Optimal Motorcycle Engine Mount Design Parameter Identification Using Robust Optimization Algorithms

Mechanical vibrations have a significant impact on ride comfort; the driver is constantly distracted as result. Volumetric engine inertial unbalances and road profile irregularities create mechanical vibrations. The purpose of this study to employ optimization algorithms identify structural elements that contribute vibration propagation provide optimal solutions for reducing induced by unbalance and/or abnormalities in motorcycle. powertrain assembly, swing-arm vibration-isolating mounts make up system. Engine are used restrict transferred forces motorbike frame owing shaking or irregularities. Two 12-degree-of-freedom (DOF) motorcycle systems (PMS) were modeled examined design study. first model was compute mount parameters transmitted load through while only considering loads, whereas second considered both bump loads. In configurations, infinitely stiff. stiffness, location, orientation be parameters. computational methods minimize loads forces. To continue process, Grey Wolf Optimizer (GWO), meta-heuristic swarm intelligence algorithm inspired grey wolves nature, utilized. demonstrate GWO’s superior performance PMS, other such Genetic Algorithm (GA) Sequential Quadratic Programming (SQP) comparison. engine’s force, GWO employed determine mounting cost constraint functions formulated optimized, promising results obtained documented. modes due decoupled smooth ride.