Abstract
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.
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References
Carvalho N, Chaim O, Cazarini E, Gerolamo M (2018) Manufacturing in the fourth industrial revolution: a positive prospect in sustainable manufacturing. Procedia Manuf 21:671–678. https://doi.org/10.1016/j.promfg.2018.02.170
Nowotarski P, Paslawski J (2017) Industry 4.0 concept introduction into construction SMEs. IOP Conf Ser mater Sci Eng 245. https://doi.org/10.1088/1757-899X/245/5/052043
Glaessgen E, Stargel D (2012) The digital twin paradigm for future NASA and U.S. Air Force vehicles. 53rd AIAA/ASME/ASCE/AHS/ASC Struct Struct Dyn Mater Conf AIAA/ASME/AHS Adapt Struct Conf AIAA 1–14. https://doi.org/10.2514/6.2012-1818
Liu Z, Meyendorf N, Mrad N (2018) The role of data fusion in predictive maintenance using digital twin. 020023:020023. https://doi.org/10.1063/1.5031520
Bruynseels K, de Sio FS, van den Hoven J (2018) Digital twins in health care: ethical implications of an emerging engineering paradigm. Front Genet 9:1–11. https://doi.org/10.3389/fgene.2018.00031
Graessler I, Poehler A (2018) Integration of a digital twin as human representation in a scheduling procedure of a cyber-physical production system. IEEE Int Conf Ind Eng Eng Manag 2017-Decem:289–293. https://doi.org/10.1109/IEEM.2017.8289898
Tao F, Cheng J, Qi Q, Zhang M, Zhang H, Sui F (2018) Digital twin-driven product design, manufacturing and service with big data. Int J Adv Manuf Technol 94:3563–3576. https://doi.org/10.1007/s00170-017-0233-1
Cao H, Folan P (2012) Product life cycle: the evolution of a paradigm and literature review from 1950-2009. Prod Plan Control 23:641–662. https://doi.org/10.1080/09537287.2011.577460
Tao F, Sui F, Liu A, Qi Q, Zhang M, Song B, Guo Z, Lu SCY, Nee AYC (2019) Digital twin-driven product design framework. Int J Prod Res 57:3935–3953. https://doi.org/10.1080/00207543.2018.1443229
Qi Q, Tao F (2018) Digital twin and big data towards smart manufacturing and Industry 4.0: 360 degree comparison. IEEE Access 6:3585–3593. https://doi.org/10.1109/ACCESS.2018.2793265
Xiang F, Zhi Z, Jiang G (2018) Digital twins technolgy and its data fusion in iron and steel product life cycle. 2018 Ieee 15Th Int Conf Networking, Sens Control
Zhang H, Zhang G, Yan Q (2018) Dynamic resource allocation optimization for digital twin-driven smart shopfloor. ICNSC 2018 - 15th IEEE Int Conf Networking, Sens Control 1–5. https://doi.org/10.1109/ICNSC.2018.8361283
Tao F, Zhang M (2017) Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing. IEEE Access 5:20418–20427. https://doi.org/10.1109/ACCESS.2017.2756069
Ameri F, Sabbagh R (2016) Digital factories for capability modeling and visualization. Pp 69–78
Vachálek J, Bartalský L, Rovný O, et al (2017) The digital twin of an industrial production line within the Industry 4 . 0 concept. 21st Int Conf Process Control 258–262. https://doi.org/10.1109/PC.2017.7976223
Zhang M, Zuo Y, Tao F (2018) Equipment energy consumption management in digital twin shop-floor: a framework and potential applications. In: 2018 IEEE 15th international conference on networking. Sensing and Control (ICNSC), IEEE, pp 1–5
Zhuang C, Liu J, Xiong H (2018) Digital twin-based smart production management and control framework for the complex product assembly shop-floor. Int J Adv Manuf Technol 96:1149–1163. https://doi.org/10.1007/s00170-018-1617-6
Wärmefjord K, Söderberg R, Lindkvist L, et al (2017) Shaping the digital twin for design and production engineering. In: Volume 2: advanced manufacturing. ASME, p V002T02A101
Wärmefjord K, Söderberg R, Lindkvist L, et al (2017) Inspection data to support a digital twin for geometry assurance. In: Volume 2: Advanced Manufacturing. ASME, p V002T02A101
