The rapid advancements in electronics, electric vehicles, and green technologies have imposed increasingly stringent demands on copper-based materials. These requirements include high thermal and electricity conductivity, corrosion resistance, and strength properties at both room temperature and high temperatures. Rare-earth elements are excellent microalloying agents due to their typical metallic properties and highly active chemical characteristics; these properties and characteristics enable them to react with almost all elements except noble gases. The addition of rare-earth elements to copper and copper alloys can have several beneficial effects, such as impurity removal, purification, enhancement of the metallographic structure, and improved corrosion resistance. These effects can also raise the heat treatment temperature and enhance plastic processing, thereby further improving the overall properties of copper alloys. This review examines the influence of rare-earth elements (REEs) on copper and its alloys, along with their diverse industrial applications. It was found that elements such as La, Ce, Y, and Nd are commonly added to enhance properties like electrical conductivity, strength, corrosion resistance, purity, and hot workability in alloys such as pure copper, Cu-Ni-Si, Cu-Cr-Zr, and Cu-Fe-P. The review will lay a foundation and provide novel method for the development of advanced copper alloy.

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.

Integral panels are widely used in aerospace industries. A filling roll bending process is proposed to form integral panels. Filling roll bending experiments of aluminum alloy integral panels were carried out. A 3D elastic–plastic finite element model of filling roll bending process was established and validated by experiment. The effects of filler and process parameters on the deformation homogeneity of the panels were analyzed by using experimental and numerical methods. The results indicate that the filler can improve the deformation homogeneity. With the increasing of the displacement of the top-roller from 5 mm to 40 mm, the experimental and simulation bending radii with filler all reduce, the experimental results reduce from 5806 mm to 190 mm, the simulation results reduce from 5924 mm to 199 mm, and the simulation springback rates with filler reduce from 0.92% to 0.15%. It is proved that high geometric accuracy of the integral panels can be obtained by using filling roll bending process.

The effects of Ti addition on the microstructure, mechanical properties and corrosion resistance of the Cu-30Zn-6Ni-1.5Ti (wt.%) alloy were investigated in the present study. Microstructure analysis confirms that the Ni₃Ti and NiTi phases are formed in the Cu-Zn-Ni-Ti alloy. Most of the Ni₃Ti particles dissolve into the matrix, whereas NiTi particles remained after the solution treatment. Moreover, nano-sized Ni₃Ti and Cu₂NiZn phases are precipitated from the matrix during the aging process. The yield strength improvement of the studied alloy is attributed to the Orowan strengthening (accounting for 42.5% of the total yield strength), then the grain boundary strengthening (31.1%), the dislocation strengthening (16.9%), the twin boundary strengthening (5.0%) and the solid solution strengthening (0.5%). After cold rolling with 90% deformation and aging at 440 °C for 1 h, the designed alloy has a hardness of 327.1 HV, a yield strength of 1192 MPa, an ultimate tensile strength of 1297 MPa, an elongation of 1.8%, an electrical conductivity of 11.2% IACS and a corrosion rate of 2.8 μm/month in 3.5% NaCl solution.

Nano Cu precipitation plays a crucial role in significantly improving the performance of the Cu-bearing high-strength low-alloy steel. The final cooling temperature effects the transformation products of austenite during the continuous cooling process, as well as the nano precipitations of steel. This study investigated the microstructure and hardness at different final cooling temperatures (750, 700, 650, 600, 550, and 500 °C) using the MMS-300 thermal simulation experimental machine (Northeastern University, Shenyang, China) and Vickers hardness tester. The changes in microstructure and the phase transformation law of austenite were determined during continuous cooling and then analyzed. The precipitation reaction of nano Cu precipitation during continuous cooling was studied using transmission electron microscopy (TEM), revealing the precipitation state under different final cooling temperature conditions. The results showed that the precipitations led to an increase and then a decrease in the microhardness, and the microhardness reaches its peak at 550 °C. The precipitations changed from spherical to elliptical, and the size gradually increased when the final cooling temperature increased.

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.