## 4 Conclusions and Discussion  
The multi-cell elliptical cavity pre-tuning machine developed in this study represents a significant advancement in superconducting cavity frequency tuning technology. By integrating an automatic measurement system, iterative tuning algorithms, close-loop feedback control, and an automated pre-tuning device, the system achieves high-precision adjustment of cavity frequency and electric field flatness. Compared to traditional manual tuning methods, the automated pre-tuning machine significantly reduces tuning time, provides accurate tuning parameters, enhances process stability, and ensures reliable operation of cavities under high acceleration gradients. Experimental results show that the system improves the field flatness of copper test cavities from 2% to 96.5% and stabilizes the resonant frequency to 648 MHz. By incorporating the bead-pull system, laser sensors for coaxiality monitoring, and motor-driven fixtures for cavity deformation measurement, the pre-tuning machine can automatically perform high-frequency, coaxiality testing, adjustment, and tuning of the cavity as an integrated process. It optimizes the tuning algorithm, reduces manual intervention, addresses the tuning challenges of large structures, and significantly improves efficiency and consistency. The experimental data further validates the theoretical framework proposed in this study, particularly the frequency shifts observed during cavity compression and stretching, which align closely with the predictions of the equivalent circuit model. Additionally, the coupling effect between cells was confirmed, as stretching Cell-1 caused a 0.03 MHz frequency shift in Cell-2, which is consistent with the theoretical coupling coefficient analysis. These experimental findings not only establish the high precision of the system but also demonstrate its adaptability to complex superconducting cavity systems. Despite the system's exceptional tuning performance and enhanced automation, several challenges were observed during the experiments. First, the high yield strength of copper cavities (207 MPa) requires strong forces for shaping, making precise control difficult. Higher-strength fixture designs are needed to provide the strong forces needed for adjustment. Secondly, a nonlinear relationship between displacement and frequency was observed, especially during large physical adjustments. Adaptive control algorithms will help to optimize the adjustment process. Additionally, stepper motor movements are problematic at high speeds, while sensors gradually lose their accuracy when operating fora long time. Future work should prioritize sub-micron motor control precision and sensor calibration protocols resistant to thermal drift. The results of this study show that the system works in laboratory settings and can be used in large-scale accelerator projects. This system will be used in the CSNS-II to optimize elliptical superconducting cavities for stable 500 kW beam operation. Its modular design allows adaptation to other systems requiring precise field control, including medical proton therapy devices. Next-stage research will explore machine learning algorithms to optimize adjustment paths and reduce tuning cycles. Standardized adjustment tools should also be developed for different cavity types, such as the 1.3 GHz Tesla-style cavities and medical proton therapy spherical cavities, to increase system flexibility. The pre-tuning machine developed in this study provides an efficient, stable, and scalable automated solution for optimizing the performance of superconducting cavities. With the ability to achieve electric field flatness of more than 95%, the system can enhance cavity tuning accuracy and also provide robust technical support for future applications. With further algorithmic refinements and cost optimization, this system holds promise for applications in accelerator physics and compact medical proton therapy systems. Conceptualization, A.L. and L.M. ; methodology, A.L. and H.L. ; software, A.L. and Q.C. ; validation, A.L.