Dr. Angela Bernardini, CITEAN
Virtual engineering technology has undergone rapid progress in recent years and has been widely accepted for commercial product development. Product design and manufacturing organizations are moving from the traditional multiple and serial test cycle approach to simulation, which solves problems and validates performances using CAE and CAD tools.
For an efficient process, it is essential that design variants can be done within a short time frame. This generally leads to a challenge when the system under study exhibits nonlinear behavior. This session introduces a new methodology based on neural networks (NNs) and genetic algorithms (GAs), which “put data to work” and provide the best possible solution for a given design based on the available data. The goal of this methodology is to provide designers with a tool that can be used to select the optimum design for a given product. This is possible thanks to the optimization of the NN itself through GA implementation based on the available training data. Genetic algorithms have been used for neural networks in two main ways: to optimize the network architecture and to train the weights of a fixed architecture.
NN的性能尺寸依赖于其他变量,选择元素(神经元),架构和学习算法的选择。特别地,连接密度(神经元)确定其存储信息并从中学习的能力。一方面,减少的连接数量可以禁用网络以近似该功能。另一方面,密集连接可能导致过度装备。通常被视为使用与自适应权重连接的简单基本单元实现复杂非线性函数的方法。我们专注于优化这些网络的连接结构,使用气体降低学习时间,避免CAD / CAE环。实际上,该实现提供了神经网络拓扑,通常,当他们学习和分类新数据时,通常比随机或完全连接的拓扑更好。
Genetic operators, such as mutation and cross-over, introduce variety into the initial randomly connected population, modifying the network’s architecture and testing candidate solutions. Once the most effective NN is trained, it is possible to adjust the design parameters, with the same accuracy as FEA or testing data, but sharply reducing the simulation time: The approximate hour and an half needed to analyze critical points by FEA is reduced to few seconds using neural networks. A MATLAB graphical user interface (GUI) works as a quick design guide, where the training data for the NN is obtained from a set of automatically generated FEA analyses. To assess the effectiveness of this methodology, several practical applications are shown. As an example, the optimal preload for bolted joints is returned in a few seconds starting from bolt’s geometry, friction coefficient, and applied torque.
Recorded: 22 Jun 2010
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