6th World Congress on Integrated Computational Materials Engineering (ICME 2022): On-Demand Posters
Program Organizers: William Joost; Kester Clarke, Los Alamos National Laboratory; Danielle Cote, Worcester Polytechnic Institute; Javier Llorca, IMDEA Materials Institute & Technical University of Madrid; Heather Murdoch, U.S. Army Research Laboratory; Satyam Sahay, John Deere; Michael Sangid, Purdue University
Monday 7:30 AM
May 2, 2022
Room: On-Demand Session Room
Location: Hyatt Regency Lake Tahoe
Modeling of Grain Structure Development as a Function of Melt Pool Shape and Grid Resolution During Alloy AM: Matthew Rolchigo1; John Coleman1; Gerry Knapp1; Sam Reeve1; Robert Carson2; Jim Belak2; 1Oak Ridge National Laboratory; 2Lawrence Livermore National Laboratory
Grain structure development during AM processing is strongly dependent on material and scan pattern, as different melt pool conditions yield variation in epitaxial growth of substrate grains and nucleation of new grains. When modeling grain structure development from a substrate as a function of layer and passing the modeled grain structures to property calculations, it is important that the microstructure is representative of the majority of the build and as independent of the estimated initial condition as possible. This study uses the cellular automata (CA)-based ExaCA model to examine effects of substrate (an initial condition), nucleation density (a material input), and scan pattern (a process input) on “quasi steady-state” (i.e., not changing significantly as a function of layer) grain structure development. Grain sizes and textures will be quantified and compared, and associated variation in constitutive properties calculated using ExaConstit will also be discussed.
Analysis and Numerical Simulation of Plastic Anisotropy Behavior of DIN 1623 St14 Steel Sheet Under Associated Flow Rule Approach: Kaouter Babouri1; Nedjoua Matougui1; Oualid Chahaoui2; 1National School of Mining and Metallurgy-Annaba; 2Engineering Sciences and Advanced Materials Laboratory (ISMA), Laghrour-Abbes University of Khenchela
the present work aims to predict mechanical behavior of an industrial rolled sheet for a DIN 1623 St14 (DC04) steel via numerical model, based on the plastic anisotropy. First, an experimental device of simple tensile tests, in several directions relative to the rolling direction, and the studied material are described. Second, the modeled and simulated results behaviour under Hill1948 quadratic formalism yield criteria and FEM Method are compared. Finally, an heterogeneity of the properties is well recorded through the analysis of the stress-strain curves obtained via modeling, as well as for the yield stresses and Lankford r-values in uniaxial tensile test. However, the influence of “r-values” upon the Yield locus shape defined by the above comparison is well highlighted.
Analysis and Numerical Simulation of Microstructural Anisotropy Behavior of DIN 1623 St14 Steel Before and After Recrystallisation: Kaouter Babouri1; Nedjoua Matougui1; Oualid Chahaoui1; 1National School of Mining and Metallurgy-Annaba
this work aims to describe a qualitative and quantitative phenomenological study of the morphological texture of an industrial rolled sheet for a DIN 1623 St14 (DC04) steel before and after static recrystallization. The objective is to better control the evolution of the anisotropy from a microstructural point of view, in order to optimize the subsequent mechanical properties. All the stereological parameters are calculated before and after recrystallization, through statistical analysis under classical image processing and using selected maps subsets. As a result, significant variation from the calculated mechanical properties is recorded locally based on the Taylor model. Consequently, the optimisation of the locale anisotropy behavior and it’s assessment from microscopic to macroscopic scale are well described.
Image Processing Based Failure Site Prediction of PC Wires During Wiredrawing Using Supervised Machine Learning Approach: Mohamed Heddar1; Mehdi Brahim2; Nedjoua Matougui1; 1ENSMM - Annaba; 2CRTI - Cheraga
The aim of this work is to develop an image texture and defect morphology based method for predicting failure site whether it is during decoiling or wiredrawing probabilistically. Traditional image segmentation of micrographs for acquiring stereological particles data is used coupled with image processing for extracting texture based information. The data are modeled through supervised classification using Random Forest Classifier (RFC) with hyper parameter optimization for model calibration. Based on comparison between experimental results and model testing points, it was found that this model can achieve decent predictive results with classification accuracy rate around ~ 71.3%. The obtained results can be improved using of multisource datasets with the addition of local mechanical properties.
Study of Dendrite Growth in Nickel-Based Superalloy Directional Solidification via a GPU-Accelerated Multiphase-Field-Lattice Boltzmann Method: Huxiang Xia1; 1Tsinghua University
Dendrite morphology evolution is closely related to the occurrence of various defects and directly affects the quality of the alloy in the directional solidification process. In this study, a multiphase-field model was applied to simulate the dendrite growth in the nickel-based superalloy directional solidification process. The influence of natural convection, which is the main factor leading freckles formation in alloy solidification, was investigated by coupling with the lattice Boltzmann method. The thermodynamic data used in this simulation was obtained from PanNickel thermodynamic database. The dendrite morphology was analyzed between with and without nature convection condition, and the result shows that the dendrite morphology under natural convection condition is more consistent with the actual alloy. The effect of grain orientation on the dendrite morphology was studied. It was found that the average flow velocity in the mushy zone doesn’t change much when the grain inclination angle θ≤10°.
