Voxel-based method for predicting workpiece chipping in end milling of unsintered pure iron-powder compact

The miniaturization and high-torque requirements of electric motors in automotive and industrial applications have increased the adoption of axial-gap motors that employ unsintered pure iron-powder compacts. However, machining these brittle materials, particularly through end milling, typically results in significant workpiece chipping, which impedes cost-effective prototyping and small-lot production. Conventional chipping-prediction approaches, such as finite-element analysis and critical uncut chip-thickness methods, are limited by their computational costs and prediction accuracy, respectively. This study proposes a novel method for predicting chipping regions in the end milling of pure iron-powder compacts via voxel-based cutting-force simulation. The chipping risk at each voxel was evaluated based on the magnitude and direction of the simulated cutting force and local workpiece rigidity. Chipping was predicted when the risk index exceeded the threshold value. Cutting experiments were conducted to validate the proposed method, which shows good agreement between the predicted and observed chipping regions under various milling conditions. The results indicate that the proposed method can efficiently and accurately predict the chipping regions, thus outperforming conventional approaches in terms of computational cost. Although parameter tuning and threshold calibration were performed experimentally, the voxel-based framework enables practical prediction and analysis of transient machining phenomena. Future investigations shall focus on expanding the method to a wider range of machining conditions and integrating material-property considerations for further generalization. This approach offers a practical tool for optimizing machining parameters to minimize chipping and enhance the manufacturability of brittle powder compacts.