Predicting subgrain size and dislocation density in machining-induced surface microstructure of Nickel using supervised model-based learning.

Microstructure evolution under interactive effects of severe shear strains, strain-rates and the accompanied temperature rise often follows complex trajectories. Encapsulating the process–structure linkages under these conditions is vital for prediction and control of product outcomes from processes that involve severe plastic deformation. This paper examines the microstructure transformations during severe shear deformation induced by plane strain machining (PSM) on high-purity (99.99%) Nickel. Deformation conditions in both chips and the surface are created using PSM and characterized via in-situ techniques which are then juxtaposed with orientation imaging microscopy (OIM) via electron back scattered diffraction (EBSD). The dislocation densities are quantified using the broadening of X-ray diffraction peaks of crystallographic planes. We capture the variation of microstructure response (subgrain size and dislocation density), by applying the supervised model-based learning techniques combined with physics-based models to enhance the predictions performance. The features involved in the study are cutting speed, rake angle, temperature, strain, strain-rate, in addition to lnZ and a rate parameter R identified from the saturated Microstructure evolution under interactive effects of severe shear strains, strain-rates and the accompanied temperature rise often follows complex trajectories. Encapsulating the process–structure linkages under these conditions is vital for prediction and control of product outcomes from processes that involve severe plastic deformation. This paper examines the microstructure transformations during severe shear deformation induced by plane strain machining (PSM) on high-purity (99.99%) Nickel. Deformation conditions in both chips and the surface are created using PSM and characterized via in-situ techniques which are then juxtaposed with orientation imaging microscopy (OIM) via electron back scattered diffraction (EBSD). The dislocation densities are quantified using the broadening of X-ray diffraction peaks of crystallographic planes. We capture the variation of microstructure response (subgrain size and dislocation density), by applying the supervised model-based learning techniques combined with physics-based models to enhance the predictions performance. The features involved in the study are cutting speed, rake angle, temperature, strain, strain-rate, in addition to lnZ and a rate parameter R identified from the saturated [ABSTRACT FROM AUTHOR]