Tractor Cabin Structure Borne Noise Reduction in the Virtual Environment

In recent days, cabin variants in the tractor are preferred by the farmers for the Coziness and longer field hour operation with less fatigue. Noise perceived by customer is the most important factor taken into account during the design stage, as it’s directly linked with operator’s comfort. Observed noise levels has to be within the defined limits as per national/international standards Overall cabin noise levels is contributed by the structure borne noise below 630 Hz. Structure borne noise is the noise typically radiated by the door, roof, windshield, floor, fender and structure assembly due to the engine excitation through the transmission housings and backstories. This paper depicts the process of tractor cabin structure borne noise prediction in the virtual environment. Firstly, Engine bearing loads and axle bearings has been extracted in the virtual stage from the vehicle level driveline model using commercially available MBD software. The finite element (FE) model of the cabin and full vehicle has been built using the software FE Codes. Noise Transfer Function (NTF) and Panel contribution analysis and sensitivity study has been executed to identify the critical panels, systems and assemblies. Once critical panels are identified, machine learning based NTF Optimization has been executed at the cabin level to minimize the NTF levels at the early stage of design. Model finalized based on the machine learning has been integrated with the full vehicle level model. Lastly the structure borne noise has been predicted at the operator ear level using the derived loads across the bearings as the input. Predicted NTF and structure borne noise levels has been compared with the physical measurement data to ensure good correlation. With the design modifications on the identified parts and assemblies based on the sensitivity study lower structure borne noise levels has been achieved in the virtual environment, which assists to shorten the lead time.