Research on industrial structural design methods based on parametric modeling and deep learning optimization algorithms

As the demands for structural performance and design efficiency in industrial products continue to rise, the limitations of traditional structural design methods in terms of modeling flexibility and optimization capabilities have become increasingly apparent. This paper addresses the need for efficient modeling and performance optimization in industrial structural design by proposing a systematic design method that integrates parametric modeling with intelligent optimization algorithms. Based on the concept of parameter-driven modeling, a reconfigurable structural parameter system is established to establish a connection between structural geometry and design variables. Multiple intelligent optimization algorithms, including genetic algorithms and particle swarm optimization, are designed and integrated to address multi-objective requirements such as structural lightweighting, strength enhancement, and cost control, thereby establishing an integrated modeling and optimization workflow. The proposed method is validated through modeling and optimization experiments on typical industrial structures. Experimental results demonstrate that this method not only significantly enhances the automation and intelligence of structural design but also exhibits advantages in design efficiency, result accuracy, and system adaptability. It provides a feasible path for rapid iterative optimization of complex engineering structures and holds promising engineering application prospects and promotional value.