Machine Learning Assisted Yield Strength and Hardness Prediction of Multi-Principal Element Alloys: Mohammad Fuad Nur Taufique1; Ankit Roy2; Ganesh Balasubramanian2; Gaoyuan Ouyang3; Duane Johnson3; Ram Devanathan1; 1Pacific Northwest National Laboratory; 2Lehigh University; 3Ames Laboratory
Multi-Principal Element Alloys (MPEAs) have better properties, such as yield strength, hardness, and corrosion resistance compared to conventional alloys. Compositional optimization is a challenging task to obtain desired properties of MPEAs and machine learning is a potential tool to rapidly accelerate the search and design of new materials. We have implemented machine learning tools to predict the yield strength and Vickers hardness of MPEAs at room temperature by employing gradient boost regression (GBR) algorithm. Our results suggest that valence electron concentration (VEC) is the key feature dominating the yield strength and hardness of MPEAs. Our predicted yield strength and hardness values on experimental validation set show <10 % error with respect to the actual values. We believe that our machine learning model will act as a swift tool for screening the half a trillion large search space of MPEAs and down select promising compositions for useful applications.
Molecular Dynamics Simulation of Adhesive Response to Aging for Epoxy Polymer: Mohammad Fuad Nur Taufique1; Martin Losada2; Nir Goldman3; Sebastien Hamel3; Ram Devanathan1; 1Pacific Northwest National Laboratory; 2PPG Industries, Inc. ; 3Lawrence Livermore National Laboratory
New structural adhesives are needed to mitigate the corrosion and thermal expansion issues associated with joining dissimilar lightweight materials, but adhesive developers lack a fundamental understanding of the chemistry that occurs in the adhesive as the joint ages. In this study, we developed structural adhesive molecular models and applied molecular dynamics (MD) simulations to gain molecular insights into the influence of water molecules on the structural and mechanical properties of epoxy-based adhesives (DGEBA + Jeffamine (JD230)). Calculated RDFs in conjunction with experimental infrared spectroscopy data suggest that water molecules are mainly coordinated with hydroxyl groups, ether groups, primary amines and secondary amines of the epoxy network, which might have impact on mechanical properties. Simulated stress-strain data indicates that increasing water content deteriorates the mechanical properties. This integration of atomic level information with mechanical properties of the hydrated epoxy system can evolve to a new direction in lightweight joint technologies.
Core Structure, Energy, and Mobility of Pyramidal Dislocations in Magnesium: Yang Yang1; Bin Li1; Kefan Chen1; 1University of Nevada, Reno
Magnesium (Mg) and its alloys are attractive lightweight structural materials for improving energy efficiency in automotive and aerospace applications. However, the room temperature ductility of Mg is limited because of the lack of sufficient pyramidal dislocations that are able to accommodate the strain along the c-axis but have a critical resolved shear stress nearly two orders of magnitude higher than basal dislocations. Previous simulations showed that pyramidal dislocations tend to dissociate onto the basal plane, forming an immobile configuration. In this work, we show new simulation results and demonstrate that both Py-I on {10-11} and Py-II {11-22} dislocations are stable and mobile. The Nye tensor which describes the degree of lattice distortion of dislocation core for these dislocations is calculated. Atomic stress and dislocation energy are also calculated.
Effect of Grain Boundary Migration on Radiation Induced Segregation in Polycrystalline Metal: Aashique Rezwan1; Yongfeng Zhang1; 1University of Wisconsin Madison
Radiation-induced segregation (RIS) can detrimentally degrade the mechanical properties and corrosion resistance of materials used in nuclear reactors. While in coarse-grained alloys, the grain boundaries (GB) and the grain size are regarded as static under irradiation, in nanocrystalline materials RIS is accompanied by grain growth via GB migration. In this work, a diffusion-based model is developed for RIS in ternary Fe-Cr-Ni model alloy considering both the vacancy and interstitial mediated diffusion. When grain growth is activated simultaneously, the overall RIS is found to increase with increasing average grain size. An inhomogeneous concentration is observed in grains of different sizes. At a moving GBs, the segregation profile becomes asymmetrical, in contrast to the symmetrical profile at static GBs. While Cr is still depleted behind a moving GB, it becomes enriched in the front. These findings imply different effects of RIS in nanocrystalline alloys than those in their coarse-grained counterparts.
Modeling of Coupled Diffusion Transport in Cylindrical Transformations: Rahul Basu1; 1VTU
A set of coupled transport equations in the cylindrical system is formulated. Effects of thermal, mass and surface conditions are included. A matrix method is applied after transformations to solve eigen-values and obtain eigen solutions in a simple direct way. The state matrix is used to decouple and describe areas of equilibrium, and attenuation. Usual methods in the literature use algebraic decoupling which obscures the contributions of the eigen-values and stability properties of the state matrix. The solutions provide information on the boundary layers and attenuation of applied energy on the surface. Some technological applications are highlighted.