A fundamental to simulating the response of metallic alloys in manufacturing processes is a material model to describe the flow stress properties at a large range of strain rates and temperatures. Phenomenological models like Johnson-Cook [1], Childs [2] and Shin-Kim [3] are commonly used for modelling and simulation of material behaviour in cutting and forming processes. These models may, however, have limited applicability as they need to be reassessed for new material conditions, e.g., when the microstructure varies due to slight changes along the manufacturing value-chain. To overcome this limitation, physics-based (dislocation-based) models have been proposed [4, 5]. These models describe the flow stress properties in a cumulative manner and generally include the effects of lattice friction stress (Peierls-Nabarro stress); grain boundary hardening (Hall-Petch), solid-solution strengthening, precipitation hardening (Orowan strengthening mechanism) and long-range interactions with obstacles (e.g., dislocation structure).

This presentation aims to demonstrate the capabilities of the physics-based approach for modelling and simulation of flow stress properties of pearlitic-ferritic microalloyed steels as well as the titanium and Ni-based alloys. The presented physics-based models can be coupled with microstructure simulations and thermodynamic and kinetic models to provide a unified platform for simulation of material response along the manufacturing value-chain.

References

[1] A. Malakizadi, S. Cedergren, I. Sadik, L. Nyborg, Inverse identification of flow stress in metal cutting process using Response Surface Methodology. Simulation Modelling Practice and Theory. 2016; 60:40-53.

[2] T.H. Childs, Revisiting flow stress modelling for simulating chip formation of carbon and low alloy steels. Procedia CIRP. 2019; 82:26-31.

[3] H. Shin H, J.B. Kim, A phenomenological constitutive equation to describe various flow stress behaviors of materials in wide strain rate and temperature regimes, J. Eng. Mater. Technol., 2010; 132(2): 021009

[4] A. Malakizadi, J. Saelzer, S. Berger, Y. Alammari, D. Biermann, A physics-based constitutive model for simulation of chip formation when machining of Ti-6Al-4V titanium alloy. 19th CIRP Conference on Modeling of Machining Operations, 2023. Accepted for publication.

[5] L.E. Lindgren, K. Domkin, S. Hansson, Dislocations, vacancies and solute diffusion in physical based plasticity model for AISI 316L. Mechanics of Materials. 2008; 40(11):907-19.