Big data approach for the simultaneous determination of the topology and end-effector location of a planar linkage mechanism

Abstract

Although significant advances have been made in mechanism synthesis to find a mechanism generating a desired motion at its end-effector, no available synthesis methods can determine the topology of a mechanism and its end-effector location simultaneously. It is generally difficult to pre-determine the location of the end-effector relative to the input drive link because the mechanism synthesis may fail if the end-effector location is incorrectly selected. Therefore, the simultaneous determination of the mechanism topology and its end-effector location can be critically useful to advance automated mechanism synthesis. Motivated by this, we propose a neural network-based big data approach to achieve the simultaneous determination. To implement a big data approach which requires a training dataset consisting of diverse mechanisms of different topologies, we propose the use of a spring-connected rigid block model as a unified means to be able to represent diverse mechanisms. The big data approach is followed by gradient-based shape optimization to determine the detailed dimensions of the mechanism synthesized by the approach. The effectiveness and validity of the proposed method are checked with various mechanism synthesis problems.