To develop a spindle head that meets stringent workspace and acceleration requirements for high-efficiency machining, this study proposes a geometric-parameter optimization framework that integrates performance atlases with a Pareto-based multi-objective optimization algorithm. Novel inertia-matrix-based performance indices are introduced to enable accurate evaluation of the spindle head’s linear and angular acceleration capabilities. The kinematic and dynamic performance distributions are subsequently mapped over the design space, and a feasible high-performance design region is identified using predefined performance constraints. Within this region, Pareto optimization is performed to generate a Pareto front, from which the optimal geometric parameters are selected. The spindle head performance, based on the optimized geometric parameters, is verified through dynamic simulations under representative cutting-force conditions; under the specified acceleration profiles, the maximum actuator driving force is . The results confirm that the spindle head satisfies the acceleration requirements while maintaining actuator forces within practical limits, thereby supporting the development of next-generation high-performance hybrid machine tools.
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