  This research delves into optimizing mechanism design, with an emphasis on
the energy efficiency and the expansive design possibilities of reciprocating
mechanisms. It investigates how to efficiently integrate Computer-Aided Design
(CAD) simulations with Bayesian Optimization (BO) and a constrained design
space, aiming to enhance the design optimization process beyond the confines of
traditional kinematic and dynamic analysis. The study sets out to create a
novel optimization framework that merges CAD simulations with a BO strategy.
Initially, the feasibility of a mechanism design is assessed through CAD-motion
simulations, which gauge its practicality. Upon deeming a design feasible, an
evaluation via CAD-motion simulations is conducted to ascertain the objective
value. This research proposes utilizing non-parametric Gaussian processes for
crafting a surrogate model of the objective function, considering the design
space's static and dynamic constraints. The findings reveal that the introduced
CAD-based Bayesian Optimization framework adeptly identifies optimal design
parameters that minimize root mean square (RMS) torque while complying with
predetermined constraints. This method markedly diminishes the complexity seen
in analytical approaches, rendering it adaptable to intricate mechanisms and
practicable for machine builders. The framework evidences the utility of
integrating constraints in the optimization process, showing promise for
attaining globally optimal designs efficiently. A case study on an emergency
ventilator, with three design parameters, demonstrates a 71% RMS torque
reduction after 255 CAD-based evaluations, underscoring the approach's
effectiveness and its potential for refining mechanism design optimization.
