Acoustic analysis and data-driven control of vehicle NVH: A framework for manufacturing process optimization

Abstract

This study introduces a comprehensive data-driven framework for acoustic analysis and control of noise, vibration, and harshness (NVH) in vehicle manufacturing. By integrating advanced techniques such as Transfer Path Analysis (TPA) and Operational Transfer Path Analysis (OTPA) with big data analytics, the framework systematically identifies and mitigates critical noise and vibration sources across key frequency ranges. Experimental validation demonstrated a 47% reduction in physical testing and a 28% shorter optimization cycle, improving vehicle interior acoustic performance. The proposed approach provides a scalable solution for sustainable NVH management by reducing resource consumption and supporting data-driven decision-making in manufacturing processes. These findings underscore the framework's adaptability to various manufacturing environments and its contribution to advancing efficient NVH optimization practices in modern vehicle design.