Evaluating performance metrics in non-homogeneous cutting processes using a random-grid based digital image correlation (DIC) method

The mechanics of chip formation is widely known to be a robust indicator of cutting performance. While chips have been studied and classified for several decades, a direct quantitative correlation between chip morphology and properties of the resulting machined surface is yet to be established. In this work, we develop and demonstrate a digital image correlation (DIC) technique that specifically addresses this issue. Our technique is a departure from standard DIC methods in that it uses an iterative technique on multiple independent random grids, allowing us to obtain accurate full-field deformation measurements during chip formation. The method works especially well at or near free surfaces (e.g., machined surface) and interfaces (e.g., tool-chip contact) and when deformation is temporally unsteady and spatially non-homogeneous. Given that these are central features of nearly all cutting processes, we show how our technique can quantify unsteady, non-uniform flow fields, residual surface and near-surface strains, as well as the amount of redundant deformation during chip formation. These results suggest the use of a simple heuristic test to evaluate critical process performance metrics, such as part surface quality, sub-surface damage and relative energy consumption during machining.