Machine learning and Python assisted design and verification of Fe–based amorphous/nanocrystalline alloy. (July 2022)

• Record Type: Journal Article
• Authors: Tang, Yichuan; Wan, Yuan; Wang, Zhongqi; Zhang, Cong; Han, Jiani; Hu, Chaohao; Tang, Chengying
• Is Part Of: Materials & design. Volume 219(2022)
• Publication Date: 2022-07
• Subjects: Machine learning -- Artificial neural networks -- Amorphous alloy -- Nanocrystalline alloys -- Magnetic properties; Materials -- Periodicals; Engineering design -- Periodicals; Matériaux -- Périodiques; Conception technique -- Périodiques; Electronic journals; 620.11
• DOI: 10.1016/j.matdes.2022.110726
• Languages: English
• ISSNs: 0264-1275

Abstract

Graphical abstract: Highlights: A machine learning and Python assisted method was proposed to predict six kinds magnetic properties of Fe-based amorphous and nanocrystalline alloys. Artificial Neural Network algorithm showed the best predictive ability with determination coefficient of>0.90. An alloy Fe83 B9 P3 C4 Nb1 with desired magnetic properties was designed and verified experimentally. The predicted magnetic properties by Artificial Neural Network are in very good agreement with experimental measured ones. Abstract: We report a machine learning (ML) and Python assisted strategy to accelerate the design and verification of Fe–based amorphous and nanocrystalline alloy with desired properties. Linear Regression (LR), Support Vector Regression (SVR), Decision Tree Regression (DTR), Artificial Neural Network (ANN) and Random Forest Regression (RFR) are employed to build prediction models of soft magnetic properties, such as saturation magnetic flux density ( B s ), coercivity force ( H c ), magnetization ( M s ), Curie temperature ( T c ), maximum permeability ( µmax ) and effective permeability ( µe ). It is found that ANN has the excellent fitting ability with largest coefficient of determination ( R 2 ) to predict the soft magnetic properties of new designed alloys. Then, Python screening is used to find the alloy compositions with best soft magnetic properties of Fe–B–P–C–Nb system. Finally, Fe83 B9 P3 C4 Nb1 alloy with good soft magnetic properties has been designed and prepared to verify. It is indicated that the soft magnetic properties of Fe83 B9 P3 C4 Nb1 amorphous and nanocrystalline alloy predicted by ML are in agreement with the experimental measured results. These findings indicate that ML and Python assisted approach can accelerate the design of Fe–based alloys with desired properties accurately.