Open Access
Review
Review of Digital Twin in the Automotive Industry on Products, Processes and Systems
Heli Liu1, 2
, Benjamin Zhang1
, Vincent Wu1, 2
, Xiao Yang1, 2
, Liliang Wang1, 2, *
Author Information
Submitted: 17 Feb 2025 | Revised: 10 Mar 2025 | Accepted: 20 Mar 2025 | Published: 24 Mar 2025
Abstract
In the era of digital manufacturing, digital technologies are rapidly revolutionising the automotive industry. Among these, the digital twin, an enabling industry 4.0 digital technology first introduced two decades ago, is characterised by the seamless integration of physical and cyber realms. The digital twin is undergoing extensive investigations within the automotive sector, covering various perspectives including design, manufacturing, and application. By leveraging the big manufacturing data captured by spatially distributed sensing networks, the digital twin shows the capacity to create high-fidelity models of actual manufacturing practices, thereby significantly improving the precision and efficiency of production processes. Integrated with other digital technologies such as big data analytics (BDA) and the Internet of Things (IoT), the digital twin mirrors components in the physical world into the virtual environment and facilitates the exchange of real-time information to achieve fully converged cyber-physical spaces. This in turn minimises costs and improves the overall product quality, flexibility of manufacturing processes, and system integration. This work reviewed recent advancements in digital twin applications in the automotive industry focusing on automotive products, manufacturing processes, and manufacturing systems. Insights were provided into the future of digitally enhanced technologies in automotive manufacturing towards digital manufacturing and developing digital product passports (DPPs) for circular economy (CE).
Keywords
References
1.
Liu, H.; Saksham, D.; Shen, M.; Chen, K.; Wu, V.; Wang, L. Industry 4.0 in metal forming industry towards automotive applications: A review. Int. J. Automot. Manuf. Mater. 2022, 1, 2. https://doi.org/10.53941/ijamm0101002.
2.
Sepasgozar, S.M.E.; Shi, A.; Yang, L.; Shirowzhan, S.; Edwards, D.J. Additive Manufacturing Applications for Industry 4.0: A Systematic Critical Review. Buildings 2020, 10, 231. https://doi.org/10.3390/buildings10120231.
3.
Weber, P.; Hiller, S.; Lasi, H. Design and Evaluation of an Approach to Generate Cross-Domain Value Scenarios in the Context of the Industrial Internet of Things: A Capability-Based Approach. In Proceedings of the 2019 Portland International Conference on Management of Engineering and Technology (PICMET), Portland, OR, USA, 25–29 August 2019; pp. 1–8. https://doi.org/10.23919/PICMET.2019.8893687.
4.
Li, L. China’s manufacturing locus in 2025: With a comparison of “Made-in-China 2025” and “Industry 4.0”. Technol. Forecast. Soc. Change 2018, 135, 66–74. https://doi.org/10.1016/j.techfore.2017.05.028.
5.
Aquilani, B.; Piccarozzi, M.; Abbate, T.; Codini, A. The Role of Open Innovation and Value Co-creation in the Challenging Transition from Industry 4.0 to Society 5.0: Toward a Theoretical Framework. Sustainability 2020, 12, 8943. https://doi.org/10.3390/su12218943.
6.
Tao, F.; Qi, Q. Make more digital twins. Nature 2019, 573, 490–491. https://doi.org/10.1038/d41586-019-02849-1.
7.
VanDerHorn, E.; Mahadevan, S. Digital Twin: Generalization, characterization and implementation. Decis. Support Syst. 2021, 145, 113524. https://doi.org/10.1016/j.dss.2021.113524.
8.
Gligoric, N.; Krco, S.; Hakola, L.; Vehmas, K.; De, S.; Moessner, K.; Van Kranenburg, R. SmartTags: IoT Product Passport for Circular Economy Based on Printed Sensors and Unique Item-Level Identifiers. Sensors 2019, 19, 586. https://doi.org/10.3390/s19030586.
