2504000083
  • Open Access
  • Review
Industry 4.0 in Metal Forming Industry Towards Automotive Applications: A Review
  • Heli Liu 1, 2,   
  • Dhawan Saksham 1, 2,   
  • Merrill Shen 1,   
  • Kangan Chen 1,   
  • Vincent Wu 3,   
  • Liliang Wang 1, 2, *

Received: 23 Sep 2022 | Accepted: 18 Nov 2022 | Published: 18 Dec 2022

Abstract

Industry 4.0 is shaping the metal forming industry. The ongoing key Industry 4.0 technologies, including the industrial cyber-physical system (I-CPS), industrial internet of things (I-IoT), digital twin (DT), big data (BD) and cloud computing (CC), are expected to improve every stage during the metal forming processes, including the supply chains, raw material provision, tool design and manufacture, forming operations, energy consumption, cost, quality control and customer services. Here, we review the development and implementations of these key Industry 4.0 technologies in the metal forming industry. Based on the discussion of the opportunities and challenges of Industry 4.0 technologies in metal forming, this review provides some perspectives of future metal forming research directions towards automotive applications.

References 

  • 1.
    Awasthi A. ; Saxena K.K. ; Arun V . Sustainable and smart metal forming manufacturing process. Materials Today. Proceedings, 2021, 44, Part 1: 2069-2079.
  • 2.
    Cao J. ; Banu M . Opportunities and challenges in metal forming for lightweighting: review and future work. Journal of Manufacturing Science and Engineering, 2020, 142(11): 110813.
  • 3.
    Cullen J.M. ; Allwood J.M. ; Bambach M .D. Mapping the global flow of steel: from steelmaking to end-use goods. Environmental Science & Technology, 2012, 46(24): 13048-13055.
  • 4.
    Cullen J.M. ; Allwood J .M. Mapping the global flow of aluminum: from liquid aluminum to end-use goods. Environmental Science & Technology, 2013, 47(7): 3057-3064.
  • 5.
    El Fakir O. ; Wang L.L. ; Balint D. ; et al . Numerical study of the solution heat treatment, forming, and in-die quenching (HFQ) process on AA5754. International Journal of Machine Tools and Manufacture, 2014, 87: 39-48.
  • 6.
    Zheng K.L. ; Dong Y.C. ; Zheng J.H. ; et al . The effect of hot form quench (HFQ®) conditions on precipitation and mechanical properties of aluminium alloys. Materials Science and Engineering: A, 2019, 761: 138017.
  • 7.
    Zhang Q.L. ; Luan X. ; Dhawan S. ; et al . Development of the post-form strength prediction model for a high-strength 6xxx aluminium alloy with pre-existing precipitates and residual dislocations. International Journal of Plasticity, 2019, 119: 230-248.
  • 8.
    Sun Y.H. ; Wang K.H. ; Politis D.J. ; et al . An experimental investigation on the ductility and post-form strength of a martensitic steel in a novel warm stamping process. Journal of Materials Processing Technology, 2020, 275: 116387.
  • 9.
    Liu X.C. ; Di B.Z. ; Yu X.N. ; et al . Development of a formability prediction model for aluminium sandwich panels with polymer core. Materials, 2022, 15(12): 4140.
  • 10.
    Ma G.J. ; Wang L.L. ; Gao H.X. ; et al . The friction coefficient evolution of a TiN coated contact during sliding wear. Applied Surface Science, 2015, 345: 109-115.
  • 11.
    Elmkhah H. ; Mahboubi F. ; Abdollah-zadeh A. ; et al . A new approach to improve the surface properties of H13 steel for metal forming applications by applying the TiAlN multi-layer coating. Journal of Manufacturing Processes, 2018, 32: 873-877.
  • 12.
    Lugscheider E. ; Bobzin K. ; Piñero C. ; et al . Development of a superlattice (Ti,Hf,Cr)N coating for cold metal forming applications. Surface and Coatings Technology, 2004, 177/178: 616-622.
  • 13.