Söderberg R, Wärmefjord K, Carlson JS, Lindkvist L (2017) Toward a digital twin for real-time geometry assurance in individualized production. CIRP Ann - Manuf Technol 66:137–140. https://doi.org/10.1016/j.cirp.2017.04.038
He Y, Guo J, Zheng X (2018) From surveillance to digital twin: challenges and recent advances of signal processing for industrial internet of things. IEEE Signal Process Mag 35:120–129. https://doi.org/10.1109/MSP.2018.2842228
Pargmann H, Euhausen D, Faber R (2018) Intelligent big data processing for wind farm monitoring and analysis based on cloud-technologies and digital twins: a quantitative approach. 2018 3rd IEEE Int Conf Cloud Comput Big Data Anal ICCCBDA 2018 233–237. https://doi.org/10.1109/ICCCBDA.2018.8386518
Sivalingam K, Spring M, Sepulveda M, Davies P (2018) A review and methodology development for remaining useful life prediction of offshore fixed and floating wind turbine power converter with digital twin technology perspective - IEEE Conference Publication. 197–204. https://doi.org/10.1109/ICGEA.2018.8356292
Tao F, Zhang M, Liu Y, Nee AYC (2018) Digital twin driven prognostics and health management for complex equipment. CIRP Ann 67:169–172. https://doi.org/10.1016/j.cirp.2018.04.055
Luo W, Hu T, Zhang C, Wei Y (2018) Digital twin for CNC machine tool: modeling and using strategy. J Ambient Intell Humaniz Comput 10:2–5. https://doi.org/10.1007/s12652-018-0946-5
Schroeder G, Steinmetz C, Pereira CE, et al (2017) Visualising the digital twin using web services and augmented reality. IEEE Int Conf Ind Informatics 522–527. https://doi.org/10.1109/INDIN.2016.7819217
Tuegel EJ (2012) The airframe digital twin : some challenges to realization. 1–8
Li C, Mahadevan S, Ling Y, Choze S, Wang L (2017) Dynamic Bayesian network for aircraft wing health monitoring digital twin. AIAA J 55:930–941. https://doi.org/10.2514/1.J055201
Tuegel EJ, Ingraffea AR, Eason TG, Spottswood SM (2011) Reengineering aircraft structural life prediction using a digital twin. Int J Aerosp Eng 2011:. https://doi.org/10.1155/2011/154798, 2011, 1, 14
Knapp GL, Mukherjee T, Zuback JS, Wei HL, Palmer TA, de A, DebRoy T (2017) Building blocks for a digital twin of additive manufacturing. Acta Mater 135:390–399. https://doi.org/10.1016/j.actamat.2017.06.039
DebRoy T, Zhang W, Turner J, Babu SS (2017) Building digital twins of 3D printing machines. Scr Mater 135:119–124. https://doi.org/10.1016/j.scriptamat.2016.12.005
Zhang H, Liu Q, Chen X, et al (2017) A digital twin-based approach for designing and decoupling of hollow glass production line. IEEE Access 1–1. https://doi.org/10.1109/ACCESS.2017.2766453
Scaglioni B, Ferretti G (2018) Towards digital twins through object-oriented modelling: a machine tool case study. IFAC-PapersOnLine 51:613–618. https://doi.org/10.1016/j.ifacol.2018.03.104
Iglesias D, Bunting P, Esquembri S, Hollocombe J, Silburn S, Vitton-Mea L, Balboa I, Huber A, Matthews GF, Riccardo V, Rimini F, Valcarcel D (2017) Digital twin applications for the JET divertor. Fusion Eng Des 125:71–76. https://doi.org/10.1016/j.fusengdes.2017.10.012
Uhlemann THJ, Schock C, Lehmann C, Freiberger S, Steinhilper R (2017) The digital twin: demonstrating the potential of real time data acquisition in production systems. Procedia Manuf 9:113–120. https://doi.org/10.1016/j.promfg.2017.04.043
Lynch C (2008) Big data: how do your data grow? Nature 455:28–29. https://doi.org/10.1038/455028a
Barnaghi P, Sheth A, Singh V, Hauswirth M (2015) Computing : looking back. Looking Forward. IEEE Internet Comput 19:7–11
Bandaru S, Ng AHC, Deb K (2017) Data mining methods for knowledge discovery in multi-objective optimization: part a - survey. Expert Syst Appl 70:139–159. https://doi.org/10.1016/j.eswa.2016.10.015
