Xingliang Chai

When a thin-walled grid conical shell is milled, the rib layout makes local stiffness jump from tool position to tool position, and the cutting force pushes the workpiece into an elastic retreat along the shell normal, so the actual wall thickness falls short of the theoretical depth of cut. The new route treats finite-element simulation as the source domain and real milling experiments as the target domain, uses transfer learning to predict the normal cutting force first, then feeds that load into an equivalent-stiffness analytical model to compute deflection. Across four test sets the method reached 93.05% to 95.27% prediction accuracy, above the CNN, PINN and TL comparison models.

When a thin-walled grid conical shell is milled, the rib layout makes local stiffness jump from tool position to tool position, and the cutting force pushes the workpiece into an elastic retreat along the shell normal, so the actual wall thickness falls short of the theoretical depth of cut. The new route treats finite-element simulation as the source domain and real milling experiments as the target domain, uses transfer learning to predict the normal cutting force first, then feeds that load into an equivalent-stiffness analytical model to compute deflection. Across four test sets the method reached 93.05% to 95.27% prediction accuracy, above the CNN, PINN and TL comparison models.