## 1 Introduction  
The TP2 copper tube is widely used in fields such as air conditioning, automotive, and aerospace due to its high thermal conductivity and corrosion resistance [1, 2]. Currently, the production of TP2 copper tubes primarily employs the casting-rolling process, which includes steps such as melting and casting, rolling, joint drawing, and coiling. The casting-rolling process flow for copper tubes is illustrated in Figure 1. As one of the steps in the production process, joint drawing is characterized by its efficiency, stability, and precision. Many scholars have conducted extensive research on tube drawing. [PERSON] et al. performed a finite element analysis on AZ31 magnesium alloy tubes and concluded that the optimal mold taper angle combination is 7.5\({}^{\circ}\) for the mandrel and 13\({}^{\circ}\) for the outer die. They also found that increasing the length of the outer die sizing section increases the drawing force [3]. [PERSON] et al. proposed using a finite element model to analyze the drawing process of tubes with initial wall thickness non-uniformity, addressing more practical production issues [4]. [PERSON] et al. developed an ultrasonic vibration-assisted drawing system that demonstrated that ultrasonic vibration improves the tensile properties of the tubes, helping to enhance the forming limit of the tubes [5]. [PERSON] et al. verified that applying forward and backward forces in the deformation zone can control the stress-strain and damage states of metals [6]. [PERSON] et al. used three methods--synchroftdiffraction, and electron backscatter diffraction--to study the macroscopic and microscopic structure evolution of the maximum and minimum wall thickness positions during the drawing of copper tubes [7]. [PERSON] et al. used the upper bound analysis method and energy method to study the stress changes in the floating mandrel drawing process and verified the accuracy of these methods in stress analysis through finite element validation [8]. [PERSON] et al. studied the influence law of copper pipe drawing speed, vibration parameters, and external die cone angle on drawing force [9]. [PERSON] et al. explored the influence of non-uniform wall thickness defects on drawing force, displacement of the floating core head, and stress and strain, and concluded that the drawing force decreased by 6% compared with 15% wall thickness non-uniformity and 0% wall thickness non-uniformity [10]. [PERSON] et al. studied the influence of different spinning parameters on the finished product during the forming process of 1070 thin-walled aluminum alloy and improved the uniformity of the wall thickness of the product by improving the diameter-to-thickness ratio of the billet and reasonable spinning parameters [11]. [PERSON] et al. discussed the influence of pipe outer diameter manufacturing deviation and wall thickness manufacturing deviation and found that pipe outer diameter manufacturing deviation had a small influence on pipeline reliability, while wall thickness manufacturing deviation had a relatively large influence on pipeline reliability [12]. [PERSON] et al. studied the influence of rotary wheel feed ratio and rotary wheel inlet radius on the wall thickness deviation of shear spinning conical parts. When the feed ratio is small, the wall thickness deviation will be affected, the deformation resistance of different sections will have a greater difference, and the material flow per unit time will increase the surface roughness [13]. All the above research on joint drawing is reflected in the influence of die size on drawing force, friction force, stress and temperature field, etc. There is still a lack of research on the non-uniform wall thickness defect of copper pipe. The neural network optimization algorithm plays a crucial role in the modern deep learning field, from the basic gradient descent algorithm to the advanced adaptive optimization algorithm and then to the new swarm intelligence optimization method.