Digital twin-based sustainable intelligent manufacturing: a review
Source abstract
Abstract
Abstract As the next-generation manufacturing system, intelligent manufacturing enables better quality, higher productivity, lower cost, and increased manufacturing flexibility. The concept of sustainability is receiving increasing attention, and sustainable manufacturing is evolving. The digital twin is an emerging technology used in intelligent manufacturing that can grasp the state of intelligent manufacturing systems in real-time and predict system failures. Sustainable intelligent manufacturing based on a digital twin has advantages in practical applications. To fully understand the intelligent manufacturing that provides the digital twin, this study reviews both technologies and discusses the sustainability of intelligent manufacturing. Firstly, the relevant content of intelligent manufacturing, including intelligent manufacturing equipment, systems, and services, is analyzed. In addition, the sustainability of intelligent manufacturing is discussed. Subsequently, a digital twin and its application are introduced along with the development of intelligent manufacturing based on the digital twin technology. Finally, combined with the current status, the future development direction of intelligent manufacturing is presented.
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Research context
OpenAlex topics: Digital Transformation in Industry · Additive Manufacturing and 3D Printing Technologies · Manufacturing Process and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (100+)
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1. Waschull S, Bokhorst JAC, Molleman E et al (2020) Work design in future industrial production: transforming towards cyber-physical systems. Comput Ind Eng. 139:10567910.1016/j.cie.2019.01.053 ↗
2. Javed Y, Felemban M, Shawly T et al (2019) A partition-driven integrated security architecture for cyber-physical systems. arXiv:190103018[csSY]10.1109/MC.2019.2914906 ↗
3. Van Der Zeeuw A, Van Deursen AJ, Jansen G (2019) Inequalities in the social use of the internet of things: a capital and skills perspective. New Media Soc 21(6):1344–136110.1177/1461444818821067 ↗
4. Habibi M, Fazli M, Movaghar A (2019) Efficient distribution of requests in federated cloud computing environments utilizing statistical multiplexing. Future Gener Comput Syst 90:451–46010.1016/j.future.2018.08.032 ↗
5. Yan H, Yu P, Long D (2019) Study on deep unsupervised learning optimization algorithm based on cloud computing. In: 2019 international conference on intelligent transportation, Big data & smart city (ICITBS), pp 679–68110.1109/ICITBS.2019.00168 ↗
6. He B, Pan QJ, Deng ZQ (2018) Product carbon footprint for product life cycle under uncertainty. J Clean Prod 187:459–47210.1016/j.jclepro.2018.03.246 ↗
7. Radhi H (2019) Multi-objective optimization of turning process during machining of AISI 1025 on CNC machine using multi-objective particle swarm optimization. Univ Thi-Qar J Eng Sci 10(1):65–70
8. He B, Cao XY, Gu ZC (2020) Kinematics of underactuated robotics for product carbon footprint. J Clean Prod 257:12049110.1016/j.jclepro.2020.120491 ↗
9. Chiaverini S, Siciliano B, Egeland O (1994) Review of the damped least-squares inverse kinematics with experiments on an industrial robot manipulator. IEEE Trans Control Syst Technol 2(2):123–13410.1109/87.294335 ↗
10. Altintas Y, Yang J, Kilic ZM (2019) Virtual prediction and constraint of contour errors induced by cutting force disturbances on multi-axis CNC machine tools. CIRP Ann 68(1):377–38010.1016/j.cirp.2019.04.019 ↗
11. Zheng P, Wang HH, Sang ZQ et al (2018) Smart manufacturing systems for Industry 4.0: conceptual framework, scenarios, and future perspectives. Front Mech Eng 13(2):137–15010.1007/s11465-018-0499-5 ↗
12. Tao F, Qi QL, Liu A et al (2018) Data-driven smart manufacturing. J Manuf Syst 48:157–16910.1016/j.jmsy.2018.01.006 ↗
