Developing digital characteristics (DC), the genome of manufacturing technologies, for digitally enhanced manufacture of metal forming products

Li-Liang Wan,*2*, Xiao Yang1,2

1 Metal Forming and Materials Modelling Group, Department of Mechanical Engineering,
Imperial College London, London SW7 2AZ, UK
2SmartForming Research Base, Imperial College London, LondonSW7 2AZ, UK

EXTENDED 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 products, which requires a coherent research agenda involving the merging of tribological knowledge, manufacturing and data science. Digital characteristics (DC) is proposed for characterising manufacturing technologies from the perspective of the metadata generated during the process. The DC is the visualisation of manufacturing metadata for a specific manufacturing process incorporating essential information spanning the design, manufacturing and application stages of manufactured products. 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 [1], 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 mechanism­based accurate theoretical modelling, enabling the development of data-centric lubricant limit diagrams and intuitive and quantitative evaluation of the lubricant performance.

Keywords: digital characteristics; digitally enhanced manufacturing; industry 4.0; lubricant limit diagram.


[1] Yang, X., Liu, H., Dhawan, S., Politis, D. J., Zhang, J., Dini, D.,... & Wang, L., Nat Commun., 13, (2022) 5748

Brief Introduction of Speaker
Li-Liang WANG

Dr Li-Liang WANG is a Reader and the head of Metal Forming and Material Modelling group (MFG) at Imperial College London. Dr Wang's major research interests include the design and development of advanced metal forming technologies and manufacturing system. His work has made fundamental contributions to the characterization and modelling of materials and interfacial behaviours of engineering materials. Particularly, Dr Wang's research has direct impacts on lightweight manufacturing, e.g., Cloud-FE technologies for metal forming process design and optimization (Journal of Materials Processing Technology, 2017, 250: 228-238); Data sciences in metal forming (Nature Communications, 2022, 13: 5748); novel lightweight forming technology: FAST (International Journal of Plasticity, 2019, 119: 230-248); and innovative material characterization techniques (Additive Manufacturing 2021, 37: 101720). Dr Wang has authored/co-authored 120+ papers (H-i: 24; Ctns: 2100+). He has 11 filed/awarded
patents; 10 patents have been taken up by industry. Dr Wang is the founding director of the SmartForming Research Base at Imperial College London, and his research group is the receipt of the 2022 Presidenfs