Digitally-enhanced lubricant evaluation scheme for hot stamping applications

Xiao Yang, Heli Liu, Saksham Dhawan, Denis J. Politis, Jie Zhang, Daniele Dini, Lan Hu, Mohammad M. Gharbi and Liliang Wang ()
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Xiao Yang: Imperial College London
Heli Liu: Imperial College London
Saksham Dhawan: Imperial College London
Denis J. Politis: University of Cyprus
Jie Zhang: Imperial College London
Daniele Dini: Imperial College London
Lan Hu: Imperial College London
Mohammad M. Gharbi: Houghton Deutschland GmbH
Liliang Wang: Imperial College London

Nature Communications, 2022, vol. 13, issue 1, 1-9

Abstract: Abstract Digitally-enhanced technologies are set to transform every aspect of manufacturing. Networks of sensors that compute at the edge (streamlining information flow from devices and providing real-time local data analysis), and emerging Cloud Finite Element Analysis technologies yield data at unprecedented scales, both in terms of volume and precision, providing information on complex processes and systems that had previously been impractical. Cloud Finite Element Analysis technologies enable proactive data collection in a supply chain of, for example the metal forming industry, throughout the life cycle of a product or process, which presents revolutionary opportunities for the development and evaluation of digitally-enhanced lubricants, which requires a coherent research agenda involving the merging of tribological knowledge, manufacturing and data science. In the present study, data obtained from a vast number of experimentally verified finite element simulation results is used for a metal forming process to develop a digitally-enhanced lubricant evaluation approach, by precisely representing the tribological boundary conditions at the workpiece/tooling interface, i.e., complex loading conditions of contact pressures, sliding speeds and temperatures. The presented approach combines the implementation of digital characteristics of the target forming process, data-guided lubricant testing and mechanism-based accurate theoretical modelling, enabling the development of data-centric lubricant limit diagrams and intuitive and quantitative evaluation of the lubricant performance.

Date: 2022
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DOI: 10.1038/s41467-022-33532-1

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