13. Zhong RY, Xu X, Klotz E et al (2017) Intelligent manufacturing in the context of Industry 4.0: a review. Engineering 3(5):616–63010.1016/J.ENG.2017.05.015 ↗
14. Yadav A, Jayswal SC (2017) Modelling of flexible manufacturing system: a review. Int J Prod Res 56(7):2464–248710.1080/00207543.2017.1387302 ↗
15. Li BH, Hou BC, Yu WT et al (2017) Applications of artificial intelligence in intelligent manufacturing: a review. Front Inf Technol Electron Eng 18(1):86–9610.1631/FITEE.1601885 ↗
16. Kusiak A (2017) Smart manufacturing. Int J Prod Res 56(1/2):508–517
17. He B, Hua YC (2017) Feature-based integrated product model for low-carbon conceptual design. J Eng Des 28(6):408–43210.1080/09544828.2017.1316833 ↗
18. Bouzary H, Frank Chen F (2018) Service optimal selection and composition in cloud manufacturing: a comprehensive survey. Int J Adv Manuf Technol 97(1/4):795–80810.1007/s00170-018-1910-4 ↗
19. Bouzary H, Chen FF, Krishnaiyer K (2018) Service matching and selection in cloud manufacturing: a state-of-the-art review. Procedia Manuf 26:1128–113610.1016/j.promfg.2018.07.149 ↗
20. Bauer D, Stock D, Bauernhansl T (2017) Movement towards service-orientation and app-orientation in manufacturing it. Procedia CIRP 62:199–20410.1016/j.procir.2016.06.079 ↗
21. Giret A, Garcia E, Botti V (2016) An engineering framework for service-oriented intelligent manufacturing systems. Comput Ind 81:116–12710.1016/j.compind.2016.02.002 ↗
22. Cao Y, Wang SL, Kang L et al (2015) Study on machining service modes and resource selection strategies in cloud manufacturing. Int J Adv Manuf Technol 81(1/4):597–61310.1007/s00170-015-7222-z ↗
23. He B, Zhang D, Gu ZC et al (2020) Skeleton model-based product low carbon design optimization. J Clean Prod. https://doi.org/10.1016/j.jclepro.2020.12168710.1016/j.jclepro.2020.121687 ↗
24. Grossmann IE (2019) Optimization and management in manufacturing engineering: resource collaborative optimization and management through the internet of things. Optim Methods Softw 34(1):220–22310.1080/10556788.2018.1527332 ↗
25. Soni R, Bhatia M, Singh T (2019) Digital twin: intersection of mind and machine. Int J Comput Intell IoT 2(3):667–670
26. Zheng P, Lin Y, Chen CH et al (2018) Smart, connected open architecture product: an IT-driven co-creation paradigm with lifecycle personalization concerns. Int J Prod Res 57(8):2571–258410.1080/00207543.2018.1530475 ↗
27. Nikolakis N, Alexopoulos K, Xanthakis E et al (2019) The digital twin implementation for linking the virtual representation of human-based production tasks to their physical counterpart in the factory-floor. Int J Comput Integr Manuf 32(1):1–1210.1080/0951192X.2018.1529430 ↗
28. Wang SY, Wan JF, Zhang DQ et al (2016) Towards smart factory for Industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Comput Netw 101:158–16810.1016/j.comnet.2015.12.017 ↗
29. Grieves M (2014) Digital twin: manufacturing excellence through virtual factory replication. White paper, pp 1–7
30. Abdulmotaleb ES (2018) Digital twins: the convergence of multimedia technologies. IEEE Multimed 25(2):87–9210.1109/MMUL.2018.023121167 ↗
31. Negri E, Fumagalli L, Macchi M (2017) A review of the roles of digital twin in CPS-based production systems. Procedia Manuf 11:939–94810.1016/j.promfg.2017.07.198 ↗
32. Chhetri MB, Krishnaswamy S, Loke SW (2004) Smart virtual counterparts for learning communities. In: Bussler C et al (eds) Web information systems—WISE 2004 workshops, WISE 2004. Springer, Berlin, pp 125–134
33. Glaessgen E, Stargel D (2012) The digital twin paradigm for future NASA and U.S. Air force vehicles. In: 2012 53rd AIAA/ASME/ASCE/AHS/ASC Structures, structural dynamics and materials conference, pp 1–14
34. Lee J, Bagheri B, Kao HA (2015) A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manuf Lett 3:18–2310.1016/j.mfglet.2014.12.001 ↗
35. Söderberg R, Wärmefjord K, Carlson JS et al (2017) Toward a digital twin for real-time geometry assurance in individualized production. CIRP Ann 66(1):137–14010.1016/j.cirp.2017.04.038 ↗
