The structural optimization of internal flow channels in hydraulic systems is critical for enhancing overall performance and achieving lightweight design. However, most existing optimization studies focus primarily on improving flow performance while neglecting manufacturability constraints, resulting in designs that are often infeasible to fabricate and thus hindering their practical application. To address this issue, this study proposes a level set-based process planning strategy tailored for additive-subtractive hybrid manufacturing (ASHM), aiming to resolve the manufacturability challenges associated with flow channels generated by machine learning-based optimization algorithms. The proposed method segments the flow channel into linear and curved sections based on internal wall features. By converting the 3D model into representative 2D feature images, the level set functions of each section are evolved individually and integrated with toolpath constraints to enable automatic identification of manufacturing regions and assignment of appropriate additive or subtractive operations. Through iterative calculations involving geometric relationships and processing trajectories, the start and end positions of additive and subtractive processes are determined, ultimately yielding a comprehensive process plan that includes manufacturability-constrained subdivisions and corresponding fabrication strategies. A case study on an optimized flow channel within a simplified hydraulic manifold demonstrates that the proposed method reduces the pressure drop by 79.41%, thereby validating its effectiveness in terms of machining accuracy and toolpath planning feasibility.
Li et al. (Thu,) studied this question.
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