    Wang L.L. ; Zhou J. ; Duszczyk J. ; et al . Friction in aluminium extrusion—part 1: a review of friction testing techniques for aluminium extrusion. Tribology International, 2012, 56: 89-98.
  • 14.
    Wang L.L. ; Yang H .L. Friction in aluminium extrusion—part 2: a review of friction models for aluminium extrusion. Tribology International, 2012, 56: 99-106.
  • 15.
    Yang X. ; Liu X.C. ; Liu H. ; et al . Experimental and modelling study of friction evolution and lubricant breakdown behaviour under varying contact conditions in warm aluminium forming processes. Tribology International, 2021, 158: 106934.
  • 16.
    Liu H.L. ; Yang X. ; Zheng Y. ; et al . Experimental study on galling behavior in aluminum stamping processes. Physical Sciences Forum, 2022, 4(1): 10.
  • 17.
    Hu Y. ; Wang L. ; Politis D.J. ; et al . Development of an interactive friction model for the prediction of lubricant breakdown behaviour during sliding wear. Tribology International, 2017, 110: 370-377.
  • 18.
    Hu Y.R. ; Yuan X. ; Ma G.J. ; et al . Tool-life prediction under multi-cycle loading during metal forming: a feasibility study. Manufacturing Review, 2015, 2: 28.
  • 19.
    Li Z.X. ; Rezaei S. ; Wang T. ; et al . Recent advances and trends in roll bonding process and bonding model: a review. Chinese Journal of Aeronautics, 2022, in press.
  • 20.
    Bai C.G. ; Dallasega P. ; Orzes G. ; et al . Industry 4.0 technologies assessment: a sustainability perspective. International Journal of Production Economics, 2020, 229: 107776.
  • 21.
    Ibarra D. ; Ganzarain J. ; Igartua J .I. Business model innovation through industry 4.0: a review. Procedia Manufacturing, 2018, 22: 4-10.
  • 22.
    Xu H.S. ; Yu W. ; Griffith D. ; et al . A survey on industrial internet of things: a cyber-physical systems perspective. IEEE Access, 2018, 6: 78238-78259.
  • 23.
    Majeed A. ; Zhang Y.F. ; Ren S. ; et al . A big data-driven framework for sustainable and smart additive manufacturing. Robotics and Computer-integrated Manufacturing, 2021, 67: 102026.
  • 24.
    Wilson, G. PwC: digital factories shaping the future of manufacturing. Available Online: https://manufacturingdigital.com/technology/pwc-digital-factories-shaping-future-manufacturing (Accessed on 23 September 2022).
  • 25.
    Leitão P. ; Colombo A.W. ; Karnouskos S . Industrial automation based on cyber-physical systems technologies: prototype implementations and challenges. Computers in Industry, 2016, 81: 11-25.
  • 26.
    Gupta N. ; Tiwari A. ; Bukkapatnam S .T.S.; et al. Additive manufacturing cyber-physical system: supply chain cybersecurity and risks. IEEE Access, 2020, 8: 47322-47333.
  • 27.
    Chen B. ; Chang J .Y.J. Dynamic analysis of intelligent coil leveling machine for cyber-physical systems implementation. Procedia CIRP, 2017, 63: 390-395.
  • 28.
    Sun J. ; Peng W. ; Ding J.G. ; et al . Key intelligent technology of steel strip production through process. Metals, 2018, 8(8): 597.
  • 29.
    Lee J. ; Noh S.D. ; Kim H.J. ; et al . Implementation of cyber-physical production systems for quality prediction and operation control in metal casting. Sensors, 2018, 18(5): 1428.
  • 30.
    Gan L. ; Huang H.H. ; Li L. ; et al . IoT-enabled energy efficiency monitoring and analysis method for energy saving in sheet metal forming workshop. Journal of Central South University, 2022, 29(1): 239-258.
  • 31.
    Witkowski K . Internet of things, big data, industry 4.0—innovative solutions in logistics and supply chains managemen. Procedia Engineering, 2017, 182: 763-769.
