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E-Mobility and low noise IC Engines has pushed product development teams to focus more on sound quality rather than just on reduced noise levels and legislative needs. Furthermore, qualification of products from a sound quality perspective from an end of line testing requirement is also a major challenge. End of line (EOL) NVH testing is key evaluation criteria for product quality with respect to NVH and warranty. Currently for subsystem or component level evaluation, subjective assessment of the components is done by a person to segregate OK and NOK components. As human factor is included, the process becomes very subjective and time consuming. Components with different acceptance criteria will be present and it’s difficult to point out the root cause for NOK components. In this paper, implementation of machine learning is done for acoustic source detection at end of line testing.

NVH refinement has gained increased importance in the automotive industry especially in the last two decades, due to increased global competition and customer requirements. Furthermore, owing to the stringent legislative requirements on the radiated noise levels have also put additional demands on the automotive industry from the NVH perspective. Engineers have been constantly focusing on improving NVH evaluation techniques and test methodologies to improve the quality of test data, ease of measurement and productivity. The traditional input/output technique based modal analysis has always been an NVH engineer's favorite tool, for identifying the dynamic behavior of the component / BIW / vehicle in whole. The knowledge of a structure's modes and mode shape gives sufficient information of the component with respect to the system's modal alignment, for further optimization of performance, weight and for updating the CAE model.

For an automotive exhaust system, noise level and back pressure are the most important parameters for passenger comfort and engine performance respectively. The sound quality perception of the existing silencer design was unacceptable, although the back pressure measured was below the target limit. To improve the existing design, few concepts were prepared by changing the internal elements of silencer only. The design constraints were the silencer shell dimensions, volume of silencer, inlet pipe and outlet tailpipe positions, which had to be kept same as that of the existing base design. The sound quality signal replaying and synthesizing was performed to define the desired sound quality. The numerical simulation involves 3D computational fluid dynamics (CFD) with appropriate boundary condition having less numerical diffusions to predict the back pressure. The various silencer concepts developed with this preliminary analysis, was then experimentally verified with the numerical data.