Local and global accessibility analysis for generative process planning

This dissertation presents a primitive-based approach in support of the generative process planning including automatic machine selection, tool selection, and part setup optimization. The approach includes local and global accessibility analysis with tool geometry on tessellated solid models, building the 4-vector (facet, machine, setup, and tool) multi-axis (4 or 5-axis) manufacturing primitives (MPs) and clustering them into the manufacturing features (MFs).; The first crucial feature of the approach is computing the tool accessibility of a solid model. The accessibility approach is fully developed for tessellated models and the tool geometry of spherical end-mills. In the approach, local accessibility analysis is used to set an upper bound of the feasible tool radius for regions of a given part and global accessibility analysis based on a ray (tool without radius) is used to create a set of accessibility maps called binary sphere maps (BSMs). A transformation function is then used to modify the set of BSMs based on a given tool geometry.; The second crucial feature of the approach is optimizations of the Sculptured Surfaces Machining (SSM) orientation on the worktable of a multi-axis NC machine. Govindarajalu (2003) created a Binary Integer Programming (BIP) model for clustering the 3-axis MPs derived from accessibility maps on solid models into the MFs for automated setup planning with the objective of minimizing machining time. Govindarajalu used the BIP model to optimize the setup of SSM on 3-axis machines. In SSM multi-axis (4-axis and 5-axis) machining offers many advantages over 3-axis machining. This dissertation presents an approach to derive the multi-axis MPs from the 3-axis MPs and applies a similar BIP model to multi-axis machining.; Finally, a 4-vector BIP model has been developed from a 2-vector (facet and orientation) BIP model given by Govindarajalu. A two-step decomposition has been done to solve the model. Experiments were conducted on the sample parts to demonstrate the validity of the approach. The results showed that the approach was useful for selecting machines, tools, and for optimizing setups based on an objective of minimizing machining time.