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Title: Interactive modelling of the evolutionary tribological phenomena in cold stamping
Author: Hu, Yiran
ISNI:       0000 0004 7963 7361
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2018
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Tribological contacts are responsible for approximately 23% of world's total energy consumption with the majority of energy consumption involved in manufacturing operations. Manufacturing is one of the main energy consuming sectors and the optimizing of its friction and wear performance can significantly increase the lifetime and reduce the maintenance work of equipment. In cold stamping, friction and wear that are generated between the tool and work-piece are important tribological responses infuencing the material ow, surface quality and tool-life. It has been found that these responses are not material properties but highly nonlinear phenomena, which vary spatially and historically with operation parameters, such as contact pressure, sliding speed and sliding distance of forming processes. It has been widely observed that galling and lubricant breakdown phenomena that occur during cold stamping cause evolutionary surface morphology changes and thus involve the simultaneous interaction between friction and wear: the galling generated on a tool surface during forming can cause coefficient of friction (COF) to increase and wear rate to decrease; the lubricant lm breakdown with time during single path sliding leads to a rapid increase in the COF and wear at the contact. In this thesis, interactive friction models were developed to predict the evolution of COF with the effect of galling and lubricant breakdown. In these models, the historical and pressure dependencies of these phenomena combining the effects of operation parameters were addressed. These interactive friction models could be applied to situations involving galling and lubricant breakdown in a dynamic environment such as the metal forming industry, where the use of traditional constant COF values present limits in predictive accuracy.
Supervisor: Wang, Liliang ; Masen, Marc Sponsor: China Scholarship Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral