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Parametric Optimization of Permeability of Green Sand Mould Using ANN and ANFIS Methods

机译:使用ANN和ANFIS方法参数优化青砂模具的渗透性

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In foundry industries, various additives are used to increase the sand mould properties such as green strength and permeability number. In the present paper, camphor has been used as additive to enhance the mould's permeability so as to improve the casting quality. The optimum quantity of camphor that can be added to the sand mixture was found to be 1 wt%. Further, prediction of green sand mould permeability number has been done using both artificial neural network (ANN) and adaptive neuro-fuzzy interference system (ANFIS). The models were built using experimental data as per Taguchi's L27 orthogonal array (OA). The predicted permeability numbers by both models were found to be very close to that of experimental values; however, the predictability of ANFIS model was found to be better than ANN model as the error percent was less in former case.
机译:在铸造行业中,各种添加剂用于增加砂模特性,如绿色强度和渗透率。 在本文中,樟脑已被用作添加剂以增强模具的渗透性,以提高铸造质量。 可以将可以加入到砂混合物中的樟脑的最佳量为1wt%。 此外,使用人工神经网络(ANN)和自适应神经模糊干扰系统(ANFIS)进行了预测绿色砂模渗透率。 根据Taguchi的L27正交阵列(OA),使用实验数据建造了模型。 发现两种模型的预测渗透性数字非常接近实验值的非常接近; 然而,发现ANFIS模型的可预测性比ANN模型更好,因为误差百分比在以前的情况下较少。

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