  • 32.
    Ralph B.J. ; Sorger M. ; Hartl K. ; et al . Transformation of a rolling mill aggregate to a cyber physical production system: from sensor retrofitting to machine learning. Journal of Intelligent Manufacturing, 2022, 33(2): 493-518.
  • 33.
    Wang L. ; Zhu B. ; Liu Y. ; et al . Design and application of CPS for hot stamping based on cloud computing. Advanced High Strength Steel and Press Hardening, Proceedings of the 5th International Conference (ICHSU2020), Shanghai, China: ICHUS, 2021: 423-429.
  • 34.
    Oruganti S.K. ; Khosla A. ; Thundat T .G. Wireless power-data transmission for industrial internet of things: simulations and experiments. IEEE Accesss, 2020, 8: 187965-187974.
  • 35.
    PwC . Annual manufacturing report 2020. Available Online: https://www.pwc.co.uk/industries/manufacturing/insights/annual-manufacturing-report.html (Accessed on 23 September 2022).
  • 36.
    Sisinni E. ; Saifullah A. ; Han S. ; et al . Industrial internet of things: challenges, opportunities, and directions. IEEE Transactions on Industrial Informatics, 2018, 14(11): 4724-4734.
  • 37.
    Yang M . Sensing technologies for metal forming. Sensors and Materials, 2019, 31(10): 3121-3128.
  • 38.
    Suresh A. ; Udendhran R. ; Yamini G . Internet of things and additive manufacturing: toward intelligent production systems in industry 4.0. Kanagachidambaresan, G.; Anand, R.; Balasubramanian, E.; et al. Internet of things for industry 4.0: designchallenges and solutions. Cham: Springer, 2020: 73-89.
  • 39.
    Mahayotsanun N. ; Sah S. ; Cao J. ; et al . Tooling-integrated sensing systems for stamping process monitoring. International Journal of Machine Tools & Manufacture, 2009, 49(7/8): 634-644.
  • 40.
    Groche P. ; Brenneis M . Manufacturing and use of novel sensoric fasteners for monitoring forming processes. Measurement, 2014, 53: 136-144.
  • 41.
    Yang M . Smart metal forming with digital process and IoT. International Journal of Lightweight Materials and Manufacture, 2018, 1(4): 207-214.
  • 42.
    Tatipala S. ; Wall J. ; Johansson C. ; et al . A hybrid data-based and model-based approach to process monitoring and control in sheet metal forming. Processes, 2020, 8(1): 89.
  • 43.
    Dilberoglu U.M. ; Gharehpapagh B. ; Yaman U. ; et al . The role of additive manufacturing in the era of industry 4.0. Procedia Manufacturing, 2017, 11: 545-554.
  • 44.
    Ashima R. ; Haleem B. ; Bahl S. ; et al . Automation and manufacturing of smart materials in additive manufacturing technologies using Internet of Things towards the adoption of industry 4.0. Materials Today: Proceedings, 2021, 45(6): 5081-5088.
  • 45.
    Kawamoto K. ; Ando H. ; Yamamichi K . Application of servo presses to metal forming processes. Procedia Manufacturing, 2018, 15: 31-38.
  • 46.
    Chen E.H. ; Cao H.J. ; He Q.Y. ; et al . An IoT based framework for energy monitoring and analysis of die casting workshop. Procedia CIRP, 2019, 80: 693-698.
  • 47.
    Gan L. ; Li L. ; Huang H .H. Digital twin-driven sheet metal forming: modeling and application for stamping considering mold wear. Journal of Manufacturing Science and Engineering, 2022, 144(12): 121003.
  • 48.
    Tao F. ; Zhang H. ; Liu A. ; et al . Digital twin in industry: state-of-the-art. IEEE Transactions on Industrial Informatics, 2019, 15(4): 2405-2415.
  • 49.
    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.
  • 50.
    Tao F. ; Qi Q .L. Make more digital twins. Nature, 2019, 573(7775): 490-491.
  • 51.
