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首页> 外文会议>International Conference on Robot Intelligence Technology and Applications >The Flexural Strength Prediction of Porous Cu-Sn-Ti Composites via Artificial Neural Networks
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The Flexural Strength Prediction of Porous Cu-Sn-Ti Composites via Artificial Neural Networks

机译：人工神经网络多孔Cu-Sn-Ti复合材料的弯曲强度预测

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摘要

Porous alloy-composites have demonstrated excellent qualities with regards to grinding superalloys. Flexural strength is an important mechanical property associated with the porosity level as well as inhomogeneity in porous composites. Owing to the non-linear characteristics of the constituents of the composite material, the prediction of specific mechanical properties by means of the conventional regression model is often unsatisfactory. Therefore, the utilisation of artificial intelligence for the prediction of such properties is non-trivial. This study evaluates the efficacy of artificial neural network (ANN) in predicting the flexural strength of porous Cu-Sn-Ti composite with Molybdenum disulfide (MoS2) particles. The input parameters of the ANN model are the average carbamide particles size, the porosity volume as well as the weight fraction of the MoS2 particles. The determination of the number of hidden neurons of the single hidden layer ANN model developed is obtained via an empirical formulation. The ANN model developed is compared to a conventional multiple linear regression (MLR) model. It was demonstrated that the ANN-based model is able to predict well the flexural strength of the porous-composite investigated in comparison to the MLR model.
机译：多孔合金复合材料在研磨超合金方面表现出优异的品质。弯曲强度是与孔隙率水平相关的重要力学性质以及多孔复合材料中的不均匀性。由于复合材料成分的非线性特性，通过传统的回归模型预测特定的机械性能通常是不令人满意的。因此，用于预测这些性质的人工智能的利用是非微不足道的。该研究评估了人工神经网络（ANN）预测多孔Cu-Sn-Ti复合材料与二硫化钼（MOS2）颗粒的弯曲强度的效果。 ANN模型的输入参数是平均氨基胺颗粒尺寸，孔隙体积以及MOS2颗粒的重量分数。通过经验制剂获得所开发的单个隐藏层ANN模型的隐藏神经元数的确定。将ANN模型与传统的多线性回归（MLR）模型进行比较。结果证明基于ANN的模型能够预测与MLR模型相比研究的多孔复合材料的弯曲强度。

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