A Computational Framework for Data-Driven Mechanism Design Innovation

Suny At Stony Brook, Stony Brook NY

Investigators

Abstract

Recent trends in democratization of manufacturing capability such do-it-yourself hobby shops, 3D printing technology, as well as low-cost sensors, actuators, and microcontrollers, call for a corresponding democratization of design tools that can help engineers and tinkerers alike to innovate and invent motion generating devices. Motion generation is a fundamental aspect of machines, at the heart of which are kinematic mechanisms that make it possible for motions to be transmitted or transformed. A kinematic mechanism is a collection of moving pieces linked together through kinematic joints such as hinge joints and sliders. Mechanism design innovation involves the selection of an appropriate mechanism type (i.e., the number of moving pieces and joints as well as the pattern of their interconnections) and the determination of key dimensions in the mechanism needed to generate the desired motions. Once a mechanism type is selected, the appropriate dimensions can often be determined by solving a system of polynomial equations. The task of type selection, however, is not so amenable to mathematical treatment, and requires a level of intuition that may take many years to develop and is difficult to pass on. This award supports the development of a set of web-based, data-driven design tools that unify the type and dimensional synthesis for mechanism design innovation. The planned MOOC (massive open online course) will help bring these tools to the masses and help promote interest in science and engineering including high school students and those from under-represented groups. The research team will bring together the diverse fields of reverse engineering, computational shape analysis, and design kinematics to develop a data-driven paradigm for kinematic synthesis of mechanical motion generation devices. The goal is to advance the science of mechanism design and lead to practical and efficient design tools capable of solving highly complex motion generation problems faced by machine designers. Central to this research is the creation of a new computational framework for simultaneous type and dimensional synthesis of various mechanisms. This includes (1) the development of unified versions of design equations that span broad classes of mechanisms; (2) the development of unified algorithms for data-driven simultaneous type and dimensional synthesis of planar, spherical and spatial mechanisms, and (3) the creation of a mechanism design portal, which will allow users to design, store, search, compare, and analyze mechanisms.