Capturing Local Material Heterogeneities in Numerical Modelling of Microstructure Evolution
1
Faculty of Metals Engineering and Industrial Computer Science, AGH University of Science and Technology, Poland
Submission date: 2021-09-06
Final revision date: 2021-10-11
Acceptance date: 2021-10-14
Online publication date: 2021-10-19
Publication date: 2021-12-08
Corresponding author
Lukasz Madej
Faculty of Metals Engineering and Industrial Computer Science, AGH University of Science and Technology, Mickiewicza 30, 30-059, Krakow, Poland
Faculty of Metals Engineering and Industrial Computer Science, AGH University of Science and Technology, Mickiewicza 30, 30-059, Krakow, Poland
Journal of Machine Engineering 2021;21(4):29-48
KEYWORDS
TOPICS
ABSTRACT
The work focuses on developing the complex digital shadow of the metallic material microstructure that can predict its evolution during metal forming operations. Therefore, such a digital shadow has to consider all major physical mechanisms influencing the particular investigated phenomenon. The motivation for the work is directly related to the development of modern metallic materials, often of multiphase nature, which leads to local heterogeneities influencing microstructure behavior and eventually macroscopic properties of the final product. The concept of the digital microstructure shadow, stages of the model development, and examples of practical applications to simulation of microstructure evolution are presented within the work. Capturing local heterogeneities that have a physical origin and eliminating numerical artifacts is particularly addressed. Obtained results demonstrate the capabilities of such a digital microstructure shadow approach in the numerical design of final product properties.
REFERENCES (34)
1.
NAWAZ M., SHAKOOR R.A., KAHRAMAN R., MONTEMOR M.F., 2021, Cerium Oxide Loaded with Gum Arabic As Environmentally Friendly Anti-Corrosion Additive for Protection of Coated Steel, Materials and Design. 198, 109361, https://doi.org/10.1016/j.matd....
2.
DE SOUZA J.F.T., PACCA S.A., 2021, Carbon Reduction Potential and Costs Through Circular Bioeconomy in the Brazilian Steel Industry, Resources, Conservation and Recycling, 169, https://doi.org/10.1016/j.resc.... 2021.105517.
3.
GHANAVATI R., NAFFAKH-MOOSAVY H., 2021, Additive Manufacturing of Functionally Graded Metallic Materials: A Review of Experimental and Numerical Studies, Journal of Materials Research and Technology, 13, 1628–1664, https://doi.org/10.1016/j.jmrt....
4.
ZHANG C., OUYANG D., PAULY S., LIU L., 2021, 3D Printing of Bulk Metallic Glasses, Materials Science and Engineering, R: Reports, 145, 100625, https://doi.org/10.1016/j.mser....
5.
DELKOWSKI M., SMITH C.T.G., ANGUITA V.J., SILVA S.R.P., 2021, Increasing the Robustness and Crack Resistivity of High-Performance Carbon Fiber Composites for Space Applications, IScience, 24. https://doi.org/ 10.1016/j.isci.2021.102692.
6.
MAY M., RUPAKULA G.D., MATURA P., 2020, Non-Polymer-Matrix Composite Materials for Space Applications, Composites Part C: Open Access, 3, 100057, https://doi.org/10.1016/j.jcom....
7.
FONSTEIN N., 2015, Advanced High Strength Sheet Steels, 1st ed., Springer International Publishing, https://doi. org/ 10.1007/978-3-319-19165-2.
8.
LAZURENKO D.V., LOZANOV V.V., STARK A., PYCZAK F., RUKTUEV A.A., EMURLAEV K.I., SONG L., BATAEV I.A., IVANOV I.V., OGNEVA T.S., BATAEV A.A., 2021, In Situ Synchrotron X-Ray Diffraction Study of Reaction Routes in Ti-Al3Ti-Based Composites: The Effect of Transition Metals on L12 Structure Stabilization, Journal of Alloys and Compounds, 875, 160004, https://doi.org/10.1016/j.jall....
9.
