Abstract

Smart manufacturing aims to convert data acquired across the product lifecycle into manufacturing intelligence in order to yield positive impacts on all aspects of manufacturing [TAO18]. Digital Twin (DT) is a powerful concept in smart manufacturing context. Broadly defined as the virtual and computerized counterpart of a physical system [KRI18], DTs basis itself on synchronization between the virtual and real system, thanks to sensed data and connected smart devices, mathematical models and real time data elaboration [NEG17]. In this work a Standard Based Digital Twin of a turning machining process is developed, and a number of domain-specific challenges are surpassed, including diverse communication standards, heterogeneous data structures and interfaces. The real manufacturing process is instrumented with temperature [BAR97] vibration and force sensors strategically positioned closely to the cutting process as well as in the environment [KER12], [LE12], [SAB15]. Sensorial data is collected using a dedicated machining monitoring system that allows real time display of measured variables as well as other important indicators, such as warnings and information about the process state that are derived from these measurements via conditional or artificial intelligence models. With the applications developed in this work users can obtain a fully operational DT in three clicks: (Click1) the user will upload its CAD/STEP/SIMULATION model to automatically create the product breakdown structure, (Click2) mount the appropriate sensors on the product and (Click3) configure the IoT consumer/producer software. Data management of the repository will be done by an ISO-10303 product lifecycle management (PLM) and the Arrowhead Framework. The implementations are achieved by the combination of an ISO-10303 repository [LAN19] and the Open-Source ECLIPSE Arrowhead Framework [PAN21] for IIoT/CPS integrations. The use of the project innovations will, thus, enable optimization of upstream manufacturing processes, like design, engineering analysis and process planning, and sensor data received from the DT. REFERENCES [BAR97] Barlier, C.; Lescalier, C.; Mosian, A. (1997): Continuous Flank Wear Measurement of Turning Tools by Integrated Microthermocouple. In CIRP Annals 46 (1), pp. 35–38. DOI: 10.1016/S0007-8506(07)60770-7. [KER12] Kerrigan, K.; Thil, J.; Hewison, R.; O’Donnell, G. E. (2012): An Integrated Telemetric Thermocouple Sensor for Process Monitoring of CFRP Milling Operations. In Procedia CIRP 1, pp. 449–454. DOI: 10.1016/j.procir.2012.04.080. [KRI18] Kritzinger, Werner; Karner, Matthias; Traar, Georg; Henjes, Jan; Sihn, Wilfried (2018): Digital Twin in manufacturing: A categorical literature review and classification (51). [LAN19] Lanza, R.; Haenisch, J.; Bengtsson, K.; Rølvåg, T. (2019): Relating structural test and FEA data with STEP AP209. In Advances in Engineering Software 127, pp. 96–105. DOI: 10.1016/j.advengsoft.2018.08.005. [LE12] Le Coz, G.; Marinescu, M.; Devillez, A.; Dudzinski, D.; Velnom, L. (2012): Measuring temperature of rotating cutting tools: Application to MQL drilling and dry milling of aerospace alloys. In Applied Thermal Engineering 36, pp. 434–441. DOI: 10.1016/j.applthermaleng.2011.10.060. [NEG17] Negri, Elisa; Fumagalli, Luca; Macchi, Marco (2017): A Review of the Roles of Digital Twin in CPS-based Production Systems. In Procedia Manufacturing 11, pp. 939–948. DOI: 10.1016/j.promfg.2017.07.198. [PAN21] Paniagua, Cristina; Delsing, Jerker (2021): Industrial Frameworks for Internet of Things: A Survey. In IEEE Systems Journal 15 (1), pp. 1149–1159. DOI: 10.1109/JSYST.2020.2993323. [SAB15] Sabkhi, N.; Pelaingre, C.; Barlier, C.; Moufki, A.; Nouari, M. (2015): Characterization of the Cutting Forces Generated During the Gear Hobbing Process: Spur Gear. In Procedia CIRP 31, pp. 411–416. DOI: 10.1016/j.procir.2015.03.041. [TAO18] Tao, Fei; Qi, Qinglin; Liu, Ang; Kusiak, Andrew (2018): Data-driven smart manufacturing. In Journal of Manufacturing Systems 48, pp. 157–169. DOI: 10.1016/j.jmsy.2018.01.006.