9.
Mulhall, D.; Ayed, A.-C.; Schroeder, J.; Hansen, K.; Wautelet, T. The Product Circularity Data Sheet—A Standardized Digital Fingerprint for Circular Economy Data about Products. Energies 2022, 15, 3397. https://doi.org/10.3390/en15093397.
10.
Haghnegahdar, L.; Joshi, S.S.; Dahotre, N.B. From IoT-based cloud manufacturing approach to intelligent additive manufacturing: Industrial Internet of Things—An overview. Int. J. Adv. Manuf. Technol. 2022, 119, 1461–1478. https://doi.org/10.1007/s00170-021-08436-x.
11.
Sisinni, E.; Saifullah, A.; Han, S.; Jennehag, U.; Gidlund, M. Industrial Internet of Things: Challenges, Opportunities, and Directions. IEEE Trans. Ind. Inform. 2018, 14, 4724–4734. https://doi.org/10.1109/TII.2018.2852491.
12.
Tao, F.; Qi, Q.; Wang, L.; Nee, A.Y.C. Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison. Engineering 2019, 5, 653–661. https://doi.org/10.1016/j.eng.2019.01.014.
13.
Leitão, P.; Colombo, A.W.; Karnouskos, S. Industrial automation based on cyber-physical systems technologies: Prototype implementations and challenges. Comput. Ind. 2016, 81, 11–25. https://doi.org/10.1016/j.compind.2015.08.004.
14.
Babiceanu, R.F.; Seker, R. Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook. Comput. Ind. 2016, 81, 128–137. https://doi.org/10.1016/j.compind.2016.02.004.
15.
Bustillo, A.; Pimenov, D.Y.; Mia, M.; Kapłonek, W. Machine-learning for automatic prediction of flatness deviation considering the wear of the face mill teeth. J. Intell. Manuf. 2021, 32, 895–912. https://doi.org/10.1007/s10845-020-01645-3.
16.
Wuest, T.; Weimer, D.; Irgens, C.; Thoben, K.-D. Machine learning in manufacturing: Advantages, challenges, and applications. Prod. Manuf. Res. 2016, 4, 23–45. https://doi.org/10.1080/21693277.2016.1192517.
17.
Monostori, L. AI and machine learning techniques for managing complexity, changes and uncertainties in manufacturing. Eng. Appl. Artif. Intell. 2003, 16, 277–291. https://doi.org/10.1016/S0952-1976(03)00078-2.
18.
Kubik, C.; Knauer, S.M.; Groche, P. Smart sheet metal forming: Importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting wear states during blanking. J. Intell. Manuf. 2022, 33, 259–282. https://doi.org/10.1007/s10845-021-01789-w.
19.
Belhadi, A.; Zkik, K.; Cherrafi, A.; Yusof, S.M.; El fezazi, S. Understanding Big Data Analytics for Manufacturing Processes: Insights from Literature Review and Multiple Case Studies. Comput. Ind. Eng. 2019, 137, 106099. https://doi.org/10.1016/j.cie.2019.106099.
20.
Dai, H.-N.; Wang, H.; Xu, G.; Wan, J.; Imran, M. Big data analytics for manufacturing internet of things: Opportunities, challenges and enabling technologies. Enterp. Inf. Syst. 2020, 14, 1279–1303. https://doi.org/10.1080/17517575.2019.1633689.
21.
Helo, P.; Phuong, D.; Hao, Y. Cloud manufacturing–Scheduling as a service for sheet metal manufacturing. Comput. Oper. Res. 2019, 110, 208–219. https://doi.org/10.1016/j.cor.2018.06.002.
22.
Xu, X. From cloud computing to cloud manufacturing. Robot. Comput.-Integr. Manuf. 2012, 28, 75–86. https://doi.org/10.1016/j.rcim.2011.07.002.
23.
