An Improved High-Throughput Data Processing Based on Combinatorial Materials Chip Approach for Rapid Construction of Fe-Cr-Ni Composition-Phase Map

Perspectives on Corrosion Studies Using Scanning Electrochemical Cell Microscopy: Challenges and Opportunities

Scanning electrochemical cell microscopy (SECCM) allows for electrochemical imaging at the micro- or nanoscale by confining the electrochemical reaction cell in a small meniscus formed at the end of a micro- or nanopipette. This technique has gained popularity in electrochemical imaging due to its high-throughput nature. Although it shows considerable application potential in corrosion science, there are still formidable and exciting challenges to be faced, particularly relating to the high-throughput characterization and analysis of microelectrochemical big data. The objective of this perspective is to arouse attention and provide opinions on the challenges, recent progress, and future prospects of the SECCM technique to the electrochemical society, particularly from the viewpoint of corrosion scientists. Specifically, four main topics are systematically reviewed and discussed: (1) the development of SECCM; (2) the applications of SECCM for corrosion studies; (3) the challenges of SECCM in corrosion studies; and (4) the opportunities of SECCM for corrosion science.

Modeling and Composition Design of Low-Alloy Steel's Mechanical Properties Based on Neural Networks and Genetic Algorithms

Accurately improving the mechanical properties of low-alloy steel by changing the alloying elements and heat treatment processes is of interest. There is a mutual relationship between the mechanical properties and process components, and the mechanism for this relationship is complicated. The forward selection-deep neural network and genetic algorithm (FS-DNN&GA) composition design model constructed in this paper is a combination of a neural network and genetic algorithm, where the model trained by the neural network is transferred to the genetic algorithm. The FS-DNN&GA model is trained with the American Society of Metals (ASM) Alloy Center Database to design the composition and heat treatment process of alloy steel. First, with the forward selection (FS) method, influencing factors—C, Si, Mn, Cr, quenching temperature, and tempering temperature—are screened and recombined to be the input of different mechanical performance prediction models. Second, the forward selection-deep neural network (FS-DNN) mechanical prediction model is constructed to analyze the FS-DNN model through experimental data to best predict the mechanical performance. Finally, the FS-DNN trained model is brought into the genetic algorithm to construct the FS-DNN&GA model, and the FS-DNN&GA model outputs the corresponding chemical composition and process when the mechanical performance increases or decreases. The experimental results show that the FS-DNN model has high accuracy in predicting the mechanical properties of 50 furnaces of low-alloy steel. The tensile strength mean absolute error (MAE) is 11.7 MPa, and the yield strength MAE is 13.46 MPa. According to the chemical composition and heat treatment process designed by the FS-DNN&GA model, five furnaces of Alloy1–Alloy5 low-alloy steel were smelted, and tensile tests were performed on these five low-alloy steels. The results show that the mechanical properties of the designed alloy steel are completely within the design range, providing useful guidance for the future development of new alloy steel.