Continuous fiber-reinforced polymer composites exhibit superior mechanical properties critical for engineering applications demanding high strength-to-weight ratios. Additive manufacturing enables customized fiber placement and fabrication of complex geometries, particularly for shell structures. Existing research mostly exploits the stress fields in homogeneous components and optimizes fiber layouts by aligning the fibers with the principal stress fields. However, fiber incorporation substantially alters stress distribution, potentially resulting in suboptimal fiber path design without considering the evolution of stress fields across successive layers. Additionally, existing methodologies typically formulate the fiber optimization as a direct 3D problem, thereby encountering significant challenges when simultaneously addressing spatial fiber layouts, stress field considerations, fiber reinforcement effects, and manufacturing constraints. This work presents a novel, efficient framework for the design and manufacturing of mechanically optimized multi-layer composite shells through fiber path optimization strategies. The 3D shell surface is initially mapped to a 2D domain using modified quasi-conformal mapping, with a derived semi-analytical relationship characterizing distortion and expansion. Within the 2D domain, the component's stress field is computed iteratively, and the evolving stress distributions guide a multi-objective optimization incorporating density-controllable path extraction algorithms to generate continuous fiber paths. Furthermore, a constrained one-stroke toolpath generation algorithm is formulated to address manufacturing limitations while ensuring fiber continuity. Apart from the effectiveness of the proposed method shown by simulation results, experimental validation through multi-axis additive manufacturing confirms that optimized structures achieve enhanced structural stiffness and increased peak load capacity compared to conventional constant spacing and orientation fiber patterns, as well as existing stress-guided fiber optimization approaches.
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