Compositional optimization of glass forming alloys based on critical dimension by using artificial neural network

基于临界尺寸采用人工神经网络技术优化设计玻璃形成合金的成分(英文)

An artificial neural network （ANN） model was developed for simulating and predicting critical dimension dc of glass forming alloys. A group of Zr-Al-Ni-Cu and Cu-Zr-Ti-Ni bulk metallic glasses were designed based on the dc and their de values were predicted by the ANN model. Zr-Al-Ni-Cu and Cu-Zr-Ti-Ni bulk metallic glasses were prepared by injecting into copper mold. The amorphous structures and the determination of the dc of as-cast alloys were ascertained using X-ray diffraction. The results show that the predicted de values of glass forming alloys are in agreement with the corresponding experimental values. Thus the developed ANN model is reliable and adequate for designing the composition and predicting the de of glass forming alloy.

建立一个用于预测和模拟玻璃形成合金的临界尺寸的人工神经网络模型;基于该人工神经网络模型优化设计一系列Zr-Al-Ni-Cu和Cu-Zr-Ti-Ni块体非晶合金的成分并对其临界尺寸进行预测。采用真空喷注法制备Zr-Al-Ni-Cu和Cu-Zr-Ti-Ni块体非晶合金试样。这些块体合金的非晶态结构采用X射线衍射法进行表征并确定这些合金的非晶形成的临界尺寸。结果表明,预测的临界尺寸与实验结果吻合较好,所建立的神经网络模型能可靠地设计非晶合金的成分和预测非晶合金的临界尺寸。