Steps for Data Exchange between Real Environment and Virtual Simulation Environment

Recently the technological evolution resulting from the fourth industrial revolution, especially with the advancement of the internet of things and bigdata, coupled with the change in consumer behavior, are forcing companies to improve the efficiency of production systems. Now, companies must mass produce to keep costs low, but they must also be flexible and offer a wide variety of products. Thus, companies are increasingly using computational tools to improve decision making, especially with the application of simulation and digitization of production systems. However, online data collection is presenting itself as a solution to decrease the time for the development of simulation models and, thus, speed up decision making. In this way, this work shows the steps required for online data exchange between a real system and a virtual production system. For this, a prototype will be used that demonstrates these basic concepts.

[1]  Xin Chen,et al.  A Digital Twin-Based Approach for Designing and Multi-Objective Optimization of Hollow Glass Production Line , 2017, IEEE Access.

[2]  Klaus-Dieter Thoben,et al.  Product Lifecycle Management in the Era of Internet of Things , 2015, IFIP Advances in Information and Communication Technology.

[3]  Giuseppe Di Gironimo,et al.  Virtual production planning of a high-speed train using a discrete event simulation based approach , 2015 .

[4]  Iiro Harjunkoski,et al.  The impact of digitalization on the future of control and operations , 2017, Comput. Chem. Eng..

[5]  Meng Zhang,et al.  Digital Twin Shop-Floor: A New Shop-Floor Paradigm Towards Smart Manufacturing , 2017, IEEE Access.

[6]  John Stark,et al.  Digital Transformation of Industry , 2020 .

[7]  Joel Sauza Bedolla,et al.  PLM-MES Integration: A Case-Study in Automotive Manufacturing , 2015, PLM.

[8]  Andrew Y. C. Nee,et al.  Digital twin-driven product design framework , 2019, Int. J. Prod. Res..

[9]  Rosley Anholon,et al.  Analysis of the integration between operations management manufacturing tools with discrete event simulation , 2017, Prod. Eng..

[10]  Héctor Mesa,et al.  Solving Scheduling Problems with Genetic Algorithms Using a Priority Encoding Scheme , 2017, IWANN.

[11]  Marc Priggemeyer,et al.  Experimentable Digital Twins—Streamlining Simulation-Based Systems Engineering for Industry 4.0 , 2018, IEEE Transactions on Industrial Informatics.

[12]  Kagermann Henning Recommendations for implementing the strategic initiative INDUSTRIE 4.0 , 2013 .

[13]  Csaba Haraszkó,et al.  DES Configurators for Rapid Virtual Prototyping and Optimisation of Manufacturing Systems , 2015 .

[14]  Richard J. Schonberger,et al.  ASP, The Art and Science of Practice: Three Challenges for a Lean Enterprise in Turbulent Times , 2015, Interfaces.

[15]  Rolf Steinhilper,et al.  The Digital Twin: Realizing the Cyber-Physical Production System for Industry 4.0☆ , 2017 .

[16]  Shiwang Hou,et al.  An optimization algorithm for integrated remanufacturing production planning and scheduling system , 2017 .

[17]  Richard C. Peralta,et al.  Multiobjective genetic algorithm conjunctive use optimization for production, cost, and energy with dynamic return flow , 2014 .

[18]  Jay Lee,et al.  A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems , 2015 .

[19]  Enzo Morosini Frazzon,et al.  Simulation-based optimization for the integrated scheduling of production and logistic systems , 2016 .

[20]  Robert Eduardo Cooper Ordoñez,et al.  Theoretical proposal of steps for the implementation of the Industry 4.0 concept , 2019, Brazilian Journal of Operations & Production Management.