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With ride comfort in a motorcycle gaining significance, it is important to minimize vibration levels at the customer touch points. The reciprocating piston imparts rotary motion to the crankshaft which in turn induces unbalance forces and produces vibration in the vehicle, thus influencing the ride quality. Generally, the primary inertial forces are balanced by a combination of balancer body and crank web. However, being a commuter bike, a balancer body could not be accommodated due to cost and space constraints. In such scenario, the first order unbalance force cannot be completely eliminated but can only be redistributed by adding counterweight to the crankshaft. Proper distribution of these forces is required for optimum vibration levels at motorcycle touch sensitive points (TSP) such as handle bar, footrest etc.

In this paper the approach to predict engine noise under combustion forces is presented. This Methodology is divided into three stages: 1. Multi body dynamic (MBD) Simulation to determine excitation forces 2. Vibration analysis of engine under combustion load 3. Acoustic analysis of engine to predict Sound Pressure Level (SPL). Important parts of motorcycle engine with single cylinder are considered as flexible bodies for MBD simulation. It is necessary to accurately model crankshaft ball bearing for capturing the accurate transmissibility of combustion forces from crankshaft to casings. In this work crankshaft ball bearing is modeled with 6×6 stiffness matrix. It provides coupling between radial, axial and tilting deflections of bearing and it also allows moment transfer from crankshaft to casing. It helps to predict the realistic forces at bearings. Forces predicted from MBD simulation are applied to engine FE model for carrying out vibration analysis.

In this paper the approach to predict vibrations in motorcycles is presented. It can be divided mainly in two parts: prediction of engine forces using multi body dynamics (MBD) simulation and prediction of vibration response using FEA. Dynamic forces predicted at each engine mount through MBD simulation are used as input to FE analysis for vibration prediction. Single cylinder SI engine having primary balancer shaft is considered to develop this methodology. Flexibilities of important parts are considered for MBD simulation. Crankshaft ball bearing which is used in almost all two wheeler engine is modeled with 6×6 stiffness matrix. It provides coupling between radial, axial and tilting deflections of bearing and it also allows moment transfer from crankshaft to casing. This helps to predict realistic forces at each bearing and engine mounts. Distribution of primary and secondary forces at crank bearings and at different engine mounts is studied.