    Söderberg R. ; Wärmefjord K. ; Carlson J.S. ; et al . Toward a Digital Twin for real-time geometry assurance in individualized production. CIRP Annals, 2017, 66(1): 137-140.
  • 52.
    Moreno A. ; Velez G. ; Ardanza A. ; et al . Virtualisation process of a sheet metal punching machine within the industry 4.0 vision. International Journal on Interactive Design and Manufacturing (IJIDeM), 2017, 11(2): 365-373.
  • 53.
    Ralph B.J. ; Schwarz A. ; Stockinger M . An implementation approach for an academic learning factory for the metal forming industry with special focus on digital twins and finite element analysis. Procedia Manufacturing, 2020, 45: 253-258.
  • 54.
    Gaikwad A. ; Yavari R. ; Montazeri M. ; et al . Toward the digital twin of additive manufacturing: integrating thermal simulations, sensing, and analytics to detect process faults. IISE Transactions, 2020, 52(11): 1204-1217.
  • 55.
    Gunasegaram D.R. ; Murphy A.B. ; Matthews M.J. ; et al . The case for digital twins in metal additive manufacturing. JPhys Materials, 2021, 4(4): 040401.
  • 56.
    Stavropoulos P. ; Papacharalampopoulos A. ; Michail C.K. ; et al . Robust additive manufacturing performance through a control oriented digital twin. Metals, 2021, 11(5): 708.
  • 57.
    Phua A. ; Davies C .H.J.; Delaney G.W. A digital twin hierarchy for metal additive manufacturing. Computers in Industry, 2022, 140: 103667.
  • 58.
    Liu C. ; Roux L.L. ; Körner C. ; et al . Digital twin-enabled collaborative data management for metal additive manufacturing systems. Journal of Manufacturing Systems, 2022, 62: 857-874.
  • 59.
    Mandolla C. ; Petruzzelli A.M. ; Percoco G. ; et al . Building a digital twin for additive manufacturing through the exploitation of blockchain: a case analysis of the aircraft industry. Computers in Industry, 2019, 109: 134-152.
  • 60.
    Everton S.K. ; Hirsch M. ; Stravroulakis P. ; et al . Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing. Materials & Design, 2016, 95: 431-445.
  • 61.
    Mukherjee T. ; DebRoy T . A digital twin for rapid qualification of 3D printed metallic components. Applied Materials Today, 2019, 14: 59-65.
  • 62.
    Yao B. ; Imani F. ; Yang H . Markov decision process for image-guided additive manufacturing. IEEE Robotics and Automation Letters, 2018, 3(4): 2792-2798.
  • 63.
    Son Y.H. ; Park K.T. ; Lee D. ; et al . Digital twin–based cyber-physical system for automotive body production lines. The International Journal of Advanced Manufacturing Technology, 2021, 115(1): 291-310.
  • 64.
    Zhong R.Y. ; Newman S.T. ; Huang G.Q. ; et al . Big data for supply chain management in the service and manufacturing sectors: challenges, opportunities, and future perspectives. Computers & Industrial Engineering, 2016, 101: 572-591.
  • 65.
    Wilkinson M.D. ; Dumontier M. ; Aalbersberg I .J.J.; et al. The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 2016, 3: 160018.
  • 66.
    Fortune Business Insights. Big data in manufacturing industry size, share & industry analysis, by offering (solution and services), by development (on premise, cloud-based and hybrid), by application (customer analytics, quality assessment, supply chain management, production management, and others) and regional forecast . Available Online: https://www.fortunebusinessinsights.com/big-data-in-manufacturing-industry-102366 (Accessed on 23 September 2022).
  • 67.
    Cao J. ; Brinksmeier E. ; Fu M.W. ; et al . Manufacturing of advanced smart tooling for metal forming. CIRP Annals, 2019, 68(2): 605-628.
  • 68.
    Bonatti C. ; Mohr D . One for all: universal material model based on minimal state-space neural networks. Science Advances, 2021, 7(26): eabf3658.
  • 69.