DOU K., LORDAN E., ZHANG Y.J., JACOT A., FAN Z.Y., 2020, A Complete Computer Aided Engineering (CAE) Modelling and Optimization of High Pressure Die Casting (HPDC) Process, Journal of Manufacturing Processes, 60, 435–446, https://doi.org/10.1016/j.jmap....
10.
MADEJ L., 2017, Digital/Virtual Microstructures in Application to Metals Engineering – A Review, Archives of Civil and Mechanical Engineering, 17, 839–854, https://doi.org/10.1016/j.acme....
11.
ZHANG X., GUO X., SONG K., WANG X., FENG J., LI S., LIN H., 2021, Simulation and Verification of Thermal Conductivity of CuCr30 Contact Material Based on Morphological Changes of Cr Particles, Materials Today Communications, 26, 102153, https://doi.org/10.1016/j.mtco....
12.
BARGMANN S., KLUSEMANN B., MARKMANN J., SCHNABEL J.E., SCHNEIDER K., SOYARSLAN C., WILMERS J., 2018, Generation of 3D Representative Volume Elements for Heterogeneous Materials: A Review, Progress in Materials Science, 96, 322–384. https://doi.org/https://doi.or....
13.
MIN K.M., JEONG W., HONG S.H., LEE C.A., CHA P.R., HAN H.N., LEE M.G., 2020, Integrated Crystal Plasticity and Phase Field Model for Prediction of Recrystallization Texture and Anisotropic Mechanical Properties of Cold-Rolled Ultra-Low Carbon Steels, International Journal of Plasticity, 127, 102644. https://doi.org/10.1016/j.ijpl....
14.
SZELIGA D., BZOWSKI K., RAUCH Ł., KUZIAK R., PIETRZYK M., 2020, Mean Field and Full Field Modelling of Microstructure Evolution and Phase Transformations During Hot Forming and Cooling of Low Carbon Steels, Computer Methods in Material Science, 20, 121–132, https://doi.org/10.7494/cmms.2....
15.
CHEN K., LI H., JIANG Z., LIU F., KANG C., MA X., ZHAO B., 2021, Multiphase Microstructure Formation and Its Effect on Fracture Behavior of Medium Carbon High Silicon High Strength Steel, Journal of Materials Science and Technology, 72, 81–92, https://doi.org/10.1016/j.jmst....
16.
SONG C., WANG H., SUN Z., WEI Z., YU H., CHEN H., WANG Y., LU J., 2020, Effect of Multiphase Microstructure on Fatigue Crack Propagation Behavior in TRIP-Assisted Steels, International Journal of Fatigue, 133, 105425, https://doi.org/10.1016/j.ijfa....
17.
MADEJ L., RAUCH L., PERZYNSKI K., CYBULKA P., 2011, Digital Material Representation As an Efficient Tool for Strain Inhomogeneities Analysis at the Micro scale Level, Archives of Civil and Mechanical Engineering, 11, 661–679, https://doi.org/10.1016/S1644-....
18.
SZYNDLER J., MADEJ Ł., 2015, Numerical Analysis of the Influence of Number of Grains, FE Mesh Density and Friction Coefficient on Representativeness Aspects of the Polycrystalline Digital Material Representation - Plane Strain Deformation Case Study, Computational Materials Science, 96, 200–213, https://doi.org/10.1016/ j.commatsci.2014.08.037.
19.
LEWIS A.C., GELTMACHER A.B., 2006, Image-Based Modeling of the Response of Experimental 3D Microstructures to Mechanical Loading, Scripta Materialia, 55, 81–85, https://doi.org/10.1016/j.scri.... 2006.01.043.
20.
SZYNDLER J., MADEJ L., 2015, Comparison of 3D and 2D Digital Material Representation Channel Die Compression Results, Materials Science & Technology, Columbus, 619–626.
21.
BOGUŃ K., SITKO M., MOJŻESZKO M., MADEJ Ł., 2021, Cellular Automata-Based Computational Library for Development of Digital Material Representation Models of Heterogenous Microstructures, Archives of Civil and Mechanical Engineering, 21, 61, https://doi.org/10.1007/s43452....
22.
