Designing a high Si reduced activation ferritic/martensitic steel for nuclear power generation by using Calphad method

A high Si reduced activation ferritic/martensitic(RAFM) steel for nuclear structure application is successfully designed by using Calphad method. The main designed chemical composition is C 0.18–0.22%, Cr10.0–10.5%, W 1.0–1.5%, Si 1.0–1.3%, V+Ta 0.30–0.45%, and Fe in balance. High Si design brings excellent corrosion resistance, while low activation is advantageous in the nuclear waste processing. The experimental results indicate that the newly designed high Si RAFM steel had full martensitic structure and uniformly distributed fine second phase particles, and exhibited excellent mechanical properties and corrosion resistance. Compared to the P91 steel, this new RAFM steel designed by Calphad method is expected to be a promising candidate used in nuclear power generation, which also provides a new and effective approach to the development of RAFM steel for nuclear application.

Discovery of marageing steels: machine learning vs. physical metallurgical modelling

Physical metallurgical(PM)and data-driven approaches can be independently applied to alloy design.Steel technology is a field of physical metallurgy around which some of the most comprehensive understanding has been developed,with vast models on the relationship between composition,processing,microstructure and properties.They have been applied to the design of new steel alloys in the pursuit of grades of improved properties.With the advent of rapid computing and low-cost data storage,a wealth of data has become available to a suite of modelling techniques referred to as machine learning(ML).ML is being emergingly applied in materials discovery while it requires data mining with its adoption being limited by insufficient high-quality datasets,often leading to unrealistic materials design predictions outside the boundaries of the intended properties.It is therefore required to appraise the strength and weaknesses of PM and ML approach,to assess the real design power of each towards designing novel steel grades.This work incorporates models and datasets from well-established literature on marageing steels.Combining genetic algorithm(GA)with PM models to optimise the parameters adopted for each dataset to maximise the prediction accuracy of PM models,and the results were compared with ML models.The results indicate that PM approaches provide a clearer picture of the overall composition-microstructureproperties relationship but are highly sensitive to the alloy system and hence lack on exploration ability of new domains.ML conversely provides little explicit physical insight whilst yielding a stronger prediction accuracy for large-scale data.Hybrid PM/ML approaches provide solutions maximising accuracy,while leading to a clearer physical picture and the desired properties.

A generic high-throughput microstructure classification and quantification method for regular SEM images of complex steel microstructures combining EBSD labeling and deep learning

We present an electron backscattered diffraction(EBSD)-trained deep learning(DL)method integrating traditional material characterization informatics and artificial intelligence for a more accurate classification and quantification of complex microstructures using only regular scanning electron microscope(SEM)images.In this method,EBSD analysis is applied to produce accurate ground truth data for guiding the DL model training.An U-Net architecture is used to establish the correlation between SEM input images and EBSD ground truth data using only small experimental datasets.The proposed method is successfully applied to two engineering steels with complex microstructures,i.e.,a dual-phase(DP)steel and a quenching and partitioning(Q&P)steel,to segment different phases and quantify phase content and grain size.Alternatively,once properly trained the method can also produce quasi-EBSD maps by inputting regular SEM images.The good generality of the trained models is demonstrated by using DP and Q&P steels not associated with the model training.Finally,the method is applied to SEM images with various states,i.e.,different imaging modes,image qualities and magnifications,demonstrating its good robustness and strong application ability.Furthermore,the visualization of feature maps during the segmenting process is utilised to explain the mechanism of this method’s good performance.