scholarly journals Machine Learning for Thermal Transport Analysis of Aluminum Alloys with Precipitate Morphology

2019 ◽  
Vol 2 (4) ◽  
pp. 1800196 ◽  
Author(s):  
Jiaqi Wang ◽  
Ali Yousefzadi Nobakht ◽  
James Dean Blanks ◽  
Dongwon Shin ◽  
Sangkeun Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Aluminum Alloys ◽  
Thermal Transport ◽  
Precipitate Morphology ◽  
Transport Analysis

scholarly journals Accelerated discovery of high-strength aluminum alloys by machine learning

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Jiaheng Li ◽  
Yingbo Zhang ◽  
Xinyu Cao ◽  
Qi Zeng ◽  
Ye Zhuang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Aluminum Alloys ◽  
Mechanical Performance ◽  
High Strength ◽  
Cost Effective ◽  
Alloy System ◽  
Processing Route ◽  
Specific Strength ◽  
Cu Alloy ◽  
High Strength Aluminum Alloys

Abstract Aluminum alloys are attractive for a number of applications due to their high specific strength, and developing new compositions is a major goal in the structural materials community. Here, we investigate the Al-Zn-Mg-Cu alloy system (7xxx series) by machine learning-based composition and process optimization. The discovered optimized alloy is compositionally lean with a high ultimate tensile strength of 952 MPa and 6.3% elongation following a cost-effective processing route. We find that the Al8Cu4Y phase in wrought 7xxx-T6 alloys exists in the form of a nanoscale network structure along sub-grain boundaries besides the common irregular-shaped particles. Our study demonstrates the feasibility of using machine learning to search for 7xxx alloys with good mechanical performance.

Effects of precipitate morphology on the notch sensitivity of ductile fracture in heat-treatable aluminum alloys

2010 ◽  
Vol 527 (27-28) ◽  
pp. 7369-7381 ◽  
Author(s):  
S.P. Yuan ◽  
R.H. Wang ◽  
G. Liu ◽  
R. Li ◽  
J.M. Park ◽  
...  
Keyword(s):  
Aluminum Alloys ◽  
Ductile Fracture ◽  
Notch Sensitivity ◽  
Precipitate Morphology

Unraveling the role of bonding chemistry in connecting electronic and thermal transport by machine learning

2020 ◽  
Vol 8 (17) ◽  
pp. 8716-8721 ◽  
Author(s):  
Rinkle Juneja ◽  
Abhishek K. Singh
Keyword(s):  
Machine Learning ◽  
Thermal Transport ◽  
Ion Interactions ◽  
Bonding Chemistry

Electronic and thermal transport in materials originate from various forms of electron and ion interactions.

scholarly journals Topological Optimization of Artificial Neural Networks to Estimate Mechanical Properties in Metal Forming Using Machine Learning

Metals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1289
Author(s):  
David Merayo ◽  
Alvaro Rodríguez-Prieto ◽  
Ana María Camacho
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Aluminum Alloys ◽  
Metal Forming ◽  
Strain Curve ◽  
Topological Optimization ◽  
Plastic Behavior ◽  
Input Dataset ◽  
Wrought Aluminum Alloys ◽  
Fully Connected

The ability of a metal to be subjected to forming processes depends mainly on its plastic behavior and, thus, the mechanical properties belonging to this region of the stress–strain curve. Forming techniques are among the most widespread metalworking procedures in manufacturing, and aluminum alloys are of great interest in fields as diverse as the aerospace sector or the food industry. A precise characterization of the mechanical properties is crucial to estimate the forming capability of equipment, but also for a robust numerical modeling of metal forming processes. Characterizing a material is a very relevant task in which large amounts of resources are invested, and this paper studies how to optimize a multilayer neural network to be able to make, through machine learning, precise and accurate predictions about the mechanical properties of wrought aluminum alloys. This study focuses on the determination of the ultimate tensile strength, closely related to the strain hardening of a material; more precisely, a methodology is developed that, by randomly partitioning the input dataset, performs training and prediction cycles that allow estimating the average performance of each fully-connected topology. In this way, trends are found in the behavior of the networks, and it is established that, for networks with at least 150 perceptrons in their hidden layers, the average predictive error stabilizes below 4%. Beyond this point, no really significant improvements are found, although there is an increase in computational requirements.

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