Multi-parameter simultaneous optimization of robotic ball-end milling based on an accuracy-efficiency index

Robotic ball-end milling has shown great potential for localized machining of large complex structural components. However, the inherent low structural stiffness of industrial robots can lead to severe force-induced deformations, while the complex cutter-workpiece engagement (CWE) relationship and pronounced speed effects of ball-end milling cutters introduce strong coupling among multiple influencing factors. These challenges significantly hinder the multi-parameter optimization of robotic milling performance. To address this, the study proposes a multi-parameter simultaneous optimization strategy to enhance both machining accuracy and efficiency. Specifically, an Accuracy-Efficiency Index is formulated, which integrates the force-induced normal deformation error and the CWE volume for comprehensive performance evaluation. A speed sensitivity constraint model based on variations in axial cutting velocity is then introduced to identify tool postures with stable cutting behavior. Finally, a problem-oriented two-stage hybrid optimization framework combining genetic algorithm and sequential quadratic programming is proposed to robustly determine the optimal parameter set (γ, ε, β, η). Comparative results further show that the proposed AEI provides a more coordinated parameter solution by jointly considering deformation control and effective cutting engagement, rather than optimizing either aspect alone. Robotic milling experiments show that the proposed method reduces deformation error by 43.3% and increases material removal rate by 8.3%, thereby significantly improving machining quality while moderately enhancing machining efficiency, and providing useful support for the engineering application of robotic ball-end milling.