    Feng S. ; Zhou H.Y. ; Dong H .B. Using deep neural network with small dataset to predict material defects. Materials & Design, 2019, 162: 300-310.
  • 70.
    Bustillo A. ; Pimenov D.Y. ; Mia M. ; et al . Machine-learning for automatic prediction of flatness deviation considering the wear of the face mill teeth. Journal of Intelligent Manufacturing, 2021, 32(3): 895-912.
  • 71.
    Li X. ; Jia X.D. ; Yang Q.B. ; et al . Quality analysis in metal additive manufacturing with deep learning. Journal of Intelligent Manufacturing, 2020, 31(8): 2003-2017.
  • 72.
    Kusiak A . Smart manufacturing must embrace big data. Nature, 2017, 544(7648): 23-25.
  • 73.
    Kang H.S. ; Lee J.Y. ; Choi S.S. ; et al . Smart manufacturing: past research, present findings, and future directions. International Journal of Precision Engineering and Manufacturing-Green Technology, 2016, 3(1): 111-128.
  • 74.
    Wang A.L. ; Liu J. ; Gao H.X. ; et al . Hot stamping of AA6082 tailor welded blanks: experiments and knowledge-based cloud–finite element (KBC-FE) simulation. Journal of Materials Processing Technology, 2017, 250: 228-238.
  • 75.
    Zhu M.Q. ; Lim Y.C. ; Cai Z.J. ; et al . Cloud FEA of hot stamping processes using a software agnostic platform. The International Journal of Advanced Manufacturing Technology, 2021, 112(11): 3445-3458.
  • 76.
    Wang K.H. ; Kopec M. ; Chang S.P. ; et al . Enhanced formability and forming efficiency for two-phase Titanium alloys by Fast light Alloys Stamping Technology (FAST) Materials & Design, 2020, 194: 108948.
  • 77.
    Alsamhan A. ; Ragab A.E. ; Dabwan A. ; et al . Prediction of formation force during single-point incremental sheet metal forming using artificial intelligence techniques. PLoS One, 2019, 14(8): e0221341.
  • 78.
    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. Journal of Intelligent Manufacturing, 2022, 33(1): 259-282.
  • 79.
    Merayo D. ; Rodríguez-Prieto A. ; Camacho A . M. Topological optimization of artificial neural networks to estimate mechanical properties in metal forming using machine learning. Metals, 2021, 11(8): 1289.
  • 80.
    Li W. ; Zhang L.C. ; Chen X.P. ; et al . Predicting the evolution of sheet metal surface scratching by the technique of artificial intelligence. The International Journal of Advanced Manufacturing Technology, 2021, 112(3): 853-865.
  • 81.
    Taherkhani A. ; Basti A. ; Nariman-Zadeh N. ; et al . Achieving maximum dimensional accuracy and surface quality at the shortest possible time in single-point incremental forming via multi-objective optimization. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 2019, 233(3): 900-913.
  • 82.
    Ambrogio G. ; Filice L. ; Guerriero F. ; et al . Prediction of incremental sheet forming process performance by using a neural network approach. The International Journal of Advanced Manufacturing Technology, 2011, 54(9): 921-930.
  • 83.
    Liu S.M. ; Xia Y.F. ; Liu Y.H. ; et al . Tool path planning of consecutive free-form sheet metal stamping with deep learning. Journal of Materials Processing Technology, 2022, 303: 117530.
  • 84.
    Opritescu D. ; Volk W . Automated driving for individualized sheet metal part production—A neural network approach. Robotics and Computer-Integrated Manufacturing, 2015, 35: 144-150.
  • 85.
    Chan W.L. ; Fu M.W. ; Lu J . An integrated FEM and ANN methodology for metal-formed product design. Engineering Applications of Artificial Intelligence, 2008, 21(8): 1170-1181.
  • 86.
    Pilani R. ; Narasimhan K. ; Maiti S.K. ; et al . A hybrid intelligent systems approach for die design in sheet metal forming. The International Journal of Advanced Manufacturing Technology, 2000, 16(5): 370-375.
