Improved artificial neural network for data analysis and property prediction in slag glass-ceramic

Abstract

The development of slag glass-ceramics has environmental and commercial value. However, new types of these materials are usually developed using the "trial and error" method because of little understanding of the relationship between the composition, processing, microstructure, and properties. In this paper, artificial neural network (ANN) technology was applied to investigate the relationship between the composition content and the properties of slag glass-ceramic. The investigation showed that the ANN model had an outstanding learning ability and was effective in complex data analysis. If the data set reflects the relationship of the composition and property, the trained network will learn the relationship and then give relatively accurate and stable prediction. A new "virtual sample" technology has also been created which improves the prediction performance of the network by providing greater accuracy and reliability. With this virtual sample technology, the ANN model can establish the exact relationship from a small-size-data set, and gives accurate predictions. This improved ANN model is a powerful and reliable tool for data analysis and property prediction, and will facilitate the material design and development of slag glass-ceramics.