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Mapping the hot stamping process through developing distinctive digital characteristics

Published: 18 November 2024 Publication History

Abstract

Structural components produced through hot stamping of lightweight materials, such as aluminium alloys, play a pivotal role in mass reduction, leading to decreased CO2 emissions and enhanced fuel efficiency, especially in applications such as electric vehicles, high-speed trains, and aircraft. Concurrently, the hot stamping process is experiencing an exponential increase in data generation, stemming from ongoing production, research, and development activities. Yet, translating the inherent values of these voluminous metadata into scientific innovations and industrial breakthroughs requires the emerging expertise by consolidating the knowledge of hot stamping and data science. Here, the authors have conceptualised and developed the digital characteristics (DC) for manufacturing processes. The DC serves as the ‘DNA’ of every manufacturing process by encompassing its inherent and distinctive natures spanning over the design, manufacturing and application phases of the manufactured products. Focusing on the hot stamping process, the authors have developed the unique DC from voluminous hot stamping data derived from experimentally validated simulations and sensing networks. Results demonstrate that the DC revealed the distinct evolutionary thermo-mechanical characteristics of the hot stamping process in terms of representative geometric features, which facilitates the fundamental scientific understanding and unlocks the potential on implementing data-centric scientific innovations in advanced manufacturing paradigms.

Highlights

Establishing a cloud-based database of manufacturing processes.
Developing distinctive digital characteristics (DC) of hot stamping process.
Proposing a novel methodology to unlock inherent values of manufacturing metadata.

References

[1]
R.F. Babiceanu, R. Seker, Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook, Comput. Ind. 81 (2016) 128–137.
[3]
Dhawan, S., El Fakir, O. & Wang, L. An Online Database for Hosting and Executing Numerical Models. Patent number: 201911116210.6. Filed November 15, 2019, and issued February 25, 2020 (2020).
[4]
O. El Fakir, et al., Numerical study of the solution heat treatment, forming, and in-die quenching (HFQ) process on AA5754, Int. J. Mach. Tools Manuf. 87 (2014) 39–48.
[5]
H. Karbasian, A.E. Tekkaya, A review on hot stamping, J. Mater. Process. Technol. 210 (2010) 2103–2118.
[6]
M.R.N. King, P.D. Timms, S. Mountney, A proposed universal definition of a Digital Product Passport Ecosystem (DPPE): Worldviews, discrete capabilities, stakeholder requirements and concerns, J. Clean. Prod. 384 (2023).
[7]
M. Kopec, et al., Formability and microstructure evolution mechanisms of Ti6Al4V alloy during a novel hot stamping process, Mater. Sci. Eng. A 719 (2018) 72–81.
[8]
X. Liu, et al., Determination of the interfacial heat transfer coefficient for a hot aluminium stamping process, J. Mater. Process. Technol. 247 (2017) 158–170.
[9]
X. Liu, et al., Effects of lubricant on the IHTC during the hot stamping of AA6082 aluminium alloy: Experimental and modelling studies, J. Mater. Process. Technol. 255 (2018) 175–183.
[10]
J. Liu, et al., Transition of failure mode in hot stamping of AA6082 tailor welded blanks, J. Mater. Process. Technol. 257 (2018) 33–44.
[11]
X. Liu, et al., Characterisation of the interfacial heat transfer coefficient in hot stamping of titanium alloys, Int. Commun. Heat Mass Transf. 113 (2020).
[12]
H. Liu, et al., Industry 4.0 in metal forming industry towards automotive applications: a review, Int. J. Automot. Manuf. Mater. 1 (2022) 2.
[13]
M. Mia, L. Zhang, S. Anwar, H. Liu, Development of digital characteristics of machining based on physics-guided data, J. Manuf. Syst. 71 (2023) 438–450.
[14]
D. Mulhall, A.-C. Ayed, J. Schroeder, K. Hansen, T. Wautelet, The product circularity data sheet—a standardized digital fingerprint for circular economy data about products., Energies 15 (2022) 3397.
[15]
PricewaterhouseCoopers. Digital Factories Shaping the Future of Manufacturing. 〈https://manufacturingdigital.com/technology/pwc-digital-factories-shaping-future-manufacturing〉 (2020).
[16]
Y. Sun, K. Wang, D.J. Politis, G. Chen, L. Wang, An experimental investigation on the ductility and post-form strength of a martensitic steel in a novel warm stamping process, J. Mater. Process. Technol. 275 (2020).
[17]
F. Tao, Q. Qi, Make more digital twins, Nature 573 (2019) 490–491.
[18]
F. Tao, H. Zhang, A. Liu, A.Y.C. Nee, Digital Twin in Industry: State-of-the-Art, IEEE Trans. Ind. Inform. 15 (2019) 2405–2415.
[19]
A. Wang, et al., Multi-objective finite element simulations of a sheet metal-forming process via a cloud-based platform, Int. J. Adv. Manuf. Technol. 100 (2019) 2753–2765.
[20]
K. Wang, et al., Enhanced formability and forming efficiency for two-phase titanium alloys by Fast light Alloys Stamping Technology (FAST), Mater. Des. 194 (2020).
[21]
M.D. Wilkinson, et al., The FAIR guiding principles for scientific data management and stewardship, Sci. Data 3 (2016).
[22]
T. Wuest, D. Weimer, C. Irgens, K.-D. Thoben, Machine learning in manufacturing: advantages, challenges, and applications, Prod. Manuf. Res. 4 (2016) 23–45.
[23]
L.D. Xu, W. He, S. Li, Internet of things in industries: a survey, IEEE Trans. Ind. Inform. 10 (2014) 2233–2243.
[24]
X. Yang, et al., Experimental and modelling study of friction evolution and lubricant breakdown behaviour under varying contact conditions in warm aluminium forming processes, Tribol. Int. 158 (2021).
[25]
X. Yang, et al., Experimental and modelling studies of the transient tribological behaviour of a two-phase lubricant under complex loading conditions, Friction 10 (2022) 911–926.
[26]
X. Yang, et al., Digitally-enhanced lubricant evaluation scheme for hot stamping applications, Nat. Commun. 13 (2022) 5748.
[27]
X. Yang, et al., Interactive mechanism and friction modelling of transient tribological phenomena in metal forming processes: a review, Friction 12 (2024) 375–395.
[28]
Q. Zhang, et al., 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. 119 (2019) 230–248.
[29]
K. Zheng, et al., The effect of hot form quench (HFQ®) conditions on precipitation and mechanical properties of aluminium alloys, Mater. Sci. Eng. A 761 (2019).

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Information & Contributors

Information

Published In

cover image Computers in Industry
Computers in Industry  Volume 161, Issue C
Oct 2024
247 pages

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 18 November 2024

Author Tags

  1. Hot stamping
  2. Metal forming
  3. Metadata
  4. Digital manufacturing
  5. Digital characteristics (DC)

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