  • 87.
    Zhang M. ; Sun C.N. ; Zhang X. ; et al . High cycle fatigue life prediction of laser additive manufactured stainless steel: a machine learning approach. International Journal of Fatigue, 2019, 128: 105194.
  • 88.
    Zhou H.S. ; Xu Q.F. ; Nie Z.G. ; et al . A study on using image-based machine learning methods to develop surrogate models of stamp forming simulations. Journal of Manufacturing Science and Engineering, 2022, 144(2): 021012.
  • 89.
    Yang X. ; Liu H.L. ; Dhawan S. ; et al . Digitally-enhanced lubricant evaluation scheme for hot stamping applications. Nature Communications, 2022, 13(1): 5748.
  • 90.
    Dillon T. ; Wu C. ; Chang E . Cloud computing: issues and challenges. 2010 24th IEEE International Conference on Advanced Information Networking and Applications, Perth, WA, Australia: IEEE, 2010: 27-33.
  • 91.
    Helo P. ; Phuong D. ; Hao Y . Cloud manufacturing – scheduling as a service for sheet metal manufacturing. Computers & Operations Research, 2019, 110: 208-219.
  • 92.
    Paniti I . Adaptation of incremental sheet forming into cloud manufacturing. CIRP Journal of Manufacturing Science and Technology, 2014, 7(3): 185-190.
  • 93.
    Kao Y.C. ; Liu Y.P. ; Wei C.L. ; et al . Application of a cyber-physical system and machine-to-machine communication for metal processes. 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Houston, TX, USA: IEEE, 2018: 1-6.
  • 94.
    Dhawan S . Development of a cloud FEA platform for advanced FE simulations of metal forming processes. (Imperial College London, 2022).
  • 95.
    Wang A.L. ; El Fakir O. ; Liu J. ; et al . Multi-objective finite element simulations of a sheet metal-forming process via a cloud-based platform. The International Journal of Advanced Manufacturing Technology, 2019, 100(9): 2753-2765.
  • 96.
    Wang A.L. ; Zheng Y. ; Liu J. ; et al . Knowledge based cloud FE simulation–data-driven material characterization guidelines for the hot stamping of aluminium alloys. Journal of Physics: Conference Series, 2016, 734: 032042.
  • 97.
    Yang, Lubricant X. 4.0: Digitally enhanced lubricant development for metal forming applications. (Imperial College London, 2021.
  • 98.
    Luan X. ; Zhang Q.L. ; Fakir O.E. ; et al . Uni-Form: a pilot production line for hot/warm sheet metal forming integrated in a cloud based SMARTFORMING platform. Zhang, Y.S.; Ma, M.T. Advanced high strength steel and press hardening. Singapore: World Scientific, 2016: 492-497.
  • 99.
    Fakir O. ; Wang A. ; Zhang Q. ; et al . Multi-objective sheet metal forming simulations using a software agnostic platform. IOP Conference Series: Materials Science and Engineering, 2018, 418: 012122.
  • 100.
    Jain N. ; Choudhary S . Overview of virtualization in cloud computing. 2016 Symposium on Colossal Data Analysis and Networking (CDAN), Indore, India: IEEE, 2016: 1-4.
  • 101.
    Fuller A. ; Fan Z. ; Day C. ; et al . Digital twin: enabling technologies, challenges and open research. IEEE Access, 2020, 8: 108952-108971.
  • 102.
    Hagenah H. ; Schulte R. ; Vogel M. ; et al . 4.0 in metal forming–questions and challenges. Procedia CIRP, 2019, 79: 649-654.
Share this article:
How to Cite
Liu, H.; Saksham, D.; Shen, M.; Chen, K.; Wu, V.; Wang, L. Industry 4.0 in Metal Forming Industry Towards Automotive Applications: A Review. International Journal of Automotive Manufacturing and Materials 2022, 1 (1), 2. https://doi.org/10.53941/ijamm0101002.
RIS
BibTex
Copyright & License
article copyright Image
Copyright (c) 2022 by the authors.