Zhong, R.Y.; Xu, X.; Klotz, E.; Newman, S.T. Intelligent Manufacturing in the Context of Industry 4.0: A Review. Engineering 2017, 3, 616–630. https://doi.org/10.1016/J.ENG.2017.05.015.
24.
Gan, L.; Li, L.; Huang, H. Digital Twin-Driven Sheet Metal Forming: Modeling and Application for Stamping Considering Mold Wear. J. Manuf. Sci. Eng. 2022, 144, 121003. https://doi.org/10.1115/1.4054902.
25.
Piascik, B.; Vickers, J.; Lowry, D.; Scotti, S.; Stewart, J.; Calomino, A. Materials, Structures, Mechanical Systems, and Manufacturing Roadmap. NASA. 2012. Available online: https://www.researchgate.net/profile/Rai-Waqas-Khan/post/can_you_share_the_manufacturing_process_and_materials_required_for_deployable_composite_boom_for_solar_arraysand_process_too/attachment/5a71d9dc4cde266d5887b8b1/AS%3A588877643395072%401517410779959/download/501625main_TA12-ID_rev6_NRC-wTASR.pdf (accessed on 27 February 2024).
26.
Tao, F.; Zhang, H.; Liu, A.; Nee, A.Y.C. Digital Twin in Industry: State-of-the-Art. IEEE Trans. Ind. Inform. 2019, 15, 2405–2415. https://doi.org/10.1109/TII.2018.2873186.
27.
Wang, Z.; Gupta, R.; Han, K.; Wang, H.; Ganlath, A.; Ammar, N.; Tiwari, P. Mobility Digital Twin: Concept, Architecture, Case Study, and Future Challenges. IEEE Internet Things J. 2022, 9, 17452–17467. https://doi.org/10.1109/JIOT.2022.3156028.
28.
Lu, S.C.-Y.; Li, D.; Cheng, J.; Wu, C.L. A Model Fusion Approach to Support Negotiations during Complex Engineering System Design. CIRP Ann. 1997, 46, 89–92. https://doi.org/10.1016/S0007-8506(07)60782-3.
29.
Uhlemann, T.H.-J.; Lehmann, C.; Steinhilper, R. The Digital Twin: Realizing the Cyber-Physical Production System for Industry 4.0′. Procedia CIRP 2017, 61, 335–340. https://doi.org/10.1016/j.procir.2016.11.152.
30.
Piromalis, D.; Kantaros, A. Digital Twins in the Automotive Industry: The Road toward Physical-Digital Convergence. Appl. Syst. Innov. 2022, 5, 65. https://doi.org/10.3390/asi5040065.
31.
Gabor, T.; Belzner, L.; Kiermeier, M.; Beck, M.T.; Neitz, A. A Simulation-Based Architecture for Smart Cyber-Physical Systems. In Proceedings of the 2016 IEEE International Conference on Autonomic Computing (ICAC), Wuerzburg, Germany, 17–22 July 2016; pp. 374–379. https://doi.org/10.1109/ICAC.2016.29.
32.
Weyer, S.; Meyer, T.; Ohmer, M.; Gorecky, D.; Zühlke, D. Future Modeling and Simulation of CPS-based Factories: An Example from the Automotive Industry. IFAC-Pap. 2016, 49, 97–102. https://doi.org/10.1016/j.ifacol.2016.12.168.
33.
Qi, Q.; Tao, F. Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison. IEEE Access 2018, 6, 3585–3593. https://doi.org/10.1109/ACCESS.2018.2793265.
34.
Tao, F.; Cheng, J.; Qi, Q.; Zhang, M.; Zhang, H.; Sui, F. Digital twin-driven product design, manufacturing and service with big data. Int. J. Adv. Manuf. Technol. 2018, 94, 3563–3576. https://doi.org/10.1007/s00170-017-0233-1.
35.
Tao, F.; Zhang, M.; Liu, Y.; Nee, A.Y.C. Digital twin driven prognostics and health management for complex equipment. CIRP Ann. 2018, 67, 169–172. https://doi.org/10.1016/j.cirp.2018.04.055.
