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首页> 外文会议>International Conference on Advances in Materials and Manufacturingngineering >Prediction of surface roughness in Magneto rheological Abrasive flow finishing process by artificial neural networks and regression analysis
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Prediction of surface roughness in Magneto rheological Abrasive flow finishing process by artificial neural networks and regression analysis

机译：通过人工神经网络预测磁化流变磨削流动整理过程的表面粗糙度及回归分析

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In this study, to predict the surface roughness of stainless steel-304 in Magneto rheological Abrasive flow finishing (MRAFF) process, an artificial neural network (ANN) and regression models have been developed. In this models, the parameters such as hydraulic pressure, current to the electromagnet and number of cycles were taken as variables of the model. Taguchi's technique has been used for designing the experiments in order to observe the different values of surface roughness. A neural network with feed forward with the help of back propagation was made up of 27 input neurons, 7 hidden neurons and one output neuron. The 6 sets of experiments were randomly selected from orthogonal array for training and residuals were used to analyze the performance. To check the validity of regression model and to determine the significant parameter affecting the surface roughness, Analysis of variance (ANOVA) and F-test were made. The numerical analysis depict that the current to the electromagnet was an paramount parameter on surface roughness.
机译：在该研究中，为了预测磁流变磨料流动整理（MRAFF）过程中不锈钢-304的表面粗糙度，已经开发了人工神经网络（ANN）和回归模型。在该模型中，诸如液压的参数，电流与电磁铁的电流和循环次数被视为模型的变量。 Taguchi的技术已被用于设计实验，以观察表面粗糙度的不同值。通过回繁殖的帮助前向前饲料的神经网络由27个输入神经元，7个隐藏神经元和一个输出神经元组成。从正交阵列中随机选择6组实验，用于训练，残留物用于分析性能。为了检查回归模型的有效性，并确定影响表面粗糙度的重要参数，进行方差分析和F检验。数值分析描绘了电磁铁的电流是表面粗糙度的最低参数。

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