Data-Driven Pattern-Based Constructs Definition for the Digital Transformation Modelling of Collaborative Networked Manufacturing Enterprises

The digital transformation of collaborative networked manufacturing enterprises requires the building and the applying digital models representing the set of resources and processes knowledge. Modelling such digital copy of the physical system to perform real-time validation and optimization is quite complex and thus needs a big amount of data and some modelling patterns representing the operational semantics of the modelled elements. Generally, the modelling action has a specific application type. For this reason, the core challenge of the digital transformation modelling is to create a modular “digital model”, namely a decomposable and re-composable model, towards different applications. The authors propose an approach based on the combination of data-driven and model-based approaches, to identify and formalize modelling patterns, that combine for developing a modular executable model of the studied system.

[1]  Lin Sun,et al.  Modular based flexible digital twin for factory design , 2018, Journal of Ambient Intelligence and Humanized Computing.

[2]  Robert X. Gao,et al.  Digital Twin for rotating machinery fault diagnosis in smart manufacturing , 2018, Int. J. Prod. Res..

[3]  Abdulrahman Al-Ahmari,et al.  Requirements of the Smart Factory System: A Survey and Perspective , 2018, Machines.

[4]  Sanjay Jain,et al.  Manufacturing data analytics using a virtual factory representation , 2017, Int. J. Prod. Res..

[5]  Athanasios V. Vasilakos,et al.  Machine learning on big data: Opportunities and challenges , 2017, Neurocomputing.

[6]  Michele Dassisti,et al.  Industry 4.0 paradigm: The viewpoint of the small and medium enterprises , 2017 .

[7]  Gerd Stumme,et al.  Formal Concept Analysis , 2009, Handbook on Ontologies.

[8]  Nezih Mrad,et al.  The role of data fusion in predictive maintenance using digital twin , 2018 .

[9]  Jie Zhang,et al.  The modelling and operations for the digital twin in the context of manufacturing , 2018, Enterp. Inf. Syst..

[10]  Sandro Wartzack,et al.  Shaping the digital twin for design and production engineering , 2017 .

[11]  Fei Tao,et al.  Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison , 2018, IEEE Access.

[12]  Aaron D. Arndt,et al.  Supply chain collaboration: what's happening? , 2005 .

[13]  Fei Tao,et al.  Digital twin modeling method for CNC machine tool , 2018, 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC).

[14]  Felix T.S. Chan,et al.  Defining a Digital Twin-based Cyber-Physical Production System for autonomous manufacturing in smart shop floors , 2019, Int. J. Prod. Res..

[15]  Marco Wiering,et al.  Reinforcement Learning and Markov Decision Processes , 2012, Reinforcement Learning.

[16]  Jay Lee,et al.  Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment , 2014 .

[17]  Yew Seng Ng,et al.  Evaluation of decision fusion strategies for effective collaboration among heterogeneous fault diagnostic methods , 2011, Comput. Chem. Eng..

[18]  Amos H. C. Ng,et al.  Digital Twin: Applying emulation for machine reconditioning , 2018 .

[19]  Andrew Y. C. Nee,et al.  Digital twin driven prognostics and health management for complex equipment , 2018 .

[20]  Stoyan Stoyanov,et al.  Prognostic and health management for engineering systems: a review of the data-driven approach and algorithms , 2015 .

[21]  Roland Rosen,et al.  About The Importance of Autonomy and Digital Twins for the Future of Manufacturing , 2015 .

[22]  Xi Zhang,et al.  Effective fault detection & isolation using bond graph-based Domain decomposition , 2009, 2009 American Control Conference.

[23]  Noureddine Zerhouni,et al.  A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models , 2012, IEEE Transactions on Reliability.

[24]  Sylvain Verron,et al.  Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges , 2016, Annu. Rev. Control..

[25]  Morteza Ghobakhloo,et al.  The future of manufacturing industry: a strategic roadmap toward Industry 4.0 , 2018, Journal of Manufacturing Technology Management.

[26]  Sanford Friedenthal,et al.  A Practical Guide to SysML: The Systems Modeling Language , 2008 .

[27]  Antonio Padovano,et al.  A Digital Twin based Service Oriented Application for a 4.0 Knowledge Navigation in the Smart Factory , 2018 .

[28]  Nasser Jazdi,et al.  A concept in synchronization of virtual production system with real factory based on anchor-point method , 2018 .

[29]  J J M Braat,et al.  High-accuracy long-distance measurements in air with a frequency comb laser. , 2009, Optics letters.

[30]  Yingfeng Zhang,et al.  A framework for Big Data driven product lifecycle management , 2017 .

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