Uhlemann THJ, Lehmann C, Steinhilper R (2017) The digital twin: realizing the cyber-physical production system for Industry 4.0. Procedia CIRP 61:335–340. https://doi.org/10.1016/j.procir.2016.11.152
Thomas W, Nicholas E (1997) Friction stir welding for the transportation industries. Mater Des 18:269–273. https://doi.org/10.1016/S0261-3069(97)00062-9
Mishra RS, Mahoney MW (2007) Friction stir welding and processing. ASM Int 368. https://doi.org/10.1361/fswp2007p001
Mehta KP (2019) Sustainability in welding and processing. In: Innovations in Manufacturing for Sustainability. Springer International Publishing, pp 125–145
Chen C, Kovacevic R, Jandgric D (2003) Wavelet transform analysis of acoustic emission in monitoring friction stir welding of 6061 aluminum. Int J Mach Tools Manuf 43:1383–1390. https://doi.org/10.1016/S0890-6955(03)00130-5
Yang Y, Kalya P, Landers RG, Krishnamurthy K (2008) Automatic gap detection in friction stir butt welding operations. Int J Mach Tools Manuf 48:1161–1169. https://doi.org/10.1016/j.ijmachtools.2008.01.007
Jene T, Dobmann G, Wagner G, Eifler D (2008) Monitoring of the friction stir welding process to describe parameter effects on joint quality. Mater Sci 5454:1–11. https://doi.org/10.1007/BF03266668
Kumar U, Yadav I, Kumari S, Kumari K, Ranjan N, Kesharwani RK, Jain R, Kumar S, Pal S, Chakravarty D, Pal SK (2015) Defect identification in friction stir welding using discrete wavelet analysis. Adv Eng Softw 85:43–50. https://doi.org/10.1016/j.advengsoft.2015.02.001
Kumari S, Jain R, Kumar U, Yadav I, Ranjan N, Kumari K, Kesharwani RK, Kumar S, Pal S, Pal SK, Chakravarty D (2016) Defect identification in friction stir welding using continuous wavelet transform. J Intell Manuf 30:1–12. https://doi.org/10.1007/s10845-016-1259-1
Fleming PA, Lammlein DH, Wilkes DM, Cook GE, Strauss AM, Delapp DR, Hartman DA (2009) Misalignment detection and enabling of seam tracking for friction stir welding. Sci Technol Weld Join 14:93–96. https://doi.org/10.1179/136217108X372568
Bipul Das, Sukhomay Pal SB (2014) Monitoring of friction stir welding process through signals acquired during the welding. In: 5 th International & 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12th–14th, 2014, IIT Guwahati, Assam, India. pp 1–7
Das B, Bag S, Pal S (2016) Defect detection in friction stir welding process through characterization of signals by fractal dimension. Manuf Lett 7:6–10. https://doi.org/10.1016/j.mfglet.2015.11.006
Mishra D, Roy RB, Dutta S, Pal SK, Chakravarty D (2018) A review on sensor based monitoring and control of friction stir welding process and a roadmap to Industry 4.0. J Manuf Process 36:373–397. https://doi.org/10.1016/j.jmapro.2018.10.016
Soundararajan V, Atharifar H, Kovacevic R (2006) Monitoring and processing the acoustic emission signals from the friction-stir-welding process. Proc Inst Mech Eng Part B J Eng Manuf 220:1673–1685. https://doi.org/10.1243/09544054JEM586
Roy RB, Ghosh A, Bhattacharyya S, Mahto RP, Kumari K, Pal SK, Pal S (2018) Weld defect identification in friction stir welding through optimized wavelet transformation of signals and validation through X-ray micro-CT scan. Int J Adv Manuf Technol 99:623–633. https://doi.org/10.1007/s00170-018-2519-3
Bhat NN, Kumari K, Dutta S, Pal SK, Pal S (2015) Friction stir weld classification by applying wavelet analysis and support vector machine on weld surface images. J Manuf Process 20:274–281. https://doi.org/10.1016/j.jmapro.2015.07.002
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Roy, R.B., Mishra, D., Pal, S.K. et al. Digital twin: current scenario and a case study on a manufacturing process. Int J Adv Manuf Technol 107, 3691–3714 (2020). https://doi.org/10.1007/s00170-020-05306-w
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DOI: https://doi.org/10.1007/s00170-020-05306-w