36.
Liu, M.; Fang, S.; Dong, H.; Xu, C. Review of digital twin about concepts, technologies, and industrial applications. J. Manuf. Syst. 2021, 58, 346–361. https://doi.org/10.1016/j.jmsy.2020.06.017.
37.
PricewaterhouseCoopers. Annual Manufacturing Report 2020. 2020. Available online: https://www.pwc.co.uk/industries/manufacturing/insights/annual-manufacturing-report.html (accessed on 2 August 2022).
38.
Lo, C.K.; Chen, C.H.; Zhong, R.Y. A review of digital twin in product design and development. Adv. Eng. Inform. 2021, 48, 101297. https://doi.org/10.1016/j.aei.2021.101297.
39.
Grieves, M.; Vickers, J. Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. In Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches; Kahlen, F.-J., Flumerfelt, S., Alves, A., Eds.; Springer International Publishing: Cham, Switzerland, 2017; pp. 85–113. https://doi.org/10.1007/978-3-319-38756-7_4.
40.
Xie, G.; Yang, K.; Xu, C.; Li, R.; Hu, S. Digital Twinning Based Adaptive Development Environment for Automotive Cyber-Physical Systems. IEEE Trans. Ind. Inform. 2022, 18, 1387–1396. https://doi.org/10.1109/TII.2021.3064364.
41.
Deng, S.; Ling, L.; Zhang, C.; Li, C.; Zeng, T.; Zhang, K.; Guo, G. A systematic review on the current research of digital twin in automotive application. Internet Things Cyber-Phys. Syst. 2023, 3, 180–191. https://doi.org/10.1016/j.iotcps.2023.04.004.
42.
Söderberg, R.; Wärmefjord, K.; Carlson, J.S.; Lindkvist, L. Toward a Digital Twin for real-time geometry assurance in individualized production. CIRP Ann. 2017, 66, 137–140. https://doi.org/10.1016/j.cirp.2017.04.038.
43.
Botín-Sanabria, D.M.; Mihaita, A.-S.; Peimbert-García, R.E.; Ramírez-Moreno, M.A.; Ramírez-Mendoza, R.A.; Lozoya-Santos, J.D.J. Digital Twin Technology Challenges and Applications: A Comprehensive Review. Remote Sens. 2022, 14, 1335. https://doi.org/10.3390/rs14061335.
44.
Qi, Q.; Tao, F.; Hu, T.; Anwer, N.; Liu, A.; Wei, Y.; Nee, A.Y. Enabling technologies and tools for digital twin. J. Manuf. Syst. 2021, 58, 3–21. https://doi.org/10.1016/j.jmsy.2019.10.001.
45.
El Fakir, O.; Wang, L.; Balint, D.; Dear, J.P.; Lin, J.; Dean, T.A. Numerical study of the solution heat treatment, forming, and in-die quenching (HFQ) process on AA5754. Int. J. Mach. Tools Manuf. 2014, 87, 39–48. https://doi.org/10.1016/j.ijmachtools.2014.07.008.
46.
Wang, K.; Kopec, M.; Chang, S.; Qu, B.; Liu, J.; Politis, D.J.; Liu, G. Enhanced formability and forming efficiency for two-phase titanium alloys by Fast light Alloys Stamping Technology (FAST). Mater. Des. 2020, 194, 108948. https://doi.org/10.1016/j.matdes.2020.108948.
47.
Li, H.; Li, B.; Liu, G.; Wen, X.; Wang, H.; Wang, X.; Yang, W. A detection and configuration method for welding completeness in the automotive body-in-white panel based on digital twin. Sci. Rep. 2022, 12, 7929. https://doi.org/10.1038/s41598-022-11440-0.
48.
Zheng, K.; Dong, Y.; Zheng, J.H.; Foster, A.; Lin, J.; Dong, H.; Dean, T.A. The effect of hot form quench (HFQ®) conditions on precipitation and mechanical properties of aluminium alloys. Mater. Sci. Eng. A 2019, 761, 138017. https://doi.org/10.1016/j.msea.2019.06.027.
49.
Zhang, Q.; Luan, X.; Dhawan, S.; Politis, D.J.; Du, Q.; Fu, M.W.; Wang, L. Development of the post-form strength prediction model for a high-strength 6xxx aluminium alloy with pre-existing precipitates and residual dislocations. Int. J. Plast. 2019, 119, 230–248. https://doi.org/10.1016/j.ijplas.2019.03.013.
50.
Sun, Y.; Wang, K.; Politis, D.J.; Chen, G.; Wang, L. An experimental investigation on the ductility and post-form strength of a martensitic steel in a novel warm stamping process. J. Mater. Process. Technol. 2020, 275, 116387. https://doi.org/10.1016/j.jmatprotec.2019.116387.
51.
Zhou, C.; Zhang, F.; Wei, B.; Lin, Y.; He, K.; Du, R. Digital twin–based stamping system for incremental bending. Int. J. Adv. Manuf. Technol. 2021, 116, 389–401. https://doi.org/10.1007/s00170-021-07422-7.
52.
Wang, Q.; Jiao, W.; Zhang, Y. Deep learning-empowered digital twin for visualized weld joint growth monitoring and penetration control. J. Manuf. Syst. 2020, 57, 429–439. https://doi.org/10.1016/j.jmsy.2020.10.002.
53.
Liu, C.; Le Roux, L.; Körner, C.; Tabaste, O.; Lacan, F.; Bigot, S. Digital Twin-enabled Collaborative Data Management for Metal Additive Manufacturing Systems. J. Manuf. Syst. 2022, 62, 857–874. https://doi.org/10.1016/j.jmsy.2020.05.010.
54.
Chantzis, D.; Tracy, M.; Liu, H.; Politis, D.J.; Fu, M.W.; Wang, L. Design Optimization of Hot Stamping Tooling produced by Additive Manufacturing. Addit. Manuf. 2023, 74, 103728. https://doi.org/10.1016/j.addma.2023.103728.
55.
Phua, A.; Davies, C.H.J.; Delaney, G.W. A digital twin hierarchy for metal additive manufacturing. Comput. Ind. 2022, 140, 103667. https://doi.org/10.1016/j.compind.2022.103667.
56.
Gunasegaram, D.R.; Murphy, A.B.; Barnard, A.; DebRoy, T.; Matthews, M.J.; Ladani, L.; Gu, D. Towards developing multiscale-multiphysics models and their surrogates for digital twins of metal additive manufacturing. Addit. Manuf. 2021, 46, 102089. https://doi.org/10.1016/j.addma.2021.102089.
57.
Dilberoglu, U.M.; Gharehpapagh, B.; Yaman, U.; Dolen, M. The Role of Additive Manufacturing in the Era of Industry 4.0. Procedia Manuf. 2017, 11, 545–554. https://doi.org/10.1016/j.promfg.2017.07.148.
58.
Masinelli, G.; Shevchik, S.A.; Pandiyan, V.; Quang-Le, T.; Wasmer, K. Artificial Intelligence for Monitoring and Control of Metal Additive Manufacturing. In Industrializing Additive Manufacturing; Meboldt, M., Klahn, C., Eds.; Springer International Publishing: Cham, Switzerland, 2021; pp. 205–220. https://doi.org/10.1007/978-3-030-54334-1_15.
59.
Elambasseril, J.; Brandt, M. Artificial intelligence: Way forward to empower metal additive manufacturing product development–an overview. Mater. Today Proc. 2022, 58, 61–465. https://doi.org/10.1016/j.matpr.2022.02.485.
60.
