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Article citations


Kim, J., Oh, S. and Ki, H. (2015) A Study of Keyhole Geometry in Laser Welding of Zinc-Coated and Uncoated Steels Using a Coaxial Observation Method. Journal of Materials Processing Technology, 225, 451-462.

has been cited by the following article:

  • TITLE: Experimental Investigation of Laser Welding Process in Overlap Joint Configuration

    AUTHORS: Kamel Oussaid, Abderrazak El Ouafi, Ahmed Chebak

    KEYWORDS: Laser Welding, Nd:YAG Laser Source, Low Carbon Galvanized Steel, Taguchi Method, Overlap Welding

    JOURNAL NAME: Journal of Materials Science and Chemical Engineering, Vol.7 No.3, March 27, 2019

    ABSTRACT: This paper presents an experimental investigation of laser overlap welding of low carbon galvanized steel. Based on a structured experimental design using the Taguchi method, the investigation is focused on the evaluation of various laser welding parameters effects on the welds quality. Welding experiments are conducted using a 3 kW Nd:YAG laser source. The selected laser welding parameters (laser power, welding speed, laser fiber diameter, gap between sheets and sheets thickness) are combined and used to evaluate the variation of three geometrical characteristics of the weld (penetration depth, bead width at the surface and bead width at the interface). Various improved statistical tools are used to analyze the effects of welding parameters on the variation of the weld quality and to identify the possible relationship between these parameters and the geometrical characteristics of the weld. The results reveal that the reached hardness values are similar for all the experimental tests and all welding parameters are relevant to the weld quality with a relative predominance of laser power and welding speed. The effect of the gap is relatively limited. The investigation results reveal also that there are many options to consider for building an efficient welds quality prediction model. Results achieved using an artificial neural network based simplified model provide an indication of the prediction model performances.