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

TL;DR

This paper introduces a digital twin-based framework for dynamic manufacturing line reconfiguration, enabling quick disturbance handling and throughput recovery in complex, customizable production environments.

Contribution

It presents a holistic, automated reconfiguration framework integrating disturbance monitoring, capability modeling, optimization, and simulation at high speed.

Findings

Successfully recovered throughput in case studies with limited resources.

Prevented 26% and 63% throughput drops through reconfiguration.

Achieved reconfiguration plan generation in approximately 0.03 seconds.

Abstract

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.