Conclusion

Digital twin research has been started for many years, but there is no standardized tool or method recognized by the academic community. In this study, system dynamics was proposed as a tool that can solve integration problems such as multi-scale, multi-physics, and multi-disciplinary, which are continuously becoming issues in the digital twin field. By utilizing this, it was revealed that various heterogeneous data from various protocols or platforms can be integrated into one model. In addition, the five-step DT construction process was explained using actual data. Through the simulation results, it was confirmed that the potential problem caused by the latent effect can be found, and the method of optimizing it using the results is described in a rough way, which can contribute to the usability aspect. In this study, in order to explain the model building process using system dynamics, the update of variables is omitted (most statistical model-based modules are implemented with the lookup function). In actual DT simulation, real data is accumulated and run-time update of root causes is possible. Details on this can be found in Sücüllü & Yücel (2014) and Richardson (2015). In this study, only the service phase was implemented among the 4 phases of the total lifecycle, but design, manufacturing, and retire phases can all be added by using the module addition method described in this text. Since the system dynamics model for each phase of the total life has been studied a lot, the DT model can be completed by integrating them.