Modeling the relationship between hydrogen content and mechanical property of Ti600 alloy by using ANFIS

Abstract An adaptive network based fuzzy inference system (ANFIS) was used to predict the tensile strength of Ti600 alloy after hydrogenation. In this approach, the whole procedure of structure identification and parameter optimization was carried out automatically and efficiently. Two major influence factors were considered as input variables including hydrogenation temperature ranging of 600–750 °C and hydrogen content ranging of 0–0.502 wt.%, two different membership functions, triangular and Gaussian, were employed as fuzzy subset to compare the model accuracy. After the training and testing process, the predicted values were compared with experimental result and multiple linear regression values. It is found that the ANFIS predicted values with Gaussian and Triangular membership function were in good agreement with the experimental results with relative error of 2.69% and 2.77%, which has a equal accuracy but higher than multiple linear regression method with the error of 16.74%. Such modeling approach for the hydrogenation of titanium alloy can be highly beneficial since it offers the possibility of identifying promising processing parameters without the necessity for extensive experimental test cycles.