Finite Element-Assisted Random Forest Modeling for Development of Weld Schedules in Multi-Cycle Resistance End-Closure Welding of Zircaloy-4 Nuclear Fuel Elements

Resistance end-closure welding of Zircaloy-4 nuclear fuel elements is a critical operation in Pressurized Heavy Water Reactor fuel fabrication. Weld quality and dimensional integrity depend on upset formation, reflecting combined effects of heat generation and forging during welding. Control of pre-heat upset is essential for establishing intimate contact at the faying surfaces, whereas total upset must be maintained within specified limits to satisfy dimensional requirements for fuel bundle assembly. In the present study, Random Forest (RF) regression models were developed to predict pre-heat and total upset. A comprehensive dataset was generated using a validated Finite Element (FE) model by varying pre-heat current, post-heat current, pre-heat duration, post-heat duration, and squeeze force over the practical operating range. The RF hyperparameters were optimized using a coarse-to-fine grid search strategy with five-fold cross-validation and Root Mean Square Error (RMSE) as the optimization metric. The optimized models were evaluated using an independent blind-test dataset and achieved RMSE values of 0.06 mm for pre-heat upset and 0.08 mm for total upset. The validated models rapidly screened 4725 full-factorial weld schedules and identified candidate schedules whose predicted responses satisfied the required upset criteria. Experimental verification demonstrated good agreement with the model predictions. The proposed FE–RF approach provides an efficient tool for weld-schedule development in nuclear fuel manufacturing while reducing the need for extensive simulations and experiments.