Digital twin: current scenario and a case study on a manufacturing process

In the current scenario, industries need to have continuous improvement in their manufacturing processes. Digital twin (DT), a virtual representation of a physical entity, serves this purpose. It aims to bridge the prevailing gap between the design and manufacturing stages of a product by effective flow of information. This article aims to create a state-of-the-art review on various DTs with their application areas. The article also includes schematic representations of some of the DTs proposed in various fields. The concept is also represented by a case study based on a DT model developed for an advanced manufacturing process named friction stir welding. Towards the end, a model for implementing DT in a factory has been proposed.

[1]  I. Graessler,et al.  Integration of a digital twin as human representation in a scheduling procedure of a cyber-physical production system , 2017, 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).

[2]  Wei Zhang,et al.  Building digital twins of 3D printing machines , 2017 .

[3]  Yu Yang,et al.  Automatic gap detection in friction stir butt welding operations , 2008 .

[4]  Surjya K. Pal,et al.  Defect identification in friction stir welding using discrete wavelet analysis , 2015, Adv. Eng. Softw..

[5]  Farhad Ameri,et al.  Digital Factories for Capability Modeling and Visualization , 2016, APMS.

[6]  P. M. Galetti,et al.  Genetic Pattern and Demographic History of Salminus brasiliensis: Population Expansion in the Pantanal Region during the Pleistocene , 2018, Front. Genet..

[7]  S. Michael Spottswood,et al.  Reengineering Aircraft Structural Life Prediction Using a Digital Twin , 2011 .

[8]  Meng Zhang,et al.  Equipment energy consumption management in digital twin shop-floor: A framework and potential applications , 2018, 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC).

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

[10]  Marco Sepulveda,et al.  A Review and Methodology Development for Remaining Useful Life Prediction of Offshore Fixed and Floating Wind turbine Power Converter with Digital Twin Technology Perspective , 2018, 2018 2nd International Conference on Green Energy and Applications (ICGEA).

[11]  Eric J. Tuegel,et al.  The Airframe Digital Twin: Some Challenges to Realization , 2012 .

[12]  Rikard Söderberg,et al.  Toward a Digital Twin for real-time geometry assurance in individualized production , 2017 .

[13]  Amit P. Sheth,et al.  Physical-Cyber-Social Computing: Looking Back, Looking Forward , 2015, IEEE Internet Comput..

[14]  Radovan Kovacevic,et al.  Monitoring and processing the acoustic emission signals from the friction-stir-welding process , 2006 .

[15]  Surjya K. Pal,et al.  Weld defect identification in friction stir welding through optimized wavelet transformation of signals and validation through X-ray micro-CT scan , 2018, The International Journal of Advanced Manufacturing Technology.

[16]  Jianhua Liu,et al.  Digital twin-based smart production management and control framework for the complex product assembly shop-floor , 2018, The International Journal of Advanced Manufacturing Technology.

[17]  Yongli Wei,et al.  Digital twin for CNC machine tool: modeling and using strategy , 2018, Journal of Ambient Intelligence and Humanized Computing.

[18]  Edward H. Glaessgen,et al.  The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles , 2012 .

[19]  Radovan Kovacevic,et al.  Wavelet transform analysis of acoustic emission in monitoring friction stir welding of 6061 aluminum , 2003 .

[20]  Surjya K. Pal,et al.  A review on sensor based monitoring and control of friction stir welding process and a roadmap to Industry 4.0 , 2018, Journal of Manufacturing Processes.

[21]  Rajiv S. Mishra,et al.  Friction Stir Welding and Processing , 2007 .

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

[23]  Surjya K. Pal,et al.  Defect identification in friction stir welding using continuous wavelet transform , 2019, J. Intell. Manuf..

[24]  Jeroen van den Hoven,et al.  Digital Twins in Health Care: Ethical Implications of an Emerging Engineering Paradigm , 2018, Front. Genet..

[25]  Sankaran Mahadevan,et al.  Dynamic Bayesian Network for Aircraft Wing Health Monitoring Digital Twin , 2017 .

[26]  Kush P. Mehta,et al.  Sustainability in Welding and Processing , 2018, Materials Forming, Machining and Tribology.

[27]  J. S. Zuback,et al.  Building blocks for a digital twin of additive manufacturing , 2017 .

[28]  Dalibor Petkovic,et al.  Estimation of contact forces of underactuated robotic finger using soft computing methods , 2017, J. Intell. Manuf..

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

[30]  Kalyanmoy Deb,et al.  Data mining methods for knowledge discovery in multi-objective optimization: Part A - Survey , 2017, Expert Syst. Appl..

[31]  Rolf Steinhilper,et al.  The Digital Twin: Demonstrating the Potential of Real Time Data Acquisition in Production Systems ☆ , 2017 .

[32]  F. G. Rimini,et al.  Digital twin applications for the JET divertor , 2017 .

[33]  Hergen Pargmann,et al.  Intelligent big data processing for wind farm monitoring and analysis based on cloud-technologies and digital twins: A quantitative approach , 2018, 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA).

[34]  Carlos Eduardo Pereira,et al.  Visualising the digital twin using web services and augmented reality , 2016, 2016 IEEE 14th International Conference on Industrial Informatics (INDIN).

[35]  Yuan He,et al.  From Surveillance to Digital Twin: Challenges and Recent Advances of Signal Processing for Industrial Internet of Things , 2018, IEEE Signal Processing Magazine.

[36]  Ján Vachálek,et al.  The digital twin of an industrial production line within the industry 4.0 concept , 2017, 2017 21st International Conference on Process Control (PC).

[37]  Gianni Ferretti,et al.  Towards digital twins through object-oriented modelling: a machine tool case study , 2018 .

[38]  C. Lynch.  Big data: How do your data grow? , 2008, Nature.

[39]  Gerd Dobmann,et al.  Monitoring of the Friction Stir Welding Process to Describe Parameter Effects on Joint Quality , 2008 .

[40]  Rikard Söderberg,et al.  Inspection Data to Support a Digital Twin for Geometry Assurance , 2017 .

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

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

[43]  Feng Xiang,et al.  Digital twins technolgy and its data fusion in iron and steel product life cycle , 2018, 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC).

[44]  Hui Cao,et al.  Product life cycle: the evolution of a paradigm and literature review from 1950–2009 , 2012 .

[45]  Sukhomay Pal,et al.  Defect detection in friction stir welding process through characterization of signals by fractal dimension , 2016 .

[46]  Alvin M. Strauss,et al.  Misalignment detection and enabling of seam tracking for friction stir welding , 2009 .

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

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

[49]  Surjya K. Pal,et al.  Friction stir weld classification by applying wavelet analysis and support vector machine on weld surface images , 2015 .

[50]  Zhang Haijun,et al.  Dynamic resource allocation optimization for digital twin-driven smart shopfloor , 2018 .

[51]  Jerzy Pasławski,et al.  Industry 4.0 Concept Introduction into Construction SMEs , 2017 .

[52]  W. Thomas,et al.  Friction stir welding for the transportation industries , 1997 .

[53]  Fei Tao,et al.  Digital twin-driven product design, manufacturing and service with big data , 2017, The International Journal of Advanced Manufacturing Technology.

[54]  Núbia Gabriela Pereira Carvalho,et al.  Manufacturing in the fourth industrial revolution: A positive prospect in Sustainable Manufacturing , 2018 .