Imperial College London


Faculty of EngineeringDepartment of Mechanical Engineering

Head/Professor in Mechanics of Materials Division



+44 (0)20 7594 7082jianguo.lin




Miss Valerie Crawford +44 (0)20 7594 7083




522City and Guilds BuildingSouth Kensington Campus





Professor Lin''s research experience is in Materials and process modelling, Solid/Computational mechanics, which includes micro-mechanics modelling, and its application in creep-damage, cyclic-plasticity-damage, viscoplasticity and advanced metal forming technologies.

At the University of Sheffield and UMIST, He worked on the development of unified creep-damage, viscoplasticity-damage, coupled creep and cyclic-plasticity damage constitutive equations. These equations were implemented in FE solvers to predict the damage and failure of engineering materials, such as copper, Al-alloys and nickel-based super-alloys, under different working conditions.

At the University of Birmingham, He worked on the development of unified viscoplastic constitutive equations to model micro-structure and damage evolution in hot/warm metal forming processes. Some examples include: (i) phase transformation in hot-stamping with cold-die quenching, (ii) grain growth in superplastic forming, (iii) age precipitation hardening in creep-age-forming, (iv) recrystallisation, grain size and damage evolution in hot rolling, etc.. He is one of the originators for the development of Evolutionary Algorithms (EA)-based optimization techniques for the determination of physically-based unified constitutive equations from experimental data.

He has also developed an integrated facility for micro-mechanics modelling. Particular applications include the forming of micro components, such as extrusion of micro pins, hydroforming of micro-tubes, etc.



Alt─▒parmak SC, Yardley VA, Shi Z, et al., 2021, Challenges in additive manufacturing of high-strength aluminium alloys and current developments in hybrid additive manufacturing, International Journal of Lightweight Materials and Manufacture, Vol:4, ISSN:2588-8404, Pages:246-261

Liu S, Xia Y, Shi Z, et al., 2021, Deep learning in sheet metal bending with a novel theory-guided deep neural network, Ieee/caa Journal of Automatica Sinica, Vol:8, ISSN:2329-9266, Pages:565-581

Zhang R, Shao Z, Shi Z, et al., 2021, Effect of cruciform specimen design on strain paths and fracture location in equi-biaxial tension, Journal of Materials Processing Technology, Vol:289, ISSN:0924-0136, Pages:1-16

Zhang R, Shi Z, Shao Z, et al., 2021, An effective method for determining necking and fracture strains of sheet metals, Methodsx, Vol:8, ISSN:2215-0161

Politis DJ, Politis NJ, Lin J, 2021, Review of recent developments in manufacturing lightweight multi-metal gears, Production Engineering-research and Development, Vol:15, ISSN:0944-6524, Pages:235-262

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