Wang, C.; Tan, X.P.; Tor, S.B.; Lim, C.S. Machine learning in additive manufacturing: State-of-the-art and perspectives. Addit. Manuf. 2020, 36, 101538. https://doi.org/10.1016/j.addma.2020.101538.
61.
Rathore, M.M.; Shah, S.A.; Shukla, D.; Bentafat, E.; Bakiras, S. The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities. IEEE Access 2021, 9, 32030–32052. https://doi.org/10.1109/ACCESS.2021.3060863.
62.
Vachálek, J.; Bartalský, L.; Rovný, O.; Šišmišová, D.; Morháč, M.; Lokšík, M. The digital twin of an industrial production line within the industry 4.0 concept. In Proceedings of the 2017 21st International Conference on Process Control (PC), Strbske Pleso, Slovakia, 6–9 June 2017; pp. 258–262. https://doi.org/10.1109/PC.2017.7976223.
63.
Bhatti, G.; Mohan, H.; Singh, R.R. Towards the future of smart electric vehicles: Digital twin technology. Renew. Sustain. Energy Rev. 2021, 141, 110801, May. https://doi.org/10.1016/j.rser.2021.110801.
64.
Son, Y.H.; Park, K.T.; Lee, D.; Jeon, S.W.; Noh, S.D. Digital twin–based cyber-physical system for automotive body production lines. Int. J. Adv. Manuf. Technol. 2021, 115, 291–310. https://doi.org/10.1007/s00170-021-07183-3.
65.
Mendi, A.F. A Digital Twin Case Study on Automotive Production Line. Sensors 2022, 22, 6963. https://doi.org/10.3390/s22186963.
66.
Zhang, Q.; Shen, S.; Li, H.; Cao, W.; Tang, W.; Jiang, J.; Liu, S. Digital twin-driven intelligent production line for automotive MEMS pressure sensors. Adv. Eng. Inform. 2022, 54, 101779. https://doi.org/10.1016/j.aei.2022.101779.
67.
Xu, Y.; Sun, Y.; Liu, X.; Zheng, Y. A Digital-Twin-Assisted Fault Diagnosis Using Deep Transfer Learning. IEEE Access 2019, 7, 19990–19999. https://doi.org/10.1109/ACCESS.2018.2890566.
68.
Biesinger, F.; Meike, D.; Kraß, B.; Weyrich, M. 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 Proceedings of the 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA), Turin, Italy, 4–7 September 2018; pp. 19–26. https://doi.org/10.1109/ETFA.2018.8502467.
69.
Biesinger, F.; Kraß, B.; Weyrich, M. A Survey on the Necessity for a Digital Twin of Production in the Automotive Industry. In Proceedings of the 2019 23rd International Conference on Mechatronics Technology (ICMT), Salerno, Italy, 23–26 October 2019; pp. 1–8. https://doi.org/10.1109/ICMECT.2019.8932144.
70.
Voulgaridis, K.; Lagkas, T.; Angelopoulos, C.M.; Boulogeorgos, A.-A.A.; Argyriou, V.; Sarigiannidis, P. Digital product passports as enablers of digital circular economy: A framework based on technological perspective. Telecommun. Syst. 2024, 85, 699–715. https://doi.org/10.1007/s11235-024-01104-x.
71.
Proposal for a Regulation of the European Parliament and of the Council Establishing a Framework for Setting Ecodesign Requirements for Sustainable Products and Repealing Directive 2009/125/EC; European Commission: Brussels, Belgium, 2022. Available online: https://scholar.google.com/scholar_lookup?title=Proposal+for+a+Regulation+of+the+European+Parliament+and+of+the+Council+Establishing+a+Framework+for+Setting+Ecodesign+Requirements+for+Sustainable+Products+and+Repealing+Directive+2009125EC&publication_year=2022&author=European+Commission&inst=4887157489608331491 (accessed on 27 February 2024).
72.
Berger, K.; Schöggl, J.-P.; Baumgartner, R.J. Concept of a digital product passport for an electric vehicle battery. Presented at the Resource Efficient Vehicles Conference, Graz, Austria, 14–16 June 2021. Available online: http://axaco.s3.amazonaws.com/uploads/2021/05/17/uqYgqc6e/rev2021-048.pdf (accessed on 27 February 2024).
73.
Ruah, C.; Simeone, O.; Al-Hashimi, B.M. A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems. IEEE J. Sel. Areas Commun. 2023, 41, 3146–3160. https://doi.org/10.1109/JSAC.2023.3310093.
74.
Dogan, A.; Birant, D. Machine learning and data mining in manufacturing. Expert Syst. Appl. 2021, 166, 114060. https://doi.org/10.1016/j.eswa.2020.114060.
75.
Deloitte Survey on AI Adoption in Manufacturing. 2020. Available online: https://www2.deloitte.com/cn/en/pages/consumer-industrial-products/articles/ai-manufacturing-application-survey.html (accessed on 13 December 2022).
76.
Liu, S.; Xia, Y.; Liu, Y.; Shi, Z.; Yu, H.; Li, Z.; Lin, J. Tool path planning of consecutive free-form sheet metal stamping with deep learning. J. Mater. Process. Technol. 2022, 303, 117530. https://doi.org/10.1016/j.jmatprotec.2022.117530.
77.
Wilkinson, M.D.; Dumontier, M.; Jan Aalbersberg, I.J.; Appleton, G.; Axton, M.; Baak, A.; Blomberg, N.; Boiten, J.-W.; da Silva Santos, L.B.; Bourne, P.E.; et al. The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 2016, 3, 160018. https://doi.org/10.1038/sdata.2016.18.
78.
Zhou, D.; Yuan, X.; Gao, H.; Wang, A.; Liu, J.; El Fakir, O.; Lin, J. Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes. J. Vis. Exp. 2016, 118, e53957. https://doi.org/10.3791/53957.
79.
Barker, M.; Chue Hong, N.P.; Katz, D.S.; Lamprecht, A.L.; Martinez-Ortiz, C.; Psomopoulos, F.; Honeyman, T. Introducing the FAIR Principles for research software. Sci. Data 2022, 9, 622. https://doi.org/10.1038/s41597-022-01710-x.
80.
Wang, A.; Liu, J.; Gao, H.; Wang, L.-L.; Masen, M. Hot stamping of AA6082 tailor welded blanks: Experiments and knowledge-based cloud–finite element (KBC-FE) simulation. J. Mater. Process. Technol. 2017, 250, 228–238. https://doi.org/10.1016/j.jmatprotec.2017.07.025.
81.
Wang, A. Multi-Objective Finite Element Simulations of a Sheet Metal Forming Process via a Knowledge Based Cloud Simulation Platform; Imperial College London: London, UK, 2018. Available online: http://spiral.imperial.ac.uk/handle/10044/1/86263 (accessed on 23 June 2022).
82.
Dhawan, S.; El Fakir, O.; Wang, L. An Online Database for Hosting and Executing Numerical Models. U.S. Patent 201911116210.6, 25 February 2020.
83.
Zhu, M.; Lim, Y.C.; Cai, Z.; Liu, X.; Dhawan, S.; Liu, J.; Politis, D.J. Cloud FEA of hot stamping processes using a software agnostic platform. Int. J. Adv. Manuf. Technol. 2021, 112, 3445–3458. https://doi.org/10.1007/s00170-020-06533-x.
84.
Dhawan, S. Development of a Cloud FEA Platform for Advanced FE Simulations of Metal Forming Processes; Imperial College London: London, UK, 2022.
85.
Yang, X.; Liu, H.; Zhang, L.; Hu, Y.; Politis, D.J.; Gharbi, M.M.; Wang, L. Interactive mechanism and friction modelling of transient tribological phenomena in metal forming processes: A review. Friction 2024, 12, 375–395. https://doi.org/10.1007/s40544-023-0751-9.
