Digital twin–based cyber-physical system for automotive body production lines

At automotive manufacturing sites, meeting the delivery schedule is difficult owing to the occurrence of unpredictable abnormal scenarios such as product defects and equipment failures. To overcome this, manufacturing technologies developed as part of the Fourth Industrial Revolution are employed to meet the delivery schedule set by the customer. We propose a digital twin (DT)–based cyber-physical system (CPS) that can predict whether a product can be manufactured as per the schedule requested by a customer at an automotive body production line where abnormal scenarios occur. We designed a product, process, plan, plant, and resource information model for automotive body production lines; the proposed DT employs this model. Unlike in previous research on DTs focusing on independent engineering application development, we designed and implemented a CPS combined with a DT and other components for a Web-based integrated manufacturing platform. To the best of our knowledge, this is the first time a DT-based CPS is implemented for abnormal scenarios involving automotive body production lines; the capability of the proposed system was verified via experiments. The experimental results indicate that the proposed system achieved an average prediction performance of 96.83% for the actual production plan. We confirmed that the DT-based CPS can be applied to automotive body production lines, and it provides an advanced solution to predict whether production is possible according to the production plan.