Опубликована: Апрель 15, 2025

Some of the critical components in industrial lifting operations are lugs, elements traditionally designed with conservative approaches that prioritize safety over material efficiency, resulting oversized designs. This study proposes an innovative methodological framework employs Generative Artificial Intelligence (GAI) to optimize these components. The used is AISI 304 steel, which economical and widely available, goal reducing mass without compromising structural strength. By utilizing finite element analysis (FEA) simulations Autodesk Inventor genetic algorithms Fusion 360, a comparison was made between traditional design based on DIN 580 standard optimized designs generated by software. Three manufacturing methods were also considered: additive manufacturing, three-axis milling, casting. results demonstrated reduction up 91% scenario, along improvements factor 2.765 notable enhancement stress distribution uniformity. Another significant finding decrease maximum displacement under dynamic loading, from 0.0189 mm (standard-based design) 0.004 (generatively design), indicates increased stiffness. methodology not only overcomes limitations conventional but offers flexibility adapt various production processes, both economic (20% savings per unit) environmental (reduced carbon footprint) benefits. validates potential GAI simple using readily accessible materials, offering replicable for sectors such as renewable energy electric automotive applications. Future research should include experimental validations fatigue studies further consolidate advances real environments.

Язык: Английский