Optimum design of parallel kinematic toolheads with genetic algorithms

Authors:
Dan Zhang;Zhengyi Xu;Chris M. Mechefske;Fengfeng Xi
Affiliations:
Integrated Manufacturing Technologies Institute, National Research Council Canada, London, Ontario (Canada) N6G 4X8;Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario (Canada) M5S 3G8;Department of Mechanical Engineering, Queen's University, Kingston, Ontario (Canada) K7L 3N6;Department of Mechanical, Aerospace and Industrial Engineering, Ryerson University, Toronto, Ontario (Canada) M5B 2K3
Venue:
Robotica
Year:
2004

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Abstract

In this paper, the optimum design of parallel kinematic toolheads is implemented using genetic algorithms with the consideration of the global stiffness and workspace volume of the toolheads. First, a complete kinetostatic model is developed which includes three types of compliance, namely, actuator compliance, leg bending compliance and leg axial compliance. Second, based on this model, two kinetostatic performance indices are introduced to provide a new means of measuring compliance over the workspace. These two kinetostatic performance indices are the mean value and the standard deviation of the trace of the generalized compliance matrix. The mean value represents the average compliance of the Parallel Kinematic Machines over the workspace, while the standard deviation indicates the compliance fluctuation relative to the mean value. Third, design optimization is implemented for global stiffness and working volume based on kinetostatic performance indices. Additionally, some compliance comparisons between Tripod toolhead and other two principal Tripod-based Parallel Kinematic Machines are conducted.