1. ASM-GT Data Challenge
Soumya Mohan
Aleksandr Blekh

2. ASM-GT Data Challenge
The problem includes CS-centric and MSE-centric challenges
The actual CS-centric problem would include:
Data Import from ASM and other databases
Data visualization and analytics
Talk to Holly Rush Whether to pick optical/TEM/Precipitate related data and link with Process conditions and to the stress strain curves/Young’s Modulus and UTS
Space.gatech

3. Data Challenge Problem: Reverse Engineering of Al 6061 alloy
Al6061 is a precipitate strengthened alloy: used in aircraft and automobile applications
Al6061 is Heat Treated (HT) to age the sample and get desired mechanical properties
Your team of MSE and Data Science engineers has been approached by your boss:
Your company is trying to redesign aircraft components that were originally built in the 1950s and 1960s. They lost the original specs, the original manufacturer don’t exist anymore.
They need to reverse engineer an Al6061 part, but they do not know the heat treatment required. Your Job: Find the Heat Treatment

4. Your company has salvaged some data (Data available for free)
Process Parameters
Microstructure Attributes
As Received Al6061(No Heat treatment)
Heat treated Al6061: 400 °F at 2 hours
Heat treated Al6061: 650 °F at 2 hours
Heat treated Al6061: 750 °F at 2 hours
Optical
SEM
EBSD
TEM
What is Heat treatment: X °F at 2 hr ?
Based on this data, predict the HT required to reverse engineer the part.

5. Optical and SEM Data for all HT
775 F, SEM Image
775 F, 50X Optical Image
Three types of compositionally different ppts (particles) with different morphology

6. EBSD Characterization: Pole figures, inverse pole figure, quantitative grain shape and size information

7. TEM characterization: Precipitate Analysis
Mg2Si
Quantitative data for all the volume fraction of phases and grain sizes and grain aspect ratios
Fe2Si2Al9
(Fe,Mn,Cr)3SiAl12

8. Facilities at your disposal (We have a small sample of the unknown HT
Microstructure characterization facility
Optical: Light illumination of the microstructure
What we see is the matrix and particles  $50
SEM: Higher resolution microstructure  $100
EBSD: Crystallography of the microstructure, orientation
distribution  $150
TEM: Information: about volume fraction of each specific precipitate(particle)  $300
For example
Each test provides different hierarchical information!!

9. Data Challenge Problem: Approach
Select a given type of data and a particular microstructure attribute (possibly using dimensionality reduction)
Correlate the Process Parameter Microstructure property
Buy Optical/SEM/TEM/EBSD test on unknown sample for a 'fee'.
Use that to predict the unknown process parameter (Heat Treatment)
The teams would be provided with technical help from Consultants. (Data Science and MSE). These consultations would also have a ‘fee’ of $20 for general advice and $50 for specific advice
The winning team would be the one who can develop a comprehensive approach to predict the HT. The team who does this in the most accurate, comprehensive and economical fashion wins. 

10. Access to free data:
EBSD and TEM data
Go to Alloys: Microstructure Data SMDDP test data
Optical and SEM information: Google drive link provided in
Data for Unknown HT: Contact Soumya Mohan

11. GOOD LUCK!
For any queries contact:
Aleksandr Blekh
Soumya Mohan