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An abductive neural network approach to the design of runner dimensions for the minimization of wrappage in injection mouldings

机译:浇铸流道尺寸设计的一种仿生神经网络方法,可最大程度地减少注塑成型中的包裹率

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In this paper, the finite element and abductive network methods were used to obtain a runner-system design to reach minimum warppage on an injection moulded parts with free-form geometry. This study uses the diameter and length of the runner-system as the major control parameters. In the model creation stage, FEM simulation data were used to derivate an accurate abductive network model for predicting wrappage in injection moulded parts corresponding to different control parameters. In the optimization stage, the simulated annealing (SA) method was used. This abductive methodology allows user to efficiently discover an optimal set of parameters without involving complex iteration between the optimization process and FEM simulation.
机译:在本文中,使用有限元和外延网络方法来获得流道系统设计,以使具有自由形式几何形状的注塑零件达到最小翘曲。本研究使用流道系统的直径和长度作为主要控制参数。在模型创建阶段,使用有限元仿真数据得出精确的外展网络模型,以预测与不同控制参数对应的注塑件的包裹。在优化阶段,使用了模拟退火(SA)方法。这种归纳方法使用户可以有效地发现一组最佳参数,而无需在优化过程和FEM仿真之间进行复杂的迭代。

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