36. Bolton RN, McColl-Kennedy JR, Cheung L et al (2018) Customer experience challenges: bringing together digital, physical and social realms. J Serv Manag 29(5):776–80810.1108/JOSM-04-2018-0113 ↗
37. Tao F, Sui FY, Liu A et al (2019) Digital twin-driven product design framework. Int J Prod Res. 57(12):3935–395310.1080/00207543.2018.1443229 ↗
38. Hu LW, Nguyen NT, Tao WJ et al (2018) Modeling of cloud-based digital twins for smart manufacturing with mt connect. Procedia Manuf 26:1193–120310.1016/j.promfg.2018.07.155 ↗
39. Tao F, Cheng JF, Qi QL et al (2017) Digital twin-driven product design, manufacturing and service with big data. Int J Adv Manuf Technol 94(9/12):3563–3576
40. Schleich B, Anwer N, Mathieu L et al (2017) Shaping the digital twin for design and production engineering. CIRP Ann 66(1):141–14410.1016/j.cirp.2017.04.040 ↗
41. Haag S, Anderl R (2018) Digital twin—proof of concept. Manuf Lett 15:64–6610.1016/j.mfglet.2018.02.006 ↗
42. Guo JP, Zhao N, Sun L et al (2018) Modular based flexible digital twin for factory design. J Ambient Intell Hum Comput 10(3):1189–1200
43. Bao JS, Guo DS, Li J et al (2018) The modelling and operations for the digital twin in the context of manufacturing. Enterp Inf Syst 13(4):534–556
44. Cheng Y, Tao F, Xu LD et al (2016) Advanced manufacturing systems: supply-demand matching of manufacturing resource based on complex networks and internet of things. Enterp Inf Syst 12(7):780–79710.1080/17517575.2016.1183263 ↗
45. Bilberg A, Malik AA (2019) Digital twin driven human–robot collaborative assembly. CIRP Ann 68(1):499–50210.1016/j.cirp.2019.04.011 ↗
46. Um J, Weyer S, Quint F (2017) Plug-and-simulate within modular assembly line enabled by digital twins and the use of automation ML. IFAC-PapersOnLine 50(1):15904–1590910.1016/j.ifacol.2017.08.2360 ↗
47. Qi QL, Tao F, Zuo Y et al (2018) Digital twin service towards smart manufacturing. Procedia CIRP 72:237–24210.1016/j.procir.2018.03.103 ↗
48. Leng JW, Zhang H, Yan DX et al (2018) Digital twin-driven manufacturing cyber-physical system for parallel controlling of smart workshop. J Ambient Intell Hum Comput 10(3):1155–116610.1007/s12652-018-0881-5 ↗
49. Cheng Y, Zhang YP, Ji P et al (2018) Cyber-physical integration for moving digital factories forward towards smart manufacturing: a survey. Int J Adv Manuf Technol 97(1/4):1209–122110.1007/s00170-018-2001-2 ↗
50. Meng SH, Tang SL, Zhu YH et al (2019) Digital twin-driven control method for robotic automatic assembly system. IOP Conf Ser Mater Sci Eng 493:01212810.1088/1757-899X/493/1/012128 ↗
51. Xiang F, Zhi Z, Jiang GZ (2018) Digital twins technology and its data fusion in iron and steel product life cycle. In: 2018 IEEE 15th international conference on networking, sensing and control (ICNSC), pp 1–5
52. Zhang H, Liu Q, Chen X et al (2017) A digital twin-based approach for designing and multi-objective optimization of hollow glass production line. IEEE Access 5:26901–2691110.1109/ACCESS.2017.2766453 ↗
53. Zhuang CB, Liu JH, 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(1/4):1149–116310.1007/s00170-018-1617-6 ↗
54. Liu Q, Zhang H, Leng JW et al (2018) Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system. Int J Prod Res 57:3903–391910.1080/00207543.2018.1471243 ↗
55. Liau Y, Lee H, Ryu K (2018) Digital twin concept for smart injection molding. IOP Conf Ser Mater Sci Eng 324:01207710.1088/1757-899X/324/1/012077 ↗
56. Botkina D, Hedlind M, Olsson B et al (2018) Digital twin of a cutting tool. Procedia CIRP 72:215–21810.1016/j.procir.2018.03.178 ↗
57. Pairet È, Ardón P, Liu X et al (2019) A digital twin for human–robot interaction. In: 2019 14th ACM/IEEE international conference on human-robot interaction (HRI), pp 372-37210.1109/HRI.2019.8673015 ↗
58. Voinov N, Chernorutsky I, Drobintsev P et al (2017) An approach to net-centric control automation of technological processes within industrial IoT systems. Adv Manuf 5(4):388–39310.1007/s40436-017-0195-4 ↗
