Parameter Optimization and Prediction Model of Induction Heating for Large-Diameter Pipe

Abstract

The parameters of induction heating of large-diameter pipes have a direct effect on the final processing quality of the elbow, and the complexity of multifield coupling of magnetothermal force in induction heating can make it impossible to quantitatively optimize the design parameters of the induction heating device. In this paper, X80 pipeline steel induction heating is taken as the research object, and a corresponding numerical model is established. The influence of induction heating process parameters on the heating temperature of pipeline steel under the skin effect is determined. First, the influence of process parameters on the heating effect of pipeline steel is quantified by orthogonal test. Then, taking the optimum temperature difference between the inner and outer wall of X80 pipeline steel during the induction heating process as a target, the optimal process parameter set of the pipe induction heating is determined by using neural network genetic algorithm. Finally, comparing the relevant test criteria of the regression equation, the optimum mathematical prediction model of the outer wall temperature of the pipe induction heating process is obtained, which provides a theoretical basis for optimization of the process parameters of the pipe-based induction heating device.