Systematic Study on the Application of Mechanical Optimization Algorithms Based on Deep Learning in Product Design

With the increasing complexity of product design requirements, traditional mechanical optimization methods are facing challenges in accuracy, efficiency and adaptability. This paper proposes an innovative mechanical optimization algorithm based on deep learning, which aims to optimize the structural strength and material utilization rate in the product design process. This paper combines CNN with traditional mechanical analysis methods and develops an adaptive optimization strategy. Through the local feature extraction ability and global optimization constraints of CNN, the optimization accuracy and computational efficiency are significantly improved. This paper selects automobile body and bridge design as cases to compare deep learning optimization with traditional optimization methods. Simulation results show that the deep learning optimization algorithm performs better than traditional methods in multiple design scenarios, especially in terms of computational efficiency, which is improved by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{3 0 \%}$</tex>, and in automobile body design, the strength is improved by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{2 0 \%}$</tex> and the material utilization rate is improved by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 0 \%}$</tex>. This study not only provides new ideas for mechanical optimization in product design, but also provides efficient and feasible solutions for structural optimization in actual engineering design.