Laser processes hold significant potential in the aircraft industry, especially for paint removal during Maintenance, Repair, and Operation (MRO), which is typically manual and costly. This study explores automated laser-based paint stripping, a promising alternative that can enhance efficiency and reduce the use of hazardous chemicals and labor costs. To optimize laser parameters for effective paint removal, hyperspectral imaging is proposed as a surface analysis tool, providing vital material properties that inform subsequent processing steps. Hyperspectral imaging has shown efficacy across various applications, including paint identification, through machine learning models like Random Forests and Convolutional Neural Networks (CNNs). This paper compares these models’ performance, highlighting the adaptability of CNNs for real-time hyperspectral data processing, enabling precise laser treatments of aircraft paint systems. The results demonstrate improved accuracy and speed, underscoring hyperspectral imagings potential in automated paint removal processes.
Brüning et al. (Thu,) studied this question.