Magnetorheological (MR) fluids are intelligent fluids that change their state under a magnetic field and can be extensively applied in several industries. In this study, a model was presented to predict the MR behavioral trend of magnetite-based MR fluids using deep neural networks. The MR data of nine samples with several magnetite nanoparticle concentrations and different silicone oil viscosities were used for network construction and testing; the aforementioned data were obtained under several magnetic field strengths. Seven samples were used for network training/testing within the training interval and two samples were applied for evaluating the network accuracy outside the network training interval. Several networks, such as the multi-layer perceptron (MLP), radial basis function, and adaptive neuro-fuzzy inference system, were employed, and the results were analyzed. The accuracy parameters (R² and RMSE) of the MLP network for the training data (0.99625 and 0.00867) and test data (0.99130 and 0.01621), as well as a comparison between the predicted and laboratory-measured results of the two samples that had not been used in the modeling step, demonstrated the exceptional performance of the proposed method and an equation that was derived for predicting the shear stress. The latter equation enables researchers to achieve their needs without performing time-and cost-consuming MR tests in the laboratory.

磁流变（MR）流体是一种智能流体，可在磁场作用下改变其状态，可广泛应用于多个行业。在这项研究中，提出了一个模型来使用深度神经网络预测基于磁铁矿的 MR 流体的 MR 行为趋势。使用具有多种磁铁矿​​纳米颗粒浓度和不同硅油粘度的九个样品的 MR 数据进行网络构建和测试；上述数据是在几种磁场强度下获得的。七个样本用于训练区间内的网络训练/测试，两个样本用于评估网络训练区间外的网络准确性。采用了多种网络，例如多层感知器 (MLP)、径向基函数和自适应神经模糊推理系统，并对结果进行了分析。精度参数（训练数据（0.99625 和 0.00867）和测试数据（0.99130 和 0.01621）的 MLP 网络的R ²和 RMSE），以及未使用的两个样本的预测和实验室测量结果之间的比较建模步骤展示了所提出的方法的卓越性能以及用于预测剪切应力的方程。后一个等式使研究人员无需在实验室中进行耗时费力的 MR 测试即可满足他们的需求。