After rolling, TP2 copper tubes exhibit defects such as sawtooth marks, cracks, and uneven wall thickness after joint drawing, which severely affects the quality of the finished copper tubes. To study the effect of drawing process parameters on wall thickness uniformity, an ultrasonic detection platform for measuring the wall thickness of rolled copper tubes was constructed to verify the accuracy of the experimental equipment. Using the detected data, a finite element model of drawn copper tubes was established, and numerical simulation studies were conducted to analyze the influence of parameters such as outer die taper angle, drawing speed, and friction coefficient on drawing force, maximum temperature, average wall thickness, and wall thickness uniformity. To address the problem of the large number of finite element model meshes and low solution efficiency, the wall thickness uniformity was predicted using a radial basis function (RBF) neural network, and parameter optimization was performed using the particle swarm optimization (PSO) algorithm. The research results show that the RBF neural network can accurately predict wall thickness uniformity, and using the PSO optimization algorithm, the best parameter combination can reduce the wall thickness uniformity after drawing in finite element simulation.

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.

In the three-roll planetary rolling process, excessively high surface temperature of the rolls can easily lead to copper adhesion, deterioration of roll surface quality, shortened rolling lifespan, and severely affect the quality of copper tube products as well as production efficiency. To improve the cooling efficiency of the roll cooling system, this study developed a fluid–solid–heat coupled model and validated it experimentally to investigate the effects of nozzle diameter, spray angle, and axial position of the spray ring on the cooling performance of the roll surface. Given the low computational efficiency of finite element simulations, three machine learning models—Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Support Vector Machine (SVM)—were introduced and evaluated to identify the most suitable predictive model. Subsequently, the Particle Swarm Optimization (PSO) algorithm was employed to optimize the geometric parameters of the spray ring. The results show that the maximum deviation between the coupled model predictions and experimental data was 4.36%, meeting engineering accuracy requirements. Among the three machine learning models, the RF model demonstrated the best performance, achieving RMSE, MAE, and R² values of 1.7336, 1.3203, and 0.9082, respectively, on the test set. The combined RF-PSO optimization approach increased the heat transfer coefficient by 44.72%, providing a robust theoretical foundation for practical process parameter optimization and precision tube manufacturing.

During the three-roll planetary rolling process, the cooling efficiency of conventional nozzle structures is insufficient, which can easily lead to copper adhesion on the roll surface, product quality degradation, and shortened roll lifespan, thereby limiting both the quality of copper tubes and overall production efficiency. To enhance the performance of the cooling system, this study proposes a novel elliptical nozzle structure and develops a multiphysics coupled model to reveal the effects of nozzle inclination angle and gas–liquid pressure ratio on cooling behavior. An independently constructed experimental platform was used to measure jet flow patterns and the surface temperature of alloy steel plates under various parameter conditions, thereby validating the accuracy and reliability of the numerical model. The results indicate that, under the same effective outlet area, the elliptical nozzle significantly increases jet exit velocity and overall cooling efficiency. To address the issues of high computational cost and low efficiency during optimization using finite element simulations, a high-accuracy surrogate model based on a Random Forest (RF) algorithm was introduced, and the geometric parameters of the nozzle were globally optimized using a Particle Swarm Optimization (PSO) algorithm. Ultimately, the combined RF-PSO strategy improved the average heat transfer coefficient by 55.57%, markedly enhancing the roll cooling performance and providing a solid theoretical basis and methodological reference for high-performance cooling system design and precision copper tube manufacturing.

In this study, an “oxide layer” was formed on the surface of Cu-0.97Cr-0.11Zr alloy sheets by introducing oxygen via a non-vacuum solid solution treatment, followed by vacuum solid solution and aging treatments to obtain a layered composite microstructure. The macro- and microstructures were characterized using optical microscopy (OM), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Furthermore, the properties of the alloy were evaluated through electrical conductivity and hardness measurements. The results indicate that during the non-vacuum solid solution process, oxygen atoms diffused into the copper alloy matrix and underwent an in situ oxidation reaction with the solute Cr. As the temperature increased from 800 °C to 900 °C, the thickness of the oxide layer grew from 23.2 μm to 77.8 μm. The formation of Cr₂O₃ nanophases within the oxide layer increased the alloy hardness from 87.3 HV to 101.8 HV and the electrical conductivity from 68.75% IACS to 78.85% IACS. Following subsequent vacuum solid solution and aging treatments, both hardness and electrical conductivity were significantly enhanced, reaching an edge hardness of 114.6 HV, a core hardness of 125.1 HV, and a conductivity of 80.32% IACS. Through this method, a layered composite structure of the Cu-0.97Cr-0.11Zr alloy, characterized by a highly conductive surface and a high-hardness core, can be successfully obtained.