Algorithm Development in Materials Science and Engineering: Poster Session
Sponsored by: TMS Materials Processing and Manufacturing Division, TMS: Computational Materials Science and Engineering Committee, TMS: Integrated Computational Materials Engineering Committee, TMS: Phase Transformations Committee, TMS: Solidification Committee
Program Organizers: Mohsen Asle Zaeem, Colorado School of Mines; Mikhail Mendelev, NASA ARC; Garritt Tucker, Colorado School of Mines; Ebrahim Asadi, University of Memphis; Bryan Wong, University of California, Riverside; Sam Reeve, Oak Ridge National Laboratory; Enrique Martinez Saez, Clemson University; Adrian Sabau, Oak Ridge National Laboratory
Tuesday 5:30 PM
March 1, 2022
Room: Exhibit Hall C
Location: Anaheim Convention Center
Session Chair: Mohsen Asle Zaeem, Colorado School of Mines
M-12: Discrete Stochastic Model of Point Defect-dislocation Interaction
for Simulating Dislocation Climb: Cameron Mcelfresh1; Yinan Cui2; Sergei Dudarev3; Giacomo Po4; Sylvie Aubry5; Nicolas Bertin5; Jaime Marian1; 1University of California, Los Angeles; 2Tsinghua University; 3United Kingdom Atomic Energy Authority, Culham Science Centre; 4University of Miami; 5Lawrence Livermore National Laboratory
Dislocation climb is an important high-temperature process in metals plasticity, responsible for the phenomena such as creep, swelling, or hardening. The point-wise discrete nature of vacancies (and/or self-interstitials) confers a highly discrete nature to the climb dynamics, which isalso strongly affected by elastic forces. We develop a kinetic Monte Carlo model that captures vacancy generation and transport kinetics acting in conjunction with the evolving elastic fields provided by discrete dislocation dynamics (DDD) simulations. The two models are coupled via the applied stresses and stress gradients generated by dislocation structures at vacancy locations. This method is applied to study elementary climb processes in body-centered cubic iron and furnish climb mobility functions to be used in parametric dislocation dynamics and/or crystal plasticity simulations. We then demonstrate non-conservative plastic bypass of spherical precipitates by edge dislocations and demonstrate algorithmic extensions to use this method in parallelized DDD codes.
M-13: Finite Element Level-set Methods to Study Microstructural Evolution during Recrystallization and Grain Growth: Daniel Pino Munoz1; Marc Bernacki1; Nathalie Bozzolo1; 1Mines ParisTech, PSL University
We have been working in tight collaboration with industrial partners in order to be able to predict microstructural evolutions of metallic material due to recrystallization and grain growth phenomena during thermomechanical manufacturing processes. To this end, we developed a numerical framework based on the use of the level-set method in order to describe the motion of grain boundaries. In this framework, we try to directly account for the underlying phenomena responsible for the evolution of the microstructure in 2D and 3D at the mesoscopic scale.This approach allows to deal with an important number of microstructure characteristics (dislocation density, second phase particles, high anisotropy of boundary energy...). In this talk a summary of the latest numerical features implemented in our numerical framework will be presented. Since these phenomena are also present in other materials (such as rocks), some parallels will be drawn.
M-14: Smoke Detection in Ladle Hot Repair Process Based on Convolution Neural Network: Yanming Zhang1; Jialu Wu1; Mujun Long1; Wei Guo1; Huamei Duan1; Dengfu Chen1; 1Chongqing University
The intelligent control of fog gun dust removal is one of the significant means of energy saving and environmental protection during the process of ladle hot repair. However, the feature extraction method with poor adaptability is mostly applied to the online intelligent identification model of smoke. In order to improve the accuracy of the model, a novel smoke detection method based on the convolution neural network(CNN) is proposed. According to the VGG-net network structure, we construct a new network and optimize its parameters well. Experimental analysis show that the method achieves over 95% smoke detection accuracy on the dataset, which is obviously better than the feature extraction. As a result, the improved CNN can effectively reduce the water consumption of the fog gun intelligent control model. Thus, it provides a basis for the improvement of the intelligence and greenization in iron and steel industry.