Real-Time Robotic Painting and Surface Finishing in Automotive Manufacturing
Abstract
The surface finishing and robotic painting processes in automotive manufacturing are critical determinants of vehicle exterior quality and production efficiency. Despite considerable progress in automation, real-time correction of paint irregularities remains a major challenge due to the absence of continuous, adaptive feedback mechanisms. Conventional vision-based inspection systems primarily detect defects post-application, leading to increased rework, material wastage, and production downtime. To overcome these constraints, this paper proposes the Real-Time Adaptive Aeroacoustic Robotic Painting and Finishing System (RAARPS)-a novel intelligent framework that integrates aeroacoustic sensing and deep learning-driven adaptive control for instant defect detection and correction. The RAARPS system utilizes an array of miniature directional microphones and airflow sensors strategically positioned on robotic arms and within the paint booth to capture high-frequency acoustic and aerodynamic signals generated during spray atomization and surface wetting. These signals are processed by a hybrid convolutional and recurrent neural network model, which interprets subtle variations in sound and airflow patterns to identify turbulence, overspray, or non-uniform atomization within milliseconds. The inference model then autonomously adjusts spray parameters, such as paint flow rate, electrostatic charge, and arm motion trajectory, ensuring consistent coating thickness and finish quality. Experimental validation demonstrates that RAARPS achieves a 6% improvement in defect detection accuracy and enhances first-pass yield compared to existing vision-based systems. Furthermore, the system reduces paint waste by approximately 12% and lowers volatile organic compound (VOC) emissions by about 8%, contributing significantly to sustainable and eco-efficient manufacturing. The results confirm that RAARPS provides a transformative advancement in intelligent robotic painting, offering real-time, data-driven adaptability that enhances surface precision, operational reliability, and environmental compliance.