scholarly journals An Approach to Upscaling SPPARKS Generated Synthetic Microstructures of Additively Manufactured Metals.

2019 ◽  
Author(s):  
John A. Mitchell
Materials ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 1887 ◽  
Author(s):  
Manuel Henrich ◽  
Felix Pütz ◽  
Sebastian Münstermann

In this study, a novel approach for generating Representative Volume Elements (RVEs) is introduced. In contrast to common generators, the new RVE generator is based on discrete methods to reconstruct synthetic microstructures, using simple methods and a modular structure. The plain and uncomplicated structure of the generator makes the extension with new features quite simple. It is discussed why certain features are essential for microstructural simulations. The discrete methods are implemented into a python tool. A Random Sequential Addition (RSA)-Algorithm for discrete volumes is developed and the tessellation is realized with a discrete tessellation function. The results show that the generator can successfully reconstruct realistic microstructures with elongated grains and martensite bands from given input data sets.


2013 ◽  
Vol 79 ◽  
pp. 960-970 ◽  
Author(s):  
P. Alveen ◽  
D. Carolan ◽  
D. McNamara ◽  
N. Murphy ◽  
A. Ivanković

1988 ◽  
Vol 27 (10) ◽  
pp. 1918 ◽  
Author(s):  
Roger Philip ◽  
Rene Rivoira ◽  
Yves Lepetre ◽  
Georges Rasigni

Author(s):  
Umar Farooq Ghumman ◽  
Sourav Saha ◽  
Lichao Fang ◽  
Wing Kam Liu ◽  
Gregory Wagner ◽  
...  

Abstract Additive Manufacturing (AM) simulations are often employed to replace the expensive experiments to study the effects of processing conditions. In process modeling, one of the key limitations is the lack of reliable validation techniques. The stochastic nature and the spatial heterogeneity of microstructures make it difficult to validate the simulated microstructures against experimentally obtained images through statistical measures (e.g. average and standard deviation of grain sizes). In this work, a validation metric is proposed that can effectively quantify the dissimilarity between two AM microstructures. The methodology involves first calculating the Angularly Resolved Chord Length Distribution (ARCLD) at representative angles and then computing the Earth Mover’s Distance (EMD) to obtain the final unitless score that is named Dissimilarity Score (DS). The efficacy of the proposed methodology was first tested on synthetic microstructures, and then on AM simulations that employ the solidification model-Cellular Automaton (CA) with IN625. Results show that DS effectively measures the dissimilarity between different microstructures. The use of DS is also extended to calibrate the CA processing simulation code to match with experimental AM images from NIST AM-Bench Challenge.


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