This paper presents a novel method for the optimization of the dynamic behavior of a laminated cylinder through stacking sequence optimization. The number of layers reaches 48 and the optimized parameters are either the value of the fundamental natural frequency or the width of the frequency gaps around the excitation force frequencies. The proposed procedure involves automatic mode shape identification and a combined genetic algorithm–deep neural network procedure along with a Curriculum Learning loop, enabling the improvement of accuracy and reduction of computational costs. The proposed optimization algorithm is accurate, robust, and significantly faster than typical genetic algorithm optimization, in which the objective function values are calculated using the finite element method. A rough rule for the estimation of the necessary number of patterns is proposed and the optimal results obtained using the proposed approach are compared to the results obtained using a standard approach (without mode shape identification), in order to demonstrate the effectiveness and robustness of the new method.

本文提出了一种通过叠加顺序优化来优化叠层圆柱体动力学行为的新方法。层数达到48，优化参数是基本固有频率的值或激励力频率周围的频率间隙的宽度。拟议的程序涉及自动模式形状识别和遗传算法-深层神经网络程序以及课程学习的结合循环，可以提高准确性并降低计算成本。所提出的优化算法准确，可靠且比典型的遗传算法优化更快，在遗传算法中，使用有限元方法计算目标函数值。提出了估计所需图案数量的粗略规则，并将使用该方法获得的最佳结果与使用标准方法（无模式形状识别）获得的结果进行比较，以证明该方法的有效性和鲁棒性。新方法。