Reconfigurable Manufacturing Systems through Intelligent Workpiece Handling and Artificial Intelligence

Abstract

In recent years, businesses have strived to align their manufacturing goods with the unique needs and preferences of their customers. However, the mass customization paradigm introduces challenges mostly associated to the manipulation of small product batches and knowledge management. This paper proposes a framework for intelligent reconfigurable manufacturing systems, that leverages robotic automation supported by a collaborative dual-arm manipulator, cognitive mechatronic devices, and machine-learning algorithms. In more detail, pioneer end-effectors and machinery provide the capacity of grasping and handling a variety of products without time-consuming hardware modifications. The backbone of this manipulation scheme is a set of machine learning tools for: a) the planning and synchronization of handling agents, b) identification and localization of workpieces, c) the generation of optimal packaging configurations based on customer orders and identified workpieces, and d) the allocation of the respective handling operations to robots, operators, and machinery. The proposed framework is evaluated through its implementation on a use case deriving from the metal industry. The particularities of this use case, as expressed in product variety, workpiece complexity, customer orders and intralogistics, provide a fertile ground for the validation of the discussed technologies.