Prediction of geometrical shape of coaxial wire feeding cladding in three-beam

The work aim to study parameters window of “three beam” coaxial wire feeding and the mapping relationship between parameters and cladding geometry. Firstly,the process interval of four process parameters of laser power, scanning speed, wire feeding speed and defocusing amount was studied by single factor experiment method; Secondly, the height, width and cross-sectional area of the cladding layer was used as the quantitative indicators of the geometry of the cladding layer; Finally, a neural network model and the quadratic regression model were set up respectively which were used to predict the mapping relationship between the cladding process parameters and the quantitative indicators of the cladding layer. Based on single-channel single-factor experiments, when the laser power was between 1 300 W and 1 700 W, the scanning speed was between 3 mm/s and 7 mm/s, the wire feeding speed was between 9 mm/s and 15 mm/s, and the defocusing amount was between −2.5 mm and −1.5 mm can get the cladding of liquid bridge transition with good quality. Besides, in the prediction of the test sample data, under the condition of 85% confidence, the prediction accuracy of the BP neural network model for the height, width and cross-sectional area of the cladding layer is 100%, 100%, 93.33%, and the root mean square error is 0.21,0.07,0.24. The accuracy of the quadratic regression model is 100%, 66.67%, and 73.33%, respectively, and the root mean square errors are 0.21, 0.13, and 0.28, respectively. From the result, the cross terms of the variables in the quadratic regression model failed to fit the nonlinear process of wire cladding. By contrast, BP neural network obtained better prediction results.