FEM and machine learning aided modelling of compressive deformation anisotropy in pressure-less sintered Cu-15Ni-8Sn alloy

Abstract

Pressure-less sintering integrated forging is an effective method for densification enhancement of powder metallurgy (PM) processed Cu-15Ni-8Sn alloy. Nevertheless, compressive deformation anisotropy associated with uniaxial forging creates heterogeneous deformation zones containing residual porosity, resulting in strength degradation of the PM alloy. This work introduces a novel approach for mitigating the anisotropy arising from forging by characterising the compressive deformation heterogeneity using a synergy of finite element method (FEM) and machine learning (ML) approaches. Results obtained from Gurson model-based FEM simulation of uniaxial forging process indicate that decreasing the aspect ratio (AR) of sintered preform leads to reduction in plastic deformation heterogeneity. Additionally, the semi supervised ML framework developed here unravels three distinct strain hardening regimes and predicts an upward paradigm shift in the quantitative distribution of instantaneous strain hardening exponent with the decrease in AR. These computational predictions align with the experimental forging results which demonstrate substantial enhancement in strain hardenability, formability and densification of lower AR preform due to more uniform deformation. Uniaxially forged compact depicted a notable increase of 14% in relative density along with significant improvement in hardness by 66% as compared to the sintered compact, which can be attributed to forging induced pore elimination at microstructural level. The promising results obtained in this study have practical implications in the development of sustainable green manufacturing technology to produce high strength porosity tailored Cu-15Ni-8Sn alloy.