Tool-wear-aware deformation prediction of thin-walled parts in multi-stage milling

Thin-walled components made of difficult-to-machine materials are widely used in aerospace and energy industries but are highly susceptible to machining-induced deformation. Existing deformation prediction approaches are commonly oriented towards single-stage milling and assume stationary process conditions and therefore fail to capture the influence of tool wear, which introduces continuously evolving loading and stress states during multi-stage milling. To address this limitation, this study proposes a state-dependent deformation prediction framework, established under simplified modeling assumptions, in which tool wear is explicitly modeled as a continuously evolving internal variable governing deformation evolution. The framework integrates wear-dependent cutting-force-induced elastic deflection and machining-induced residual stress (MIRS)-driven distortion within a unified finite element-based prediction scheme. Multi-stage milling experiments on GH4169 thin-walled parts are conducted under stress-relieved conditions for validation. The results show that average deformation prediction errors remain below 10% for machined surfaces and below 15% for unmachined regions across all machining stages. Neglecting tool wear leads to prediction errors exceeding 45% during rough machining, and MIRS-induced distortion prediction deviations can exceed 100% at a few nodes, demonstrating the critical role of wear-induced loading evolution in improving deformation prediction accuracy. The findings reveal that deformation in multi-stage milling is fundamentally governed by evolving process states, highlighting the necessity of explicitly incorporating tool wear into deformation prediction models.