Surrogate model prediction and multi-objective optimization of induction heating for CFRP tubes

To address the issues of high energy consumption and slow heating rates in traditional curing processes for carbon fiber reinforced polymer (CFRP) tubes, this paper proposed the use of a segmented semi-circular rectangular coil for the external induction heating of rotating CFRP tubes, and established a corresponding multiphysics finite element model. The regulatory mechanisms of current, frequency, the number of coil turns, and the coil-to-tube distance on the maximum temperature and the temperature non-uniformity index were systematically investigated. To overcome the computational bottleneck of the excessive time consumption associated with transient coupled simulations, this paper introduced Bayesian-optimized Support Vector Regression (BO-SVR) to construct a high-precision surrogate model of the temperature field, which was then combined with the Multi-objective Snow Ablation (MSA) optimization algorithm to perform Pareto collaborative optimization of the process parameters. The results indicate that the optimized process scheme meets the requirements of specific curing temperature zones, with the temperature non-uniformity index reduced by 63% compared to the original scheme. Furthermore, the predictions of the surrogate model are in high agreement with the finite element simulation results. This study provides a solid theoretical foundation and practical process guidance for the efficient and high-quality induction heating molding of rotating CFRP tubes.