journal article·2024-01-10Open access

Quantitively evaluation of fillers' alignment for targeted manufacturing of functional composite materials

Xinfeng Zhang, Zekun Ye, Xuan Yang, Yiwen Fan, Run Hu, Bin Xie, Xiaobing Luo

Polymer Composites· Volume 45· Issue 1

Abstract

Abstract Filler‐reinforced composite materials are widely used in numerous domains such as energy, optoelectronics, chemistry, and so forth, owing to their versatile microstructures, excellent properties, and low fabrication cost. By finely regulating the distribution and orientation of fillers in the base material, composite materials with fantastic thermal, chemical, and mechanical properties have been demonstrated, which are also known as “metamaterials”. For the anisotropic fillers, the orientation of fillers is crucial for designing and predicting the properties of composite materials. However, accurate and efficient evaluation of fillers' orientation remains a challenge, which severely hinders the microstructural design and topological optimization of composite materials. To solve this problem, a bidirectional pixel‐traversal method which combines with microscopic image processing was developed to evaluate the orientation of fillers in composite materials. Specifically, a vertical orientation maximization algorithm was designed to evaluate the oriented degree and oriented direction of the whole fillers, and a crossing traversal algorithm was designed to evaluate the oriented angle of individual filler. Further, this method was applied to the evaluation of microscopic images from different researches. The evaluated results show a high accuracy (average error is less than 2%) and wide applicability in the evaluation of different types of fillers. This method reveals the underlying relationship between microscopic filler orientation and macroscopic composites properties, which paves a new way for the direct optimization and processing of composite materials.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Composite Material Mechanics · Epoxy Resin Curing Processes · Topology Optimization in Engineering

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

State Key Laboratory of Combustion, School of Energy and Power Engineering Huazhong University of Science and Technology Wuhan China

State Key Laboratory for Novel Software Technology, Department of Computer Science and Technology Nanjing University Nanjing China

School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China

Funding

National Natural Science Foundation of China

References (49)

Show source-provided references
1. 10.1088/1402-4896/acde1610.1088/1402-4896/acde16 ↗
2. 10.1002/jnm.310610.1002/jnm.3106 ↗
3. 10.1016/j.mejo.2023.10583210.1016/j.mejo.2023.105832 ↗
4. 10.1007/s10825-023-02008-w10.1007/s10825-023-02008-w ↗
5. 10.1007/s12633-021-01288-w10.1007/s12633-021-01288-w ↗
6. 10.1016/j.progpolymsci.2011.12.00510.1016/j.progpolymsci.2011.12.005 ↗
7. 10.1002/adma.20210349510.1002/adma.202103495 ↗
8. 10.1016/j.compositesa.2019.05.02410.1016/j.compositesa.2019.05.024 ↗
9. 10.1021/acsami.5b0300710.1021/acsami.5b03007 ↗
10. 10.1016/j.compscitech.2021.10892210.1016/j.compscitech.2021.108922 ↗
11. 10.1002/adma.20140173610.1002/adma.201401736 ↗
12. 10.1002/pc.2652710.1002/pc.26527 ↗
13. 10.1080/09506608.2017.130101410.1080/09506608.2017.1301014 ↗
14. 10.1016/j.polymer.2019.12176010.1016/j.polymer.2019.121760 ↗
15. 10.1002/pc.2744310.1002/pc.27443 ↗
16. 10.1002/adfm.20190041210.1002/adfm.201900412 ↗
17. 10.1002/adem.20140004510.1002/adem.201400045 ↗
18. 10.1002/adem.20160013010.1002/adem.201600130 ↗
19. 10.1016/j.carbon.2009.09.01410.1016/j.carbon.2009.09.014 ↗
20. 10.1016/j.carbon.2015.04.08510.1016/j.carbon.2015.04.085 ↗
21. 10.1016/j.matdes.2016.07.04510.1016/j.matdes.2016.07.045 ↗
22. 10.1122/1.54994510.1122/1.549945 ↗
23. 10.1016/j.compscitech.2020.10818210.1016/j.compscitech.2020.108182 ↗
24. 10.1002/pc.2623910.1002/pc.26239 ↗
25. 10.1063/1.288534710.1063/1.2885347 ↗
26. 10.1016/j.carbon.2011.06.08910.1016/j.carbon.2011.06.089 ↗
27. 10.1080/21663831.2012.68658610.1080/21663831.2012.686586 ↗
28. 10.1021/acsmacrolett.9b0049610.1021/acsmacrolett.9b00496 ↗
29. 10.1016/j.compscitech.2016.12.00610.1016/j.compscitech.2016.12.006 ↗
30. 10.1007/s10443-021-09875-110.1007/s10443-021-09875-1 ↗
31. 10.1016/S0266-3538(96)00072-310.1016/S0266-3538(96)00072-3 ↗
32. 10.1016/j.carbon.2018.04.06310.1016/j.carbon.2018.04.063 ↗
33. 10.1016/j.compositesa.2016.12.02810.1016/j.compositesa.2016.12.028 ↗
34. 10.1016/j.compositesa.2014.04.01710.1016/j.compositesa.2014.04.017 ↗
35. 10.1016/S0266-3538(01)00038-010.1016/S0266-3538(01)00038-0 ↗
36. 10.1016/S0266-3538(01)00106-310.1016/S0266-3538(01)00106-3 ↗
37. 10.1016/j.compositesa.2005.04.02110.1016/j.compositesa.2005.04.021 ↗
38. 10.1016/j.compositesa.2014.11.01610.1016/j.compositesa.2014.11.016 ↗
39. 10.1016/j.compstruct.2019.11181810.1016/j.compstruct.2019.111818 ↗
40. 10.3139/217.95026210.3139/217.950262 ↗
41. 10.1016/j.polymertesting.2015.01.01610.1016/j.polymertesting.2015.01.016 ↗
42. 10.1016/0956-7151(94)90266-610.1016/0956-7151(94)90266-6 ↗
43. 10.1177/002199830203600851410.1177/0021998302036008514 ↗
44. 10.1002/pc.1054110.1002/pc.10541 ↗
45. 10.1016/j.nanoen.2022.10798610.1016/j.nanoen.2022.107986 ↗
46. ChenS.Constrained Particle Swarm Optimization 2009–2018.
47. 10.1039/D2NR02421G10.1039/D2NR02421G ↗
48. 10.1073/pnas.161637711410.1073/pnas.1616377114 ↗
49. 10.1021/acsnano.9b0516310.1021/acsnano.9b05163 ↗