Investigation of full-field strain evolution behavior of Cu/Ni clad foils by interpretable machine learning

Void characteristics are fundamentally correlated with the macroscopic deformation responses of materials, yet traditional modeling methods exhibit inherent limitations in data mining. In this study, a machine learning (ML) framework is proposed to predict the full-field strain evolution of Cu/Ni clad foils, and the impact of intrinsic voids is quantitatively assessed using interpretative analysis methods. The local strain and void data are extracted and integrated through digital image correlation and computed tomography. To accommodate the nature of the constructed dataset, a ML model is established with reference to the concept of time series forecasting. Subsequently, the influence of microstructural features such as volume fraction (VVF), area, and size of voids are investigated, alongside their role in driving local strain evolution. This approach successfully predicts strain localization, and accurately pinpoints the onset of plastic instability and the location of crack initiation. The VVF is identified as the most predominant factor, followed by void size along the tensile direction and grain size. The strongest association is observed between the VVF and grain size, which intensifies over extended time scales. Moreover, as void coalescence is almost completed, the promoting effect of the concentrated void distribution on macroscopic strain concentration will become increasingly pronounced. These findings provide novel perspectives for exploring the intricate relationship between deformation and damage.