Magnetic gripper design optimization for robotic bending cell using artificial intelligence clustering of sheet metal parts

The manufacturing sector is currently facing unprecedented challenges in adapting to the constantly evolving demands of diverse product lines and rapid market changes. Conventional manufacturing systems are struggling to adapt to the increasing variety of production components, leading to notable inefficiencies and heightened expenses. In this context, Reconfigurable Manufacturing Systems (RMS) have emerged as a prominent strategy to boost the adaptability and responsiveness of production processes. Therefore, the design and optimization of grippers for robotic arms are deemed essential to improve efficiency and productivity. The project aims to enhance gripper design by using AI clustering techniques and dimensional analysis to cluster production components and define design parameters for novel gripper configurations. This approach aligns with the tenets of lean manufacturing and data-driven decision-making, empowering manufacturing engineers and designers. The project also aims to optimize internal design and manufacturing, reducing reliance on external suppliers, and improving long-term adaptability and competitiveness by leveraging the cost reduction that in-house processes represent. The case study examines 964 sheet metal production components, highlighting inefficiencies of manual classification, part allocation challenges, and design specification retrieval. Furthermore, it explores different scenarios to render the best cluster quality possible with the supplied dataset and the constraints that materialize when translating the design parameters into actual design properties of the grippers, as well as the gripper-part compatibility. The thesis introduces an innovative method for managing part variety in gripper design by seizing advanced technologies and data-driven decision-making. This results in substantial enhancements in time efficiency, cost reduction, safety optimization, and the eradication of inefficient workflows within the manufacturing sector.

https://orcid.org/0000-0001-5461-0355