Emergence of collective intelligence in industrial cyber-physical-social systems for collaborative task allocation and defect detection

Abstract

The manufacturing industry is facing the challenge of meeting the growing demand for personalized products, which requires enhanced agility, flexibility, reconfigurability, and sustainability on the shop floor. To tackle these requirements, one possible solution is to foster collective intelligence (CI) by sharing data and knowledge among human operators, machines, and workpieces, thereby improving resource utilization and enabling informed decision-making. However, the implementation of CI in manufacturing systems poses several challenges, including interoperability issues and the complexity of communication and coordination between heterogeneous manufacturing resources. These challenges can be addressed by integrating cyber-physical-social systems (CPSS) and distributed artificial intelligence. Therefore, this paper presents a framework for enabling the emergence of CI in industrial CPSS to achieve collaborative task allocation and defect detection. The framework infuses intelligence into physical manufacturing resources through CPSS configuration and establishes real-time collaborative communication coupled with decentralized decision-making using a multi-agent system. Additionally, an online deep Q-network (DQN) is employed to train a smart scheduler agent for self-organized assignment of manufacturing tasks to machines. The proposed method is implemented in a 3D printing factory testbed. Experimental findings demonstrate the practicality and effectiveness of the proposed method, which has been deployed in the 3D printing factory for a few months and has reduced production costs, defects, and nonconformities in 3D printed parts.