Smart Custom-Gripper with Vision Intelligence For Efficient Material Handling of Small and Irregular Parts

Automation of material handling remains a key focus in industrial manufacturing, particularly for small and irregularly shaped components such as the gear shift fork. This study presents an investigation of smart gripping systems and existing robotic configurations, aiming to enhance efficiency, accuracy, and economic viability. By integrating advanced image processing techniques, the research proposes a transformative approach to improving part orientation detection and handling precision. A collaborative robot equipped with a novel gripper was utilized to assess performance, achieving efficient re-orientation within 11 to 15 seconds. This research underscores the advantages of smart custom gripper in cost-sensitive environments, while robotic systems, despite higher initial investment and maintenance costs, offer superior adaptability and precision for complex part handling. The findings highlight the need for a context-driven selection of automation strategies, providing a data-driven framework for optimizing material handling processes in manufacturing industries.