Precision Process Planning (P4), is introduced to incorporate various models and optimization schemes at the highest level of flexibility to achieve maximum cost benefits for specific components and well defined design specifications. The various steps of this scheme for finish face milling are outlined in this dissertation. An attempt is made to explore much of the implementation of this scheme in a digital factory of the future. Finally, the impact of this scheme is demonstrated using two simple case studies, optimization of conditions and tool path for an oil pump flange surface in engine block, and tool geometry and cutting conditions selections for a face milling benchmark component called 'TEAM part'. |
