| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
An ICME Method for Predicting Phase Dissolution During Solution Treatment in Advanced Super Vacuum Die Cast Magnesium Alloys
|
Yao, Zhenjie
|
|
|
9 |
3 |
p. 301-313
|
artikel
|
| 2 |
Correction to: Universal Material Constants for MultiStage Fatigue (MSF) Modeling of the Process–Structure–Property (PSP) Relations of A000, 2000, 5000, and 7000 Series Aluminum Alloys
|
Horstemeyer, M. F.
|
|
|
9 |
3 |
p. 314
|
artikel
|
| 3 |
Extracting Knowledge from DFT: Experimental Band Gap Predictions Through Ensemble Learning
|
Kauwe, Steven K.
|
|
|
9 |
3 |
p. 213-220
|
artikel
|
| 4 |
High-Throughput Exploration of the Process Space in 18% Ni (350) Maraging Steels via Spherical Indentation Stress–Strain Protocols and Gaussian Process Models
|
Parvinian, Sepideh
|
|
|
9 |
3 |
p. 199-212
|
artikel
|
| 5 |
ICME Framework for Simulation of Microstructure and Property Evolution During Gas Metal Arc Welding in DP980 Steel
|
Deepu, M. J.
|
|
|
9 |
3 |
p. 228-239
|
artikel
|
| 6 |
Is Domain Knowledge Necessary for Machine Learning Materials Properties?
|
Murdock, Ryan J.
|
|
|
9 |
3 |
p. 221-227
|
artikel
|
| 7 |
Mining the Correlations Between Optical Micrographs and Mechanical Properties of Cold-Rolled HSLA Steels Using Machine Learning Approaches
|
Yucel, Berkay
|
|
|
9 |
3 |
p. 240-256
|
artikel
|
| 8 |
Model Selection and Evaluation for Machine Learning: Deep Learning in Materials Processing
|
Kopper, Adam
|
|
|
9 |
3 |
p. 287-300
|
artikel
|
| 9 |
Temperature-Dependent Material Property Databases for Marine Steels—Part 1: DH36
|
Bechetti, Daniel H.
|
|
|
9 |
3 |
p. 257-286
|
artikel
|
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