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首页> 外文期刊>The International Journal of Advanced Manufacturing Technology >Application of adaptive neuro fuzzy inference system and genetic algorithm for pressure path optimization in sheet hydroforming process
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Application of adaptive neuro fuzzy inference system and genetic algorithm for pressure path optimization in sheet hydroforming process

机译：自适应神经模糊推理系统和遗传算法在板材液压成形压力路径优化中的应用

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摘要

One of the most important parameters in success of sheet hydroforming process is loading (pressure) path. Improper pressure of fluid chamber during the process may cause a number of defects such as necking, tearing, and wrinkling. Theoretical calculations and finite element trial-and-error simulations to find the optimum pressure paths are so costly and time-consuming. This study underlines the application of adaptive neuro fuzzy inference system (ANFIS) and genetic algorithm (GA) for pressure path optimization in hydrodynamic hydroforming process of cylindrical-spherical parts. In this research, an ANFIS model has been developed based on finite element simulation results to identify the effect of the pressure path on the maximum thinning in the critical region of the part. In subsequent step, the ANFIS model operated as an objective function for optimization process. For this purpose, GA was incorporated into the ANFIS model to acquire the optimal pressure path in order to obtain minimum thinning in the critical region of the part. The results showed that the combination of adaptive neuro fuzzy inference approach and optimization algorithm is a good scheme to predict an improved loading pressure path minimizing the thinning in the critical region of the part and avoiding numerous trial and error simulations or experiments.
机译：片材液压成型成功的最重要参数之一是加载（压力）路径。在此过程中，流体腔室压力不当可能会导致许多缺陷，例如缩颈，撕裂和起皱。寻找最佳压力路径的理论计算和有限元试验和错误模拟非常昂贵且耗时。这项研究强调了自适应神经模糊推理系统（ANFIS）和遗传算法（GA）在圆柱球形零件流体动力液压成形过程中压力路径优化中的应用。在这项研究中，基于有限元模拟结果开发了ANFIS模型，以识别压力路径对零件关键区域中最大减薄的影响。在随后的步骤中，ANFIS模型用作优化过程的目标函数。为此，将GA合并到ANFIS模型中以获取最佳压力路径，以便在零件的关键区域获得最小的减薄。结果表明，自适应神经模糊推理方法和优化算法的组合是一种预测改进的加载压力路径的最佳方案，该路径可最大程度地减少零件关键区域的变薄，并避免了多次试验和错误模拟或实验。

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