Not Available Not available 21 2016 J AS Support design Not available Aircraft mock-up Dassault Systems V6 22 2016 C M Layout optimization No HMI interactions Not available 23 2016 J M Data Manag. in lifecycle No No No 24 2016 C AS Systems engineering & mechanical design integr. No CAE-based simulations Mathematica and Matlab/Simulink 25 2016 C R Virtual Commissioning No To implement and optimize the algorithm for control of robots VEROSIM (Virtual Environment and Robotic Simulation) 26 2016 C AS Detecting failures; definition of performance Product failures detection No No Type: C = Conference, J = Journal, BC = Book Chapter; Field: AS = Aeronautics and Space, R = Robotics, M= Manufacturing, I = Informatics

3.3. Digital Twin in the industrial engineering This section is devoted to a more detailed analysis of a subset of the papers presented in Table 2, namely those applying the DT concept to the industrial engineering: thus comprising all the papers in the manufacturing and robotics sectors. Also the informatics paper was considered in this analysis because the application domain was the industrial IoT. The schematic results of this analysis are presented in Table 3, where the papers are confronted with the main aspects emerged from the previous literature on DT. Elisa Negri et al. / Procedia Manufacturing 11 ( 2017 ) 939 948 Table 3. Analysis of the papers about Digital Twin in industrial engineering Lifecycle No Ref Field Industry 4.0 Big data CPS Semantic Data Model 11 M Industry 4.0, IoT Yes Production system lifecycle Yes Meta-information and semantics 16 R Industry 4.0 No Complex technical systems lifecycle No No 17 I Industrial IoT No

IoT lifecycle No No 18 M Smart CPS No Production system lifecycle Yes No 19 M Industry 4.0, IoT Yes Product lifecycle Yes AutomationML model for Data Exchange 23 M No No No No Database with CAD models 24 M Smart Products Yes Product lifecycle Yes Semantic Data Management 26 R No No No No

No 1) The important connection between the DT concept and the Industry 4.0, mentioned in Section 1, is confirmed also by the considered papers: in fact, only two papers do not mention it [44,47], all the others name the Industry 4.0 [25,28,31] or one of the connected concepts (IoT , Smart CPS , Smart Product ). 2) The Big Data topic is not strongly recognized by the authors as a key aspect of the DT modeling. In fact, only Rosen recognizes that the DT model requires a huge digital data storage and Schroeder carries on this argumentation stating that Big Data management and analytics become an issue in a DT context . Abramovici mentions Big Data as an analysis method to elaborate data for DT-based optimizations .