A multiscale approach for modeling metal laser welding
Source abstract
Abstract
The growing application of laser welding in the industry motivates the development of computational models to help improve and understand the details of the laser welding process. Classical molecular dynamic (MD) or finite element (FE) methods are insufficient to model the process due to several limitations. The coupling of both methods provides a unique approach for modeling the laser welding process. A laser welding model that accounts for free-electron conduction and three-dimensional laser beam growth was developed on the basis of this coupling. The model was tested on a Cu sample, and the results showed that the amount of energy required to weld the interface was much lower than the energy used in previous studies of MD laser processes. The temperatures in the weld pool and the heat-affected zones were similar to those in previous FE studies. The crystal structure near the weld pool matched the observations of the previous MD studies. Moreover, the scanning speeds associated with this model were relatively higher than those of previous MD models due to the effects of fast electron conduction.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Welding Techniques and Residual Stresses · Hydrogen embrittlement and corrosion behaviors in metals · Corrosion Behavior and Inhibition
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical and Nuclear Engineering, University of Sharjah 1 , P.O. Box 27272 Sharjah, United Arab Emirates
Nuclear Energy System Simulation and Safety Research Group, University of Sharjah 2 , United Arab Emirates
Funding
University of Sharjah
References (35)
Show source-provided references
1. J. Mater. Process. Technol. · 2003 · 134 · 5910.1016/s0924-0136(02)00921-4 ↗
2. J. Phys. D: Appl. Phys. · 2002 · 35 · 230410.1088/0022-3727/35/18/312 ↗
3. 2010
4. CrystEngComm · 2020 · 22 · 413610.1039/d0ce00358a ↗
5. Int. J. Heat Mass Transfer · 2004 · 47 · 438510.1016/j.ijheatmasstransfer.2004.04.036 ↗
6. Intermetallics · 2014 · 46 · 14710.1016/j.intermet.2013.11.012 ↗
7. J. Mater. Process. Technol. · 2005 · 167 · 39310.1016/j.jmatprotec.2005.06.018 ↗
8. Eng. Sci. Technol. Int. J. · 2016 · 19 · 9010.1016/j.jestch.2015.05.009 ↗
9. Int. J. Adv. Manuf. Technol. · 2019 · 102 · 284510.1007/s00170-019-03353-6 ↗
10. J. Laser Appl. · 2015 · 27 · S2800310.2351/1.4906467 ↗
11. 2014
12. J. Heat Transfer · 1995 · 117 · 810.1115/1.2822329 ↗
13. Comput. Therm. Sci. Int. J. · 2021 · 13 · 110.1615/ComputThermalScien.2021036376 ↗
14. 2014 · 291
15. Phys. Chem. Chem. Phys. · 2019 · 21 · 1240610.1039/c9cp02181g ↗
16. Phys. Rev. B · 2003 · 68 · 06411410.1103/physrevb.68.064114 ↗
17. 2004 · 977
18. Appl. Surf. Sci. · 2009 · 255 · 959210.1016/j.apsusc.2009.04.082 ↗
19. J. Heat Transfer · 2004 · 126 · 72710.1115/1.1797011 ↗
20. 2017 · 16310.1016/B978-0-85709-485-8.00006-1 ↗
21. Int. J. Adv. Manuf. Technol. · 2020 · 106 · 346310.1007/s00170-019-04715-w ↗
22. Comput. Methods Appl. Mech. Eng. · 2008 · 197 · 335110.1016/j.cma.2008.02.004 ↗
23. Int. J. Numer. Methods Eng. · 2010 · 83 · 94010.1002/nme.2857 ↗
24. Phys. Rev. Lett. · 1983 · 50 · 128510.1103/physrevlett.50.1285 ↗
25. Mol. Phys. · 1993 · 80 · 138910.1080/00268979300103101 ↗
26. J. Therm. Anal. Calorim. · 2012 · 110 · 15910.1007/s10973-012-2486-0 ↗
27. J. Comput. Phys. · 1995 · 117 · 110.1006/jcph.1995.1039 ↗
28. J. Chem. Phys. · 1985 · 83 · 406910.1063/1.449071 ↗
29. Phys. Rev. B · 2012 · 85 · 20531110.1103/physrevb.85.205311 ↗
30. Philos. Mag. · 2008 · 88 · 172310.1080/14786430802206482 ↗
31. Nature · 1973 · 245 · 16710.1038/245167a0 ↗
32. Int. J. Heat Mass Transfer · 2011 · 54 · 496610.1016/j.ijheatmasstransfer.2011.07.010 ↗
33. Model. Simul. Mater. Sci. Eng. · 2010 · 18 · 08500710.1088/0965-0393/18/8/085007 ↗
34. Model. Simul. Mater. Sci. Eng. · 2010 · 18 · 01501210.1088/0965-0393/18/1/015012 ↗
35. Model. Simul. Mater. Sci. Eng. · 2012 · 20 · 04502110.1088/0965-0393/20/4/045021 ↗