Authors - Yavor Dankov, Boyan Bontchev, Valentina Terzieva, Elena Paunova-Hubenova, Aleksandar Dimov Abstract - The growing demand for lightweight, high-performance, and sustain-able machine structures has accelerated the adoption of intelligent digital design methodologies in modern manufacturing. Conventional CAD-based design approaches rely heavily on manual iterations, limiting efficient exploration of complex design spaces and multi-objective trade-offs. This paper presents a hybrid AI-assisted generative design and topology optimization framework for intelligent lightweight optimization of machine structural components, with ap-plication to column-type machine structures and complex non-prismatic industrial brackets. The proposed framework integrates parametric CAD modeling, finite-element-based structural analysis, CAD-embedded generative design, and an AI-inspired algorithmic decision layer for automated evaluation and ranking of design alternatives. Key performance indicators—including mass, stiffness, stress, deflection, fatigue index, and additive-manufacturing constraints—are digitally processed and combined into a composite performance score to sup-port objective design selection. In the first case study, a rectangular machine column is evaluated across multiple volume-fraction configurations, achieving approximately 20% mass reduction while retaining 96% structural stiffness with minimal increases in stress and deflection. The second case study applies generative design to a complex industrial support bracket under multiple load cases, generating twelve feasible solutions that are algorithmically ranked based on performance and manufacturability. The results confirm that AI-assisted evaluation enables efficient design space exploration and supports intelligent, sustain-ability-driven engineering decisions for advanced digital manufacturing systems.
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