59. Uhlemann THJ, Schock C, Lehmann C et al (2017) The digital twin: demonstrating the potential of real time data acquisition in production systems. Procedia Manuf 9:113–12010.1016/j.promfg.2017.04.043 ↗
60. Coronado PDU, Lynn R, Louhichi W et al (2018) Part data integration in the shop floor digital twin: mobile and cloud technologies to enable a manufacturing execution system. J Manuf Syst 48:25–3310.1016/j.jmsy.2018.02.002 ↗
61. Schluse M, Priggemeyer M, Atorf L et al (2018) Experimentable digital twins—streamlining simulation-based systems engineering for Industry 4.0. IEEE Trans Ind Inf 14(4):1722–173110.1109/TII.2018.2804917 ↗
62. Macchi M, Roda I, Negri E et al (2018) Exploring the role of digital twin for asset lifecycle management. IFAC-PapersOnLine 51(11):790–79510.1016/j.ifacol.2018.08.415 ↗
63. Kunath M, Winkler H (2018) Integrating the digital twin of the manufacturing system into a decision support system for improving the order management process. Procedia CIRP 72:225–23110.1016/j.procir.2018.03.192 ↗
64. Biesinger F, Meike D, Kraß B et al (2018) A case study for a digital twin of body-in-white production systems general concept for automated updating of planning projects in the digital factory. In: 2018 IEEE 23rd international conference on emerging technologies and factory automation (ETFA), pp 19–2610.1109/ETFA.2018.8502467 ↗
65. Vachálek J, Bartalský L, Rovný O et al (2017) The digital twin of an industrial production line within the Industry 4.0 concept. In: 2017 21st international conference on process control (PC), pp 258–26210.1109/PC.2017.7976223 ↗
66. Uhlemann THJ, Lehmann C, Steinhilper R (2017) The digital twin: realizing the cyber-physical production system for Industry 4.0. Procedia CIRP 61:335–34010.1016/j.procir.2016.11.152 ↗
67. Tao F, Zhang M (2017) Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing. IEEE Access 5:20418–2042710.1109/ACCESS.2017.2756069 ↗
68. He B, Deng ZQ, Huang S et al (2014) Application of unascertained number for the integration of carbon footprint in conceptual design. Proc Inst Mech Eng J Eng Manuf 229(11):2088–209210.1177/0954405414539495 ↗
69. Martinez HV, Neely AD, Ouyang A et al (2019) Service business model innovation: the digital twin technology. EurOMA. https://doi.org/10.17863/CAM.3548210.17863/CAM.35482 ↗
70. Liu Y, Zhang L, Yang Y et al (2019) A novel cloud-based framework for the elderly healthcare services using digital twin. IEEE Access 7:49088–4910110.1109/ACCESS.2019.2909828 ↗
71. Balachandar S, Chinnaiyan R (2019) Reliable digital twin for connected footballer. In: International conference on computer networks and communication technologies. pp 185–19110.1007/978-981-10-8681-6_18 ↗
72. Guivarch D, Mermoz E, Marino Y et al (2019) Creation of helicopter dynamic systems digital twin using multibody simulations. CIRP Ann 68(1):133–13610.1016/j.cirp.2019.04.041 ↗
73. Tao F, Zhang M, Liu YS et al (2018) Digital twin driven prognostics and health management for complex equipment. CIRP Ann 67(1):169–17210.1016/j.cirp.2018.04.055 ↗
74. Lynn R, Chen A, Locks S et al (2015) Intelligent and accessible data flow architectures for manufacturing system optimization. In: Umeda S, Nakano M, Mizuyama H et al (eds) Advances in production management systems: innovative production management towards sustainable growth. Springer, Berlin, pp 27–35
75. Luo W, Hu T, Zhang C et al (2018) Digital twin for CNC machine tool: modeling and using strategy. J Ambient Intell Hum Comput 10(3):1129–114010.1007/s12652-018-0946-5 ↗
76. Li CZ, Mahadevan S, Ling Y et al (2017) Dynamic bayesian network for aircraft wing health monitoring digital twin. AIAA J 55(3):930–94110.2514/1.J055201 ↗
77. Kaebernick H, Kara S, Sun M (2003) Sustainable product development and manufacturing by considering environmental requirements. Robot Comput Integr Manuf 19(6):461–46810.1016/S0736-5845(03)00056-5 ↗
78. Rusinko C (2007) Green manufacturing: an evaluation of environmentally sustainable manufacturing practices and their impact on competitive outcomes. IEEE Trans Eng Manag 54(3):445–45410.1109/TEM.2007.900806 ↗
