Machine Learning-Based Hardness Prediction of High-Entropy Alloys for Laser Additive Manufacturing

Abstract

High-entropy alloys (HEAs) have attracted much attention for laser additive manufacturing, due to their superb mechanical properties. However, their industry application is still hindered by the high entry barriers of design for additive manufacturing and the limited performance library of HEAs. In most machine learning methods used to predict the properties of HEAs, their processing paths are not clearly distinguished. To overcome these issues, in this work, a novel deep neural network architecture is proposed that includes HEA manufacturing routes as input features. The manufacturing routes, i.e., as-cast and laser additive manufactured samples, are transformed into the One-Hot encoder. This makes the samples in the dataset provide better directivity and reduces the prediction error of the model. Data augmentation with conditional generative adversarial networks is employed to obtain some data samples with a distribution similar to that of the original data. These additional added data samples overcome the shortcoming of the limited performance library of HEAs. The results show that the mean absolute error value of the prediction is 44.6, which is about 27% lower than that using traditional neural networks in this work. This delivers a new path to discover chemical compositions suitable for laser additive manufactured HEAs, which is of universal relevance for assisting specific additive manufacturing processes.