材料科学
合金
人工神经网络
极限抗拉强度
熔点
基质(化学分析)
冶金
均方误差
特征(语言学)
线性回归
相关系数
复合材料
人工智能
机器学习
计算机科学
统计
数学
哲学
语言学
作者
Jaka Fajar Fatriansyah,Iping Suhariadi,Haya Ayu Fauziyyah,Ibnu Rais Syukran,Fernanda Hartoyo,Donanta Dhaneswara,Zainovia Lockman,Andrian Fauzi,Muhammad Syaikh Rohman
标识
DOI:10.1016/j.jmrt.2023.04.065
摘要
The Optimization of super alloy through alloying element composition control is very challenging since it contains more than ten type of elements. Generally, the optimization process of the mechanical properties of super alloy by composition alteration was performed via trial-and-error which is time consuming and expensive. In this study, the artificial neural network was successfully employed to reveal the correlation between alloying element and the hardness, tensile strength, and melting point of Ni based and Fe–Ni based super alloy. The model showed a good accuracy between predicted and actual values, especially for hardness and melting temperature, with R2 and RMSE values were found to be above 95% and below 3%, respectively. The Pearson Correlation Coefficient and Feature Importance revealed the linear and non-linear correlation of elements in the matrix. The validation of mathematical expression derived from symbolic regression displayed a high reliability associated with RMSE values of approaching 0%. The feature importance derived from atomic and thermal features characterization on Fe–Ni based super alloy as selected sample revealed the dominant effect of Ni matrix on the mechanical properties of the alloy. These results show the potential of our model to assist in the designing of super alloy for industry.
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