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In the era of Industry 4.0, additive manufacturing (AM) technology plays a crucial role in optimizing production processes, especially for small- and medium-sized enterprises (SMEs) striving to enhance competitiveness. Selecting the appropriate material for AM is a complex process that requires considering numerous technical, economic, and environmental criteria. Fuzzy logic-based advisory systems can effectively support decision-making in conditions of uncertainty and subjective user preferences. This study presents a developed advisory system model that uses the Analytic Hierarchy Process (AHP) method and triangular and trapezoidal membership functions, enabling dynamic adjustment of criterion weights. The results demonstrated that the system achieved 85% alignment with user preferences, confirming its effectiveness. Future research may focus on integrating fuzzy logic with machine learning algorithms to further enhance the system's precision and flexibility.
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