You Only Look Once, But the Parts Keep Moving: YOLO-Based Workpiece Pose Classification for Aerodynamic Part Feeding

Flexible part feeding is a key challenge in modern automated production, where increasing uncertainties, shorter product life cycles, and cost pressures require adaptable solutions. Aerodynamic part feeding systems, which use controlled air jets to manipulate workpieces, offer a retooling-free alternative to traditional vibratory bowl feeders. To ensure precise workpiece handling, reliable pose classification is essential. This paper presents a machine learning-based framework for classifying workpiece poses using a class of convolutional neural networks (CNNs) called YOLO and an industrial camera. Instead of relying on manually labeled real-world images-which would introduce machine downtimes and increased setup efforts-the proposed method trains CNNs exclusively on synthetic datasets. Artificial images of workpieces in various poses are generated from CAD models using the open-source rendering engine Blender. Multiple CNN architectures are trained and evaluated, achieving a classification precision exceeding 95 % for most workpieces when tested on real workpiece images. The results demonstrate that the approach enables accurate and efficient workpiece pose classification without the need for labor-intensive dataset creation. While developed for aerodynamic part feeding, the proposed method is applicable to a wide range of industrial scenarios requiring automated workpiece orientation classification.