Digital Twin-Based Smart Manufacturing: Dynamic Line Reconfiguration for Disturbance Handling

The increasing complexity of modern manufacturing, coupled with demand fluctuation, supply chain uncertainties, and product customization, underscores the need for manufacturing systems that can flexibly update their configurations and swiftly adapt to disturbances. However, current research falls short in providing a holistic reconfigurable manufacturing framework that seamlessly monitors system disturbances, optimizes alternative line configurations based on machine capabilities, and automates simulation evaluation for swift adaptations. This paper presents a dynamic manufacturing line reconfiguration framework to handle disturbances that result in operation time changes. The framework incorporates a system process digital twin for monitoring disturbances and triggering reconfigurations, a capability-based ontology model capturing available agent and resource options, a configuration optimizer generating optimal line configurations, and a simulation generation program initializing simulation setups and evaluating line configurations at approximately 400x real-time speed. A case study of a battery production line has been conducted to evaluate the proposed framework. In two implemented disturbance scenarios, the framework successfully recovers system throughput with limited resources, preventing the 26% and 63% throughput drops that would have occurred without a reconfiguration plan. The reconfiguration optimizer efficiently finds optimal solutions, taking an average of 0.03 seconds to find a reconfiguration plan for a manufacturing line with 51 operations and 40 available agents across 8 agent types. Note to Practitioners—This research is motivated by the need for an advanced framework that automates the reconfiguration of manufacturing lines in response to increasing disturbances within dynamic production environments. Existing approaches lack a holistic framework that automates disturbance monitoring, data management, line structure optimization, and pre-implementation simulation verification. This paper integrates all these components and develops an innovative configuration optimizer and simulator to improve the optimality and efficiency of disturbance handling. We demonstrate the applicability of our framework through a case study in a battery production line, focusing on agent delay issues. The approach is adaptable to a variety of disturbance scenarios in other types of manufacturing lines that share common elements such as workers, robots, machines, and auxiliary tools. Future investigations will expand on reconfiguration considerations, including spatial constraints, agent experience levels, and tool change requirements.