how we fit technical approach - johns hopkins university...concentration profile in a mg-9al at.%...

Enterprise for Multi - scale Research of Materials Deformation Driven Dynamic Precipitation in Mg-Al Alloy Suhas Eswarappa Prameela 1 , Peng Yi 1 , Vance Liu 1 , Laszlo Kecskes 2 , Tomoko Sano 3 , Michael Falk 1 , Timothy Weihs 1 1: Johns Hopkins University, Baltimore, MD. 2: MatSys, 3: ARL How We Fit Technical Approach Key Accomplishments Key Goals Major Results Impact Materials-by-Design Process Transitions to ARL, within CMRG and to other CMRGs Mechanism-based Approach UNCLASSIFIED UNCLASSIFIED Figure 1: Transmission electron micrographs showing (a) dense distribution of Mg 17 Al 12 precipitates in Mg-9Al (wt%) alloy processed by ECAE and (b) coarse distribution of precipitates seen in peak aged Mg-9Al (wt%) alloy (150 o C, 163 hrs) Thermomechanical Processing Route Modeling / Theoretical Framework 1. Identify and understand the processing parameters that control the dynamic nucleation of precipitates 2. Developed nucleation rate theory for dislocation-induced precipitation (a) to provide guidance for experimental choice of alloys and (b) to serve as input for higher level modeling • Monte Carlo simulation • Larché-Cahn solute segregation model • Classical nucleation theory with Dollins-Barnett dislocation-precipitation interaction 1. Dense distribution of fine sized dynamic precipitates produced through careful control of thermomechanical processing parameters. 2. Theoretical prediction of nucleation thermodynamics based on DFT and experimental inputs consistent with experimental observations 1. Effective processing recipes will be passed on to ARL for large scale processing of materials that will be distributed to the consortium. 2. Accurate DFT calculations of interfacial free energy could provide key inputs for accurate nucleation prediction. 3. Nucleation results will inform phase field modeling of thermomechanical processing. 1. Improved understanding of processing binary alloys to produce fine precipitates through control of extrusion rate and temperature. 2. Nucleation model connecting the thermo-mechanical processing conditions to precipitate nucleation rate equation for morphology prediction. Figure 2: Larché-Cahn theory prediction of the Al solute concentration profile in a Mg-9Al at.% alloy along the vertical line above a positive basal <a> edge dislocation, and the effect on Mg 17 Al 12 precipitate nucleation barrier. (inset) MC simulation of the solute segregation near a basal <a> edge dislocation dipole in Mg-9Al at.% alloy. a) b) Figure 3: Nanoindentation test showing higher value of hardness for the precipitates processed by ECAE compared to peak aged condition in Mg-9Al (wt%) alloy

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Enterprise for Multi-scale Research of Materials

Deformation Driven Dynamic Precipitation in Mg-Al Alloy

Suhas Eswarappa Prameela1, Peng Yi1, Vance Liu1, Laszlo Kecskes2, Tomoko Sano3, Michael Falk1, Timothy Weihs1

1: Johns Hopkins University, Baltimore, MD. 2: MatSys, 3: ARL

How We Fit Technical Approach

Key Accomplishments

Key Goals Major Results

Impact

Materials-by-Design Process

Transitions to ARL, within

CMRG and to other CMRGs

Mechanism-based Approach

UNCLASSIFIED

Figure 1: Transmission electronmicrographs showing (a) densedistribution of Mg17Al12 precipitates inMg-9Al (wt%) alloy processed by ECAEand (b) coarse distribution of precipitatesseen in peak aged Mg-9Al (wt%) alloy(150oC, 163 hrs)

Thermomechanical

Processing Route

Modeling / Theoretical

Framework

1. Identify and understand the processing parameters that control thedynamic nucleation of precipitates

2. Developed nucleation rate theory for dislocation-inducedprecipitation (a) to provide guidance for experimental choice ofalloys and (b) to serve as input for higher level modeling

• Monte Carlo simulation• Larché-Cahn solute segregation model• Classical nucleation theory with Dollins-Barnett

dislocation-precipitation interaction

1. Dense distribution of fine sized dynamic precipitates producedthrough careful control of thermomechanical processingparameters.

2. Theoretical prediction of nucleation thermodynamics based on DFTand experimental inputs consistent with experimental observations

1. Effective processing recipes will be passed on to ARL for large scaleprocessing of materials that will be distributed to the consortium.

2. Accurate DFT calculations of interfacial free energy could providekey inputs for accurate nucleation prediction.

3. Nucleation results will inform phase field modeling of thermo-mechanical processing.

1. Improved understanding of processing binary alloys to produce fineprecipitates through control of extrusion rate and temperature.

2. Nucleation model connecting the thermo-mechanical processingconditions to precipitate nucleation rate equation for morphologyprediction.

Figure 2: Larché-Cahn theoryprediction of the Al soluteconcentration profile in a Mg-9Al at.%alloy along the vertical line above apositive basal <a> edge dislocation,and the effect on Mg17Al12 precipitatenucleation barrier. (inset) MCsimulation of the solute segregationnear a basal <a> edge dislocationdipole in Mg-9Al at.% alloy.

Figure 3: Nanoindentation test showinghigher value of hardness for the precipitatesprocessed by ECAE compared to peak agedcondition in Mg-9Al (wt%) alloy

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shoulder dislocation

W, - California Institute of Technologyauthors.library.caltech.edu/55684/2/Basal Dislocation Mobility in...The 7/8 in. diameter crystals were acid sawed into• sections us ing 8N

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