SITKO M., MOJZESZKO M., RYCHLOWSKI L., CIOS G., BALA P., MUSZKA K., MADEJ L., 2020, Numerical Procedure of Three-Dimensional Reconstruction of Ferrite-Pearlite Microstructure Data from SEM/EBSD Serial Sectioning, Procedia Manufacturing, 47, 1217–1222. https://doi.org/10.1016/j.prom.... 2020.04.183.
23.
ZANKEL A., WAGNER J., POELT P., 2014, Serial Sectioning Methods for 3D Investigations in Materials Science, Micron, 62, 66–78, https://doi.org/10.1016/j.micr....
24.
POKHAREL R., LIND J., LI S.F., KENESEI P., LEBENSOHN R.A., SUTER R.M., ROLLETT A.D., 2015, In-Situ Observation of Bulk 3D Grain Evolution During Plastic Deformation in Polycrystalline Cu, International Journal of Plasticity, 67, 217–234. https://doi.org/10.1016/j.ijpl....
25.
DI CAPRIO D., STAFIEJ J., LUCIANO G., ARURAULT L., 2016, 3D Cellular Automata Simulations of Intra and Intergranular corrosion, Corrosion Science, 112, 438–450, https://doi.org/10.1016/j.cors....
26.
HAJDER L., MADEJ L., 2020, Sphere Packing Algorithm for the Generation of Digital Models of Polycrystalline Microstructures with Heterogeneous Grain Sizes, Computer Methods in Materials Science, 20, 22–30.
27.
MAAZI N., LEZZAR B., 2020, An Efficient Monte Carlo Potts Method for the Grain Growth Simulation of Single-Phase Systems, Computer Methods in Material Science, 20, 85–94, https://doi.org/10.7494/cmms.2....
28.
XUE Y., TAKATA N., LI H., KOBASHI M., YUAN L., 2021, Critical Resolved Shear Stress of Activated Slips Measured by Micropillar Compression Tests for Single-Crystals of Cr-Based Laves Phases, Materials Science and Engineering A, 806, 140861, https://doi.org/10.1016/j.msea....
29.
VEENADEVI S.V., ANANTH A.G., 2012, Fractal Image Compression Using Quadtree Decomposition and Huffman Coding, Signal & Image Processing, An International Journal, 3, 207–212. https://doi.org/10.5121/sipij. 2012.3215.
30.
SLOAN S.W., HOULSBY G.T., 1984, An Implementation of Watson’s Algorithm for Computing 2-Dimensional Delaunay Triangulations, Advances in Engineering Software 6/4, 192–197, https://doi.org/10.1016/0141-1... (84)90003-2.
31.
FABRI A., 1988, Voronoi Diagrams in CGAL, the Computational Geometry Algorithms Library, Communications in Applied Numerical Methods, 4, 709–712, https://doi.org/10.1109/ISVD.2....
32.
LIN T.J., GUAN Z.Q., CHANG J.H., LO S.H., 2014, Vertex-Ball Spring Smoothing: An Efficient Method for Unstructured Dynamic Hybrid Meshes, Computers and Structures. 136, 24–33, https://doi.org/10.1016/j.comp struc.2014.01.028.
33.
MADEJ L., SITKO M., RADWANSKI K., KUZIAK R., 2016, Validation and Predictions of Coupled Finite Element and Cellular Automata Model: Influence of the Degree of Deformation on Static Recrystallization Kinetics Case Study, Materials Chemistry and Physics, 179, 282–294, https://doi.org/10.1016/j.matc.... 2016.05.040.
34.
SITKO M., CHAO Q., WANG J., PERZYNSKI K., MUSZKA K., MADEJ L., 2020, A Parallel Version of the Cellular Automata Static Recrystallization Model Dedicated for High Performance Computing Platforms – Development and Verification, Computational Materials Science, 172, 109283, https://doi.org/10.1016/j.com matsci.2019.109283.
CITATIONS (1):
1.
Computationally Efficient Cellular Automata‐Based Full‐Field Models of Static Recrystallization: A Perspective Review
Lukasz Madej, Mateusz Sitko
steel research international
Lukasz Madej, Mateusz Sitko
steel research international
| eISSN: | 2391-8071 |
| ISSN: | 1895-7595 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
