The growing demand for noise reduction in multi-joint long-reach robotic arms necessitates the development of precise noise measurement methodologies. However, accurate characterization remains challenging due to the robot’s complex kinematics. Specifically, dynamic joint positions and motion trajectories can lead to acoustic occlusion, while the inherent directivity of sound sources further compromises measurement reliability. To address these issues, this study proposes a component-based hybrid measurement approach. First, the noise generated by a single joint was characterized using a simplified 4-point method, with Green’s function applied to correct for variable propagation distances. Subsequently, the total sound power level of the entire robotic arm was synthesized in a virtual environment by integrating the single-joint acoustic data with the arm’s operational kinematic program. Validation results demonstrate that the proposed method achieves a measurement error of only 0.8 dB relative to the reverberation chamber benchmark—an accuracy superior to that of direct measurements of the full robotic arm cycle (2.6 dB). Furthermore, a comparison with the ISO 3744:2025 9-point standard method reveals that while the proposed 4-point approach yields a slightly larger error (0.8 dB vs. 0.2 dB), it significantly reduces experimental complexity. Consequently, this method offers a sufficiently accurate and operationally efficient solution for practical engineering applications.
Zhu et al. (Sun,) studied this question.