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首页> 外文期刊>Journal of Materials Engineering and Performance >Microstructure-Tensile Properties Correlation for the Ti-6Al-4V Titanium Alloy
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Microstructure-Tensile Properties Correlation for the Ti-6Al-4V Titanium Alloy

机译：Ti-6Al-4V钛合金的组织与拉伸性能相关

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摘要

Finding the quantitative microstructure-tensile properties correlations is the key to achieve performance optimization for various materials. However, it is extremely difficult due to their non-linear and highly interactive interrelations. In the present investigation, the lamellar microstructure features-tensile properties correlations of the Ti-6Al-4V alloy are studied using an error back-propagation artificial neural network (ANN-BP) model. Forty-eight thermomechanical treatments were conducted to prepare the Ti-6Al-4V alloy with different lamellar microstructure features. In the proposed model, the input variables are microstructure features including the alpha platelet thickness, colony size, and beta grain size, which were extracted using Image Pro Plus software. The output variables are the tensile properties, including ultimate tensile strength, yield strength, elongation, and reduction of area. Fourteen hidden-layer neurons which can make ANN-BP model present the most excellent performance were applied. The training results show that all the relative errors between the predicted and experimental values are within 6%, which means that the trained ANN-BP model is capable of providing precise prediction of the tensile properties for Ti-6Al-4V alloy. Based on the corresponding relations between the tensile properties predicted by ANN-BP model and the lamellar microstructure features, it can be found that the yield strength decreases with increasing alpha platelet thickness continuously. However, the alpha platelet thickness exerts influence on the elongation in a more complicated way. In addition, for a given alpha platelet thickness, the yield strength and the elongation both increase with decreasing beta grain size and colony size. In general, the beta grain size and colony size play a more important role in affecting the tensile properties of Ti-6Al-4V alloy than the alpha platelet thickness.
机译：找到定量的微观结构-拉伸性能相关性是实现各种材料性能优化的关键。但是，由于它们之间的非线性和高度交互性，这非常困难。在本研究中，使用误差反向传播人工神经网络（ANN-BP）模型研究了Ti-6Al-4V合金的层状组织特征与拉伸性能的相关性。进行了四十八次热机械处理，以制备具有不同层状组织特征的Ti-6Al-4V合金。在建议的模型中，输入变量是微观结构特征，包括使用Image Pro Plus软件提取的α血小板厚度，菌落大小和β晶粒大小。输出变量是拉伸性能，包括极限拉伸强度，屈服强度，伸长率和面积减小。应用了十四个可以使ANN-BP模型表现出最佳性能的隐层神经元。训练结果表明，预测值与实验值之间的所有相对误差均在6％以内，这意味着训练后的ANN-BP模型能够为Ti-6Al-4V合金的拉伸性能提供精确的预测。根据ANN-BP模型预测的拉伸性能与层状微结构特征之间的对应关系，可以发现屈服强度随α血小板厚度的不断增加而降低。然而，α血小板厚度以更复杂的方式影响伸长率。另外，对于给定的α血小板厚度，屈服强度和伸长率均随着β晶粒尺寸和菌落尺寸的减小而增加。通常，与α血小板厚度相比，β晶粒尺寸和菌落尺寸在影响Ti-6Al-4V合金的拉伸性能中起着更重要的作用。

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