Mechanism design optimization through CAD-based Bayesian optimization and quantified constraints

Abstract

This study addresses the challenge of mechanism design optimization, particularly focusing on the energy efficiency and design space of reciprocating mechanisms. The research question centers on how to effectively utilize Computer-Aided Design (CAD) simulations alongside Bayesian optimization (BO) and a constrained design space to streamline the design optimization process, overcoming the limitations of traditional kinematic and dynamic analysis methods. The objective is to investigate and develop a novel optimization framework that integrates CAD-based simulations with a BO approach.To achieve this, the study employs a methodological approach. At first, the feasibility of a chosen mechanism design is evaluated through a sequence of CAD-motion simulations to quantify the (in)feasibility of this design. When this design appears to be feasible, a CAD-based design evaluation method is started, in which the objective value is extracted by a sequence of CAD-motion simulations. In this paper, we advocate the use of non-parametric Gaussian processes to build a surrogate model of the objective function and the feasible design space constrained by static and dynamic constraints. The main research results demonstrate that the proposed CAD-based Bayesian optimization framework can effectively identify optimal design parameters that minimize the root mean square (RMS) torque while adhering to specified static and dynamic constraints. This optimization approach significantly reduces the complexity associated with analytic methods, making it scalable to more complex mechanisms and implementable by machine builders. In conclusion, the study successfully develops a novel optimization framework that leverages CAD-based simulations and Bayesian optimization to streamline the design process of mechanisms. The results of an emergency ventilator case study with three design parameters show a reduction of the RMS torque with 71% after 255 CAD-based design evaluations. Moreover, the results demonstrate the effectiveness of incorporating constraints into the design optimization process and the usage of Bayesian optimization (BO), providing insights into the obtained optimum. This approach offers probabilistic insights into the expected improvement of the optimum, highlighting that the full optimization potential is utilized in a computationally efficient manner.