Abstract
Laser cladding is a method of material deposition used to envelop a substrate with a surface of material which has superior characteristics, such as corrosion or abrasion resistance. There are several process parameters to be adjusted to generate a deposited bead or a layer, and developing a model to predict a bead shape is challenging due to the highly level of coupling between the manufacturing process parameters and the bead geometry. This paper will present results for an experimental study where P420 steel cladding powder (a steel commonly used in injection molding) is deposited on low carbon structural steel plates using the coaxial powder flow laser cladding method. Five process parameters such as laser power, scanning speed and powder flow rate etc. are varied to explore their impact on the bead height, width, penetration, area, dilution area, and the bead shape. The bead shape to process parameter relationships are explored and predictive models are developed using analysis of variance, a ‘lumped parameter model, and artificial neural network (ANN) approaches. The best fit for a predictive model is achieved with the ANN approach. This work will be extended to incorporate a variety of substrate and clad materials, and layering parameters.
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