This thesis investigates Physics-Informed Neural Networks (PINNs) for predicting flank wear (VB) in milling, targeting scenarios where data are scarce and purely data-driven models struggle to generalize. The proposed PINN embeds an extended Taylor tool-life relationship through a learnable time scaling T_f and re-parameterized exponents (n, m, p) with a positive scale K. The network ingests dimensionless time τ = t/T_f together with normalized process settings (feed, depth of cut, material) and is trained with a composite objective that combines data fitting on the available VB labels, an initial-condition penalty to enforce near-zero wear at the start, and a physics anchor that nudges the prediction at τ = 1 toward a robust reference wear level.