A positioning grid is a key clamping structure for fixing the transverse and axial positions of fuel assemblies in nuclear reactors, and it is generally prepared by the transverse stamping of a Zr-4 sheet. However, the texture formed in the processing process of Zr-4 sheets can affect formability, resulting in cracking in the stamping process. Therefore, the relationship between the formability of Zr-4 sheets and the normal Kearns factor (Fn) of basal texture was studied in this paper. The results showed that the Zr-4 sheet with an Fn equaling 0.720, prepared by an isobaric reduction rolling process, would crack in the stamping process. To avoid the cracking during stamping, the formability improvement of Zr-4 sheets based on texture optimization was discussed. By using the finite element model (FEM) and a visco plastic self-consistent (VPSC) model coupled simulation, the relationship between the initial textures and formabilities of Zr-4 sheet is established. It is found that the hardening exponents (n) decreased with increasing Fns in VPSC simulations. Meanwhile, as the Fn increases, cracks are prone to occur at the bottom corner of the stamped sheet in finite element simulation. Given the results from FEM and VPSC simulations, it is proposed that the Fn should be controlled to be less than 0.7 for preventing cracks in the sheet during stamping. Additionally, a new rolling process named non-isobaric reduction rolling was designed in which the Fn of the Zr-4 sheet is successfully reduced to 0.690. The stamping results indicate that the sheet is free of cracks under an Fn of 0.690. Therefore, texture optimization with the proposed rolling process can improve the formability of Zr-4 sheets, which effectively solves the cracking problem of Zr-4 sheets.

The wall thickness of the TP2 copper tube casting billet is not uniform after a three-roll planetary rotational rolling, which affects the wall thickness uniformity of the copper tube in the subsequent process. In order to study the influence of wall thickness at different positions of copper pipe after rolling on the wall thickness of copper pipe after joint drawing, an online ultrasonic test platform was used to measure the wall thickness of copper pipe after tying, and based on the test data, a finite element model of copper pipe billet was established, and the numerical simulation of joint drawing wall thickness was conducted. Based on the results of the ultrasonic testing experiment and finite element simulation, different neural network models were used to predict the joint tensile wall thickness with the data of the ultrasonic testing experiment as input and the results of finite element simulation as output. The prediction effect of different neural network models was compared, and the results showed that the prediction and fitting effect of the SVM model was better, but overfitting occurred during the fitting process. Furthermore, particle swarm optimization is used to optimize the penalty parameter C and the kernel parameter g in the SVM model. Compared with the traditional SVM model, the PSO–SVM model is more suitable for the prediction of joint tensile wall thickness, which can better guide the production to solve this problem.