Application of laser cleaning on AlMg4.5Mn0.4 sheets for adhesive bonding

被引:0
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作者
Gacs, Jenő [1 ]
Sára Bogya, Erzsébet [1 ]
Kocsis, László [1 ]
Jacob, Timo [2 ]
机构
[1] Robert Bosch Hungary Kft., Gyömrői út 104 Budapest 1103, Hungary
[2] Ulm University, Institute of Electrochemistry, Albert-Einstein-Allee 47, Ulm,89081, Germany
来源
International Journal of Adhesion and Adhesives | 2022年 / 115卷
关键词
Cleaning - Energy dispersive spectroscopy - Magnesium alloys - Surface morphology - Ternary alloys - Morphology - Surface roughness - Adhesives - Aluminum alloys - Oxidation - Silicones - Design of experiments - Lubricating oils - Tensile strength - Manganese alloys - Microstructure - Scanning electron microscopy;
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摘要
The preparation of aluminum surfaces for adhesion has been a cumbersome task due to the diverse contamination profile (lubricating oils, residual particles, surface oxides). Recently, laser cleaning has emerged as a potential non-contact solution for surface preparation. In this paper, the effect of various parameters (frequency, power, scan speed, pulse time, hatch, defocus) of a 50 W marking laser was studied within a DoE (Design of Experiments). As a model material, AlMg4.5Mn0.4 sheets contaminated with a lubricating oil were used. During the surface treatment of the samples, four significantly different surface morphologies were formed: A – no discrete microstructures, B – cell-like microstructures, C – groove-like microstructures, and D – sandpaper-like microstructures. The developed surfaces were characterized with optical microscopy, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX) and optical profilometry. The adherability of the samples was characterized with pull tests using a commercially available silicone adhesive. Both the tensile strength and the fracture pattern of the samples showed a significant surface microstructure dependence, which did not correlate with the measured surface roughness. Based on the results, the strongest adhesion was achieved on surfaces with low oxygen content. To be able to predict the resulting microstructure after the laser treatment, two decision trees were proposed, one based on the set- and one based on the calculated laser parameters. © 2022 Elsevier Ltd
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