In planning a five-axis machining operation, obstacle-avoidance and machining efficiency are two of the most important issues, especially for machining large and complex parts which are prone to global collision and also take long time to machine, usually in days or even weeks.

In this thesis, we present efficient solutions to address these two. For the obstacle-avoidance problem, we present rigorous analyses of the obstacles in five-axis machining and propose efficient mathematical model for calculating and representing them, where the obstacle-free tool orientations can be determined completely at the tool path planning stage. In addition, as a direct application of our mathematical modeling, we present a heuristic-based solution to the optimal workpiece setup problem, thus g...[ Read more ]

In planning a five-axis machining operation, obstacle-avoidance and machining efficiency are two of the most important issues, especially for machining large and complex parts which are prone to global collision and also take long time to machine, usually in days or even weeks.

In this thesis, we present efficient solutions to address these two. For the obstacle-avoidance problem, we present rigorous analyses of the obstacles in five-axis machining and propose efficient mathematical model for calculating and representing them, where the obstacle-free tool orientations can be determined completely at the tool path planning stage. In addition, as a direct application of our mathematical modeling, we present a heuristic-based solution to the optimal workpiece setup problem, thus greatly reducing the time and error due to remounting and recalibrating of the workpiece and the machine.

With a certain workpiece setup, the machining efficiency problem is then addressed by first optimizing tool orientations and then further tool path generation considering the capacities of the machine itself. For the tool orientation optimization problem, methods and algorithms for a given iso-parametric tool path are proposed to adaptively modify the tool orientations so to reduce the maximal angular accelerations of the machine’s rotary axes, thus enabling larger feedrate assignment. As a more comprehensive solution to the machining efficiency problem, a novel algebraic tool called Machine-Dependent Potential Field (MDPF) is presented, which is defined to characterize the relationship between the material removal rate and the feed direction that considers both the part surface itself and the machine’s loadings. Base on this vector field, a tool path generation algorithm is then proposed that strives to maximize the overall material removal rate over the entire part surface while catering to the limits of the specific machine, achieving tremendous reduction in total machining time as compared to some popular tool path generation algorithms.

Finally, computer simulation experiments are reported that ratify the claimed advantages of the presented solutions.