Midterm colloquium Junhwi Mun

Automated Robotic System for Precision NVH Testing: Recognition, Targeting, and Impact Optimization

Supervisor: dr. M. Guo

Abstract: Noise, Vibration, and Harshness (NVH) is a critical area of study in the automotive and machinery industries, serving as a key performance indicator that impacts customer satisfaction, comfort, and perceived product quality. Traditionally, NVH testing is performed manually on fully assembled components or systems, relying on subjective evaluations and objective measurements to identify vibrational and acoustic characteristics. However, manual testing presents significant challenges, including variability in impact angle, targeting precision, and force application. These limitations result in inconsistent measurements, reduced repeatability, and labor-intensive processes.

To address these shortcomings, this research proposes an automated NVH testing system that utilizes a robotic arm equipped with a controlled impact hammer. The robotic system ensures precise control over the critical parameters of impact testing—angle, accuracy, and force—thereby improving the reliability and efficiency of vibration measurements. The automation process not only reduces the number of impacts required per target point but also minimizes human intervention, making the testing process faster and more consistent.

The implementation of such a system involves overcoming several research challenges. First, extracting normal vectors from point clouds to align the robotic arm's end effector accurately with non-planar or irregular component surfaces is essential. Second, optimizing the robotic arm's trajectory to balance collision avoidance, targeting accuracy, and path efficiency is crucial for navigating complex geometries. Lastly, ensuring the system’s adaptability to handle diverse and custom-designed components requires advanced calibration techniques and flexible programming.

This study presents a comprehensive approach to integrating robotic systems into NVH testing, leveraging advanced 3D data processing, multi-objective optimization, and adaptable automation. By addressing these challenges, the proposed system bridges the gap between manual and automated testing, delivering high-precision, repeatable, and efficient solutions. The findings aim to redefine NVH testing practices, improving product quality and streamlining testing workflows in pre-production environments.