Аңдатпа
Material selection for surgical robotic manipulators remains a challenging engineering problem because candidate materials must simultaneously satisfy mechanical, biomedical, environmental, manufacturing, and economic requirements. This challenge is particularly important in robotic systems for knee arthroplasty, where structural reliability and positioning accuracy strongly depend on material properties. This study presents the development of a structured component-oriented material database and an automated material selection algorithm for a collaborative surgical manipulator intended for knee joint endoprosthesis procedures. The database includes metallic materials, engineering polymers, composites, and ceramics together with their mechanical, environmental, biomedical, manufacturing, and economic characteristics. Based on this database, a multi-stage decision-support framework was developed that integrates component-specific material retrieval, hard-constraint filtering, criterion normalization, adaptive weighting, TOPSIS-based ranking, and post-ranking feasibility verification. Unlike conventional approaches that evaluate all materials simultaneously, the proposed framework retrieves only materials relevant to the selected manipulator component, reducing the search space and improving decision consistency. A numerical case study was performed for the load-bearing structure. The algorithm retrieved 21 candidate materials with complete data, of which 6 satisfied the mandatory engineering and biomedical constraints. These materials were subsequently ranked using the TOPSIS method, with AISI 316 identified as the most suitable material because of its balanced combination of mechanical strength, corrosion resistance, sterilization compatibility, and durability. Comparative evaluation using the SAW method produced the same three highest-ranked materials, while sensitivity analysis demonstrated that the ranking remained stable under moderate variations in weighting coefficients. The proposed framework provides a transparent, reproducible, and practically applicable approach to material selection for surgical robotic manipulators and establishes a foundation for future integration with CAD/CAE environments, intelligent decision-support systems, and machine-learning-assisted engineering design.