scholarly journals Identifying Structure–Property Relationships Through DREAM.3D Representative Volume Elements and DAMASK Crystal Plasticity Simulations: An Integrated Computational Materials Engineering Approach

JOM ◽  
2017 ◽  
Vol 69 (5) ◽  
pp. 848-855 ◽  
Author(s):  
Martin Diehl ◽  
Michael Groeber ◽  
Christian Haase ◽  
Dmitri A. Molodov ◽  
Franz Roters ◽  
...  
Keyword(s):  
Crystal Plasticity ◽  
Structure Property ◽  
Materials Engineering ◽  
Engineering Approach ◽  
Representative Volume ◽  
Structure Property Relationships ◽  
Integrated Computational Materials Engineering ◽  
Representative Volume Elements ◽  
Computational Materials

scholarly journals Uncertainty quantification and composition optimization for alloy additive manufacturing through a CALPHAD-based ICME framework

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Xin Wang ◽  
Wei Xiong
Keyword(s):  
Additive Manufacturing ◽  
Uncertainty Quantification ◽  
High Strength ◽  
Nominal Composition ◽  
Structure Property ◽  
Target Composition ◽  
Structure Property Relationships ◽  
Integrated Computational Materials Engineering ◽  
Composition Optimization ◽  
Computational Materials

AbstractDuring powder production, the pre-alloyed powder composition often deviates from the target composition leading to undesirable properties of additive manufacturing (AM) components. Therefore, we developed a method to perform high-throughput calculation and uncertainty quantification by using a CALPHAD-based ICME framework (CALPHAD: calculations of phase diagrams, ICME: integrated computational materials engineering) to optimize the composition, and took the high-strength low-alloy steel (HSLA) as a case study. We analyzed the process–structure–property relationships for 450,000 compositions around the nominal composition of HSLA-115. Properties that are critical for the performance, such as yield strength, impact transition temperature, and weldability, were evaluated to optimize the composition. With the same uncertainty as to the initial composition, and optimized average composition has been determined, which increased the probability of achieving successful AM builds by 44.7%. The present strategy is general and can be applied to other alloy composition optimization to expand the choices of alloy for additive manufacturing. Such a method also calls for high-quality CALPHAD databases and predictive ICME models.

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