79. Miller G, Pawloski J, Standridge CR (2010) A case study of lean, sustainable manufacturing. J Ind Eng Manag 3(1):11–32
80. Akbar M, Irohara T (2018) Scheduling for sustainable manufacturing: a review. J Clean Prod 205:866–88310.1016/j.jclepro.2018.09.100 ↗
81. Carvalho N, Chaim O, Cazarini E et al (2018) Manufacturing in the fourth industrial revolution: a positive prospect in sustainable manufacturing. Procedia Manuf 21:671–67810.1016/j.promfg.2018.02.170 ↗
82. Gbededo MA, Liyanage K, Garza-Reyes JA (2018) Towards a life cycle sustainability analysis: a systematic review of approaches to sustainable manufacturing. J Clean Prod 184:1002–101510.1016/j.jclepro.2018.02.310 ↗
83. Koren Y, Gu X, Badurdeen F et al (2018) Sustainable living factories for next generation manufacturing. Procedia Manuf 21:26–3610.1016/j.promfg.2018.02.091 ↗
84. Stoycheva S, Marchese D, Paul C et al (2018) Multi-criteria decision analysis framework for sustainable manufacturing in automotive industry. J Clean Prod 187:257–27210.1016/j.jclepro.2018.03.133 ↗
85. Moldavska A, Welo T (2019) A holistic approach to corporate sustainability assessment: incorporating sustainable development goals into sustainable manufacturing performance evaluation. J Manuf Syst 50:53–6810.1016/j.jmsy.2018.11.004 ↗
86. Physicsweb. http://www.Miit.Gov.Cn/n1146295/n1652858/n1652930/n3757016/c6429243/content.Html. Accessed 14 Aug 2018
87. Stock T, Seliger G (2016) Opportunities of sustainable manufacturing in Industry 4.0. Procedia CIRP 40:536–54110.1016/j.procir.2016.01.129 ↗
88. Garetti M, Taisch M (2012) Sustainable manufacturing: trends and research challenges. Prod Plan Control 23(2/3):83–10410.1080/09537287.2011.591619 ↗
89. He B, Zhou GF, Hou SC et al (2016) An approach to computational co-evolutionary product design. Int J Adv Manuf Technol 90(1/4):249–265
90. Kang HS, Lee JY, Choi S et al (2016) Smart manufacturing: past research, present findings, and future directions. Int J Precis Eng Manuf Green Technol 3(1):111–12810.1007/s40684-016-0015-5 ↗
91. Walshe K, McKee M, McCarthy M et al (2013) Health systems and policy research in Europe: Horizon 2020. Lancet 382(9893):668–66910.1016/S0140-6736(12)62195-3 ↗
92. Hemphill TA (2014) Policy debate: the US advanced manufacturing initiative: will it be implemented as an innovation—or industrial—policy? Innovation 16(1):67–7010.5172/impp.2014.16.1.67 ↗
93. Lewis P, Bell K (2019) Understanding the UK’s productivity problems: new technological solutions or a case for the renewal of old institutions? Employ Relat Int J 41(2):296–31210.1108/ER-10-2018-0273 ↗
94. Radanliev P, De Roure D, Nurse JR et al (2019) New developments in cyber physical systems, the internet of things and the digital economy—discussion on future developments in the industrial internet of things and Industry 4.0. Preprints 2019, 2019030094
95. Yeung G (2019) ‘Made in China 2025’: the development of a new energy vehicle industry in China. Area Dev Policy 4(1):39–5910.1080/23792949.2018.1505433 ↗
96. Zhou GH, Zhang C, Li Z et al (2019) Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing. Int J Prod Res 58:1034–105110.1080/00207543.2019.1607978 ↗
97. Lohtander M, Ahonen N, Lanz M et al (2018) Micro manufacturing unit and the corresponding 3D-model for the digital twin. Procedia Manuf 25:55–6110.1016/j.promfg.2018.06.057 ↗
98. Zhang YZ, Mu LM, Shen GX et al (2019) Fault diagnosis strategy of CNC machine tools based on cascading failure. J Intell Manuf 30(5):2193–220210.1007/s10845-017-1382-7 ↗
99. Villalonga A, Beruvides G, Castaño F et al (2018) Condition-based monitoring architecture for CNC machine tools based on global knowledge. IFAC-PapersOnLine 51(11):200–20410.1016/j.ifacol.2018.08.259 ↗
100. Yang HT, Li L, Zhang SD et al (2019) Critical point determination method of thermal-force coupling deformation of CNC machine tool bed. In: Tenth international symposium on precision engineering measurements and instrumentation, pp 110532510.1117/12.2511744 ↗