86.
Kouhizadeh, M.; Zhu, Q.; Sarkis, J. Blockchain and the circular economy: Potential tensions and critical reflections from practice. Prod. Plan. Control 2020, 31, 950–966. https://doi.org/10.1080/09537287.2019.1695925.
87.
Kumar, N.M.; Chopra, S.S. Leveraging Blockchain and Smart Contract Technologies to Overcome Circular Economy Implementation Challenges. Sustainability 2022, 14, 9492. https://doi.org/10.3390/su14159492.
88.
Adisorn, T.; Tholen, L.; Götz, T. Towards a Digital Product Passport Fit for Contributing to a Circular Economy. Energies 2021, 14, 2289. https://doi.org/10.3390/en14082289.
89.
Yang, X.; Liu, H.; Dhawan, S.; Politis, D.J.; Zhang, J.; Dini, D.; Wang, L. Digitally-enhanced lubricant evaluation scheme for hot stamping applications. Nat. Commun. 2022, 13, 5748. https://doi.org/10.1038/s41467-022-33532-1.
90.
Mia, M.; Zhang, L.; Anwar, S.; Liu, H. Development of digital characteristics of machining based on physics-guided data. J. Manuf. Syst. 2023, 71, 438–450. https://doi.org/10.1016/j.jmsy.2023.09.014.
91.
Liu, H.; Liu, X.; Yang, X.; Politis, D.J.; Zheng, Y.; Dhawan, S.; Wang, L. Mapping the hot stamping process through developing distinctive digital characteristics. Comput. Ind. 2024, 161, 104121. https://doi.org/10.1016/j.compind.2024.104121.
92.
Liu, H.; Dhawan, S.; Yang, X.; Politis, D.J.; Weill, M.; Zheng, Y.; Wang, L. Genetic exploration” of metal forming processes through information absent and fragmental data processing. J. Manuf. Syst. 2025, 79, 286–300. https://doi.org/10.1016/j.jmsy.2025.01.014.
93.
Liu, H.; Yang, X.; Weill, M.; Li, S.; Wu, V.; Politis, D.J.; Wang, L. Unlocking inherent values of manufacturing metadata through digital characteristics (DC) alignment. Comput. Ind. 2024, 163, 104148. https://doi.org/10.1016/j.compind.2024.104148.
94.
Liu, H.; Yang, X.; Politis, D.J.; Shi, H.; Wang, L. Evaluation framework of digital characteristics (DC) enhanced lubricant: Consideration of essential geometric features for hot-stamped components. J. Manuf. Syst. 2024, 75, 150–162. https://doi.org/10.1016/j.jmsy.2024.06.008.
95.
Liu, H.; Wu, V.; Weill, M.; Li, S.; Yang, X.; Politis, D.J.; Wang, L. Developing physics-informed filters to align unattributed fragmental manufacturing data against a digital characteristics space (DCS). J. Manuf. Syst. 2024, 77, 18–25. https://doi.org/10.1016/j.jmsy.2024.09.002.
96.
Liu, H.; Yang, X.; Politis, D.; Shi, H.; Wang, L. An evaluation scheme incorporating digital characteristics for transient tribological behaviours under complex loading conditions for the hot stamping process. Comput. Ind. 2025, 167, 104270. https://doi.org/10.1016/j.compind.2025.104270.
97.
Javaid, M.; Haleem, A.; Suman, R. Digital Twin applications toward Industry 4.0: A Review. Cogn. Robot. 2023, 3, 71–92. https://doi.org/10.1016/j.cogr.2023.04.003.
Issue
Volume 4, Issue 1How to Cite
Liu, H., Zhang, B., Wu, V., Yang, X., & Wang, L. (2025). Review of Digital Twin in the Automotive Industry on Products, Processes and Systems. International Journal of Automotive Manufacturing and Materials, 4(1), 6. https://doi.org/10.53941/ijamm.2025.100006
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