Analysis of the spatter distribution in laser welding using numerical statistical methods
Source abstract
Abstract
In laser welding, the formation of spatter on the weld surface is a common defect that can adversely impact weld properties. It is crucial to identify optimal laser parameters to minimize spatter formation. This study analyzes spatter distribution along a designated welding path. The influence of laser parameters, such as laser power and welding speed, on the spatter area is examined. Multivariate regression analysis reveals a direct proportionality between spatter formation and laser power, while an inverse relationship is observed with welding speed. Consequently, the proposed optimal laser parameters aim to minimize spatter. Analysis of variance confirms the significant impact of laser power on spatter formation compared to the welding speed.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Welding Techniques and Residual Stresses · Surface Roughness and Optical Measurements · Diverse Research Studies Overview
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical and Materials Engineering, University of Nebraska 1 , Lincoln, Nebraska 68588
Department of Future Convergence Engineering, Kongju National University 2 , Cheonan 31050,
Department of Mechanical and Automotive Engineering, Kongju National University 3 , Cheonan 31050,
Center for Advanced Powder Materials and Parts of Powder (CAMP2), Kongju National University 4 , Cheonan 31050,
Global Institute of Manufacturing Technology (GITECH), Kongju National University 5 , Cheonan 31050,
Funding
National Research Foundation of Korea · Korea Institute of Energy Technology Evaluation and Planning · Ministry of Trade, Industry and Energy
References (37)
Show source-provided references
1. Gap-free fibre laser welding of Zn-coated steel on Al alloy for light-weight automotive applications10.1016/j.matdes.2010.08.034 ↗
2. Welding with fiber lasers from 200 to 17000 W10.2351/1.5060499 ↗
3. Developments and trends in laser welding of sheet metal10.4028/www.scientific.net/amr.6-8.59 ↗
4. Geometrical, microstructural and mechanical characterization of pulse laser welded thin sheet 5052-H32 aluminium alloy for aerospace applications10.1016/S1003-6326(19)64977-0 ↗
5. High-speed x-ray analysis of spatter formation in laser welding of copper10.1016/j.phpro.2013.03.058 ↗
6. Spatter formation in laser welding with beam oscillation10.1016/j.phpro.2013.03.047 ↗
7. Analysis of spatters in laser welding with beam oscillation: A machine vision approach10.1016/j.cirpj.2016.05.004 ↗
8. The characteristics of laser welding of a thin aluminum tab and steel battery case for lithium-ion battery10.3390/met10060842 ↗
9. A study on laser welding for dissimilar metals of aluminum and copper using pulsed fiber laser10.1007/S12541-024-01073-W ↗
10. Effect of welding path on the weld quality of aluminum tab and steel battery case in lithium-ion battery10.1007/s12206-024-0417-1 ↗
11. Understanding of spatter formation in fiber laser welding of 5083 aluminum alloy10.1016/j.ijheatmasstransfer.2017.05.125 ↗
12. Spatter in laser welding10.2351/1.3597830 ↗
13. The importance of spatter formed in shielded metal arc welding10.1016/j.matchar.2006.09.011 ↗
14. Numerical simulation of spatter formation during fiber laser welding of 5083 aluminum alloy at full penetration condition10.1016/j.optlastec.2017.10.010 ↗
15. Observation of the keyhole behavior, spatter, and keyhole-induced bubble formation in laser welding of a steel/glass sandwich10.1007/s40194-019-00710-7 ↗
16. Optimization of laser welding of DP780 to Al5052 joints for weld width and lap-shear force using response surface methodology10.1016/j.optlastec.2020.106072 ↗
17. Novel measures for spatter prediction in laser welding of thin-gage zinc-coated steel10.1016/j.ijheatmasstransfer.2020.120830 ↗
18. Spatter feature analysis in laser welding based on motion tracking method10.1016/j.jmapro.2020.04.016 ↗
19. Relationship between spatter formation and dynamic molten pool during high-power deep-penetration laser welding10.1016/j.apsusc.2014.03.030 ↗
20. Reduction of spatter formation by superposition of two laser intensities10.2351/1.5118549 ↗
21. Understanding the influence of the focal position in laser welding on spatter reduction10.2351/1.5062230 ↗
22. The effect of using a metal tube on laser welding of the battery case and the tab for lithium-ion battery10.3390/ma13194460 ↗
23. The ImageJ ecosystem: An open platform for biomedical image analysis10.1002/mrd.22489 ↗
24. NIH image to ImageJ: 25 years of image analysis10.1038/nmeth.2089 ↗
25. Optimal process design for laser transmission welding of acrylics using desirability function analysis and overlay contour plots10.1504/IJMR.2011.037913 ↗
26. A sequentially integrated multi-criteria optimization approach applied to laser transmission weld quality enhancement—A case study10.1007/S00170-012-4203-3 ↗
27. Parameter optimization using a regression model and fitness function in laser welding of aluminum alloys for car bodies10.1007/s12541-011-0041-8 ↗
28. Weld-bead profile and costs optimisation of the CO2 dissimilar laser welding process of low carbon steel and austenitic steel AISI31610.1016/j.optlastec.2010.05.008 ↗
29. Effects of process parameters on the bead geometry of laser beam butt welded stainless steel sheets10.1007/s00170-006-0432-7 ↗
30. Effect of laser welding parameters on the heat input and weld-bead profile10.1016/j.jmatprotec.2005.02.060 ↗
31. Optimizing the CO2 laser welding process for dissimilar materials10.1016/j.optlaseng.2013.01.024 ↗
32. Observation and understanding in laser welding of pure titanium at subatmospheric pressure10.1364/oe.25.013539 ↗
33. Surface tension of molten Al-Si alloy at temperatures ranging from 923 to 1123 K10.1007/s11434-008-0372-8 ↗
34. Performance evaluation of TWIST welding using machine learning assisted evolutionary algorithms10.1007/s13369-023-08238-1 ↗
35. Selection of optimal parameters for laser transmission welding of polycarbonate using desirability function analysis10.1016/J.MATPR.2017.07.042 ↗
36. Newton’s method and its use in optimization10.1016/j.ejor.2005.06.076 ↗
37. Process optimization10.1016/B978-0-12-802961-9.00004-8 ↗