Materials Design Approaches and Experiences V: Ferrous Alloys II
Sponsored by: TMS Structural Materials Division, TMS: High Temperature Alloys Committee, TMS: Integrated Computational Materials Engineering Committee
Program Organizers: Akane Suzuki, GE Aerospace Research; Ji-Cheng Zhao, University of Maryland; Michael Fahrmann, Haynes International; Qiang Feng, University of Science and Technology Beijing; Michael Titus, Purdue University

Thursday 2:00 PM
February 27, 2020
Room: 33A
Location: San Diego Convention Ctr

Session Chair: Bryan Webler, Carnegie Mellon University; Michael Fahrmann, Haynes International


2:00 PM  Invited
Microstructure and Cracking Susceptibility of Continuously Cast Slabs of 3rd Generation Advanced High Strength Steels: Rafael Coura Giacomin1; Bryan Webler1; 1Carnegie Mellon University
    The 3rd generation of Advanced High-Strength Steels (AHSS) is being developed to obtain good combinations of strength and ductility for automotive applications. These grades contain higher levels of manganese, silicon, and/or aluminum than many other steels. Although they exhibit excellent strength and ductility after appropriate processing, they are challenging to produce using existing steel production methods. One of the major challenges has been cracking of continuously cast slabs. This work examined microstructure-property relations in as-cast steels to identify the reasons for this cracking. Failed production slabs were examined and several laboratory heats were produced to vary chemical composition. Laboratory heat compositions were 0.2wt.%C, 2.5-3.0wt.%Mn and 0.5-3.0wt.%Si. Charpy V-Notch tests were used as a measure of toughness for all samples. Grades with higher silicon exhibited the lowest toughness due to a network of thin allotriomorphic ferrite at prior austenite grain boundaries.

2:30 PM  
Development of Nuclear Grade Wrought FeCrAl Alloys for Accident Tolerant Fuel Cladding: Yukinori Yamamoto1; Kevin Field1; Bruce Pint1; Kurt Terrani1; Raul Rebak2; Russ Fawcett3; 1Oak Ridge National Laboratory; 2GE Global Research; 3Global Nuclear Fuel
    A nuclear grade wrought FeCrAl-base alloy has been developed which targets a new accident-tolerant fuel (ATF) cladding substituting for current Zr-based alloys in light water reactors (LWR). Protective alumina-scale formation in steam-containing environments at elevated temperatures is attractive for the ATF design, especially in the event of a loss-of-coolant-accident. Major efforts and challenges included the optimization of major and minor alloying elements suitable for various service circumstances such as strength, toughness, oxidation performance, and irradiation resistance, together with the processability improvement for a seamless, thin-wall tube production. An ATF assembled with the developed wrought FeCrAl cladding (IronClad, GNF) has been installed in an operating LWR power plant and the “field” test is currently in progress. A detailed story of the alloy design, development, and deployment will be presented. Research funded by U.S. Department of Energy’s Office of Nuclear Energy, Advanced Fuel Campaign of the Fuel Cycle R&D program.

2:50 PM  
The FaMUS Methodology for Quantify Materials Understanding and Its Application to the NSUF Research Portfolio: Simon Pimblott1; Rory Kennedy1; 1Idaho National Laboratory
    The Nuclear Science User Facilities (NSUF) is one of a diverse group of DOE user facilities. It is focused on advancing the understanding of radiation effects in nuclear fuels and materials in support of nuclear energy applications. The NSUF has been operating since 2007 and has developed a significant portfolio of supported research. Therefore, it is appropriate to consider its achievements and to determine its successes and shortfalls. As part of this analysis of the NSUF research program, the NSUF has developed a novel and elegant formalism for assessing the current level of understanding of nuclear fuels and of materials for use in nuclear environments: the NSUF Fuels and Materials Understanding Scale (FaMUS). The FaMUS methodology is being applied to the NSUF portfolio to quantify the progress made. This presentation summarizes the status of the assessment exercise and any notable outcomes found as well as preliminary conclusions.

3:10 PM Break

3:30 PM  Cancelled
Discovery of Maraging Steel: Machine Learning vs. Physical Metallurgical Models: Chunguang Shen1; Chenchong Wang1; Xiaolu Wei1; Wei Xu1; 1Northeastern University
    With the progression of the Materials Genome Initiative, material design by physical metallurgy (PM) and machine learning (ML) models has received much attention. However, neither PM nor ML models can perfectly deal with a dataset with small samples. Therefore, the combination of two models is a promising solution to overcome this limitation. In this study, datasets of maraging steels with small samples were established based on literature. Then, hardness of maraging steels was predicted and designed by different PM and ML models. For property prediction, the differences of prediction accuracy, microstructure sensitivity and data dependence between two models were systematically compared, and it is believed that combination of PM and ML models is helpful to alleviate small sample and make ML model have physical significance. For alloy design, a PM-guided ML model constructed by combining physical principles and ML is a promising method for producing novel, rational alloy designs.

4:00 PM  
Domain-guided ML Tool for Designing New Fe-9Cr Steels: Vyacheslav Romanov1; 1National Energy Technology Laboratory
    The main consideration for using 9 to 12% Cr martensitic-ferritic steels is their relatively high microstructural stability at an operating temperature, with a design lifetime expectation of over 30 years. Due to high dimensionality of the problem, it requires very large datasets for the data-driven model development. The key experimental data collection, particularly on microstructural phases, is very challenging, which makes it particularly difficult to compile a high-quality database for unbiased machine learning (ML). Incorporation of the domain knowledge into ML graph structure, initialization and optimization processes, and informed cross-validation presents a viable mechanism for developing accurate models and reliable alloy design tools, with limited datasets. This presentation will describe the approach to digitize empirical domain knowledge, build the graph based on causality relationships, and use machine learning methodology to identify promising alloy compositions, rank factors affecting the alloys performance, and optimize the processing parameters for specific applications.

4:20 PM  
Effect of Vibration on Residual Stress of a Stiffened Steel Plate During Welding: A Numerical Study: Rururaja Pradhan1; Mohammed Sunny2; Arunjyoti Sarkar1; 1Department of Ocean Engineering and Naval Architecture, Indian Institute of Technology, Kharagpur; 2Department of Aerospace Engineering, Indian Institute of Technology, Kharagpur
    Vibratory Stress Relief (VSR) is a new alternative technique to the conventional Thermal Stress Relief (TSR) technique to reduce the residual stress on welded components; this method has great potential to emerge as a low-cost high-efficiency technique. In this paper, the effect of vibration on the residual stress of a longitudinally stiffened plate has been numerically studied. The simulation has been carried out by adopting a sequentially coupled thermal and mechanical analysis approach by using the well-known FEM (Finite Element Method) tool ABAQUS. Post weld vibrations with different sub-resonant frequencies and amplitudes have been applied to the stiffener in order to study their effect on the residual stress. The results demonstrate the sensitivity of the peak residual stress developed in a welded stiffened plate with respect to the vibration parameters.