Performance Analysis, Mapping, and Multiobjective Optimization of a Hybrid Robotic Machine Tool

A serial-parallel hybrid machine tool is expected to integrate the respective features of pure serial/parallel mechanism. The traditional method of hybridization is to connect n (n ≥ 2) mechanisms bottom to head, in which at least one should be a parallel mechanism. One unique approach called Mechanism Hybridization is to embed one serial mechanism inside of a pure parallel mechanism, which greatly changes its overall performance. Based on this idea, an X-Y gantry system including a five-axis hybrid manipulator is developed, which is expected to be applied as the next generation of computer numerical control machine. The inverse kinematics and Jacobian matrix are derived. Since performance improvement is one of the most important factors that greatly affect the application potential of hybrid manipulators in different industry fields, to deeply investigate the comprehensive features, the local/global performance indexes of stiffness, dexterity, and manipulability are mathematically modeled and mapped. A discrete-boundary-searching method is developed to calculate and visualize the workspace. Pareto-based evolutionary multiobjective performance optimization is implemented to simultaneously improve the four indexes, and the representative nondominated solutions are listed. The proposed methodologies are generic and applicable for the design, modeling, and improvement of other parallel/hybrid robotic machine tools.