Multi-Objective Optimization of a Three Degree-of-Freedom Translational Parallel Kinematic Machine with Coplanar Rails

Abstract

Several advancements in the field of parallel manipulators have taken place in recent days as they offer many advantages over serial manipulators in terms of accuracy, agility, stiffness, speed, etc. The Parallel Kinematic Machines (PKMs) with lower Degree of Freedom (DoF) joints are being explored for a variety of industrial applications and, in particular, machining applications as these offer more accuracy, high machining capability, and more stiffness. This research work focuses on the modeling, kinematics, workspace and dexterity analyses of a 3DoF Translational PKM having coplanar rails along the Cartesian axes: -X, +X, +Y. Actuation of sliders, independently along the respective rails, offer the tool platform pure translational motion. Fixed length links are used to connect the sliders and tool platform. The PKM under study is modeled in CATIA. Inverse kinematics and workspace analysis are carried out using the performance indices, namely, Workspace Volume Index (WVI) and Global Condition Index (GCI). Attempts are also made to find the optimal dimensions of the PKM through multi-objective optimization using Genetic Algorithms in MATLAB. The methodology presented is helpful to predict the PKM's performance capability while the results obtained are helpful for the development of a physical prototype necessary for further experimental investigations.