## 6 Conclusions  
The modeling of deep rolling in ANSYS software has been used to create significant models for railway rails. To optimize the residual stress distribution in a railway rail, we simulate the deep rolling process and predict the residual stress profile, then compare and analyze the FEM simulation results with experimental data. This comparison helps fine-tune the deep rolling parameters to achieve optimal results. The results unequivocally demonstrate that applying a deep rolling pressure of 150 bar (experimental) and 157 bar (FEA) induces substantial compressive residual stresses, particularly beneath the rolling tool. This study investigated the impact of deep rolling parameters such as pressure, speed, and offset on the residual stress distribution and material deformation of UIC 860 Grade 900A railway rails using both experimental and FEA methods. Our goal was to optimize the deep rolling process for improved surface integrity and fatigue resistance. The results showed that the deep rolling pressure of 150 bar (experimentally) and 157 bar FEA induced significant compressive residual stresses, especially under the rolling tool. The experimental measurements produced an average compressive residual stress of 498.9 MPa, compared to the FEA-predicted mean value of 502.5 MPa. A paired \(t\)-test was performed to compare these values, yielding a T-value of \(-\)0.22 and a \(p\)-value of 0.833. Since the \(p\)-value is greater than 0.05, the difference between the experimental mean and the FEA-predicted value is not statistically significant, indicating that the FEA model provides a reliable estimate of the residual stress profile. The deep rolling speed of 1800 mm/min (experimentally) and 1796.52 mm/min (FEA) produced consistent stress patterns, though periodic fluctuations in pressure application were observed in the time-based analysis. The 0.1 mm offset applied in both the experimental and FEA setups ensured uniform deformation but increased the number of tool passes required to cover the entire surface, slightly extending the processing time. Despite the strong agreement between the FEA predictions and experimental results, certain limitations were identified. The simplifications in the FEA, such as the assumption of homogeneous material properties, may have introduced minor discrepancies, particularly in deeper material layers. Additionally, the \(\cos\alpha\) technique used for residual stress measurement was limited to surface-level analysis, restricting our understanding of deeper stress profiles. Future research should incorporate more advanced material models and complementary stress measurement techniques, such as neutron diffraction, to gain a more complete understanding of residual stress distributions. In conclusion, this study confirms that the optimization of the deep rolling parameters of pressure, speed, and offset leads to favorable residual stress distributions and improved material properties. The results of the paired \(t\)-test demonstrate a close alignment between FEA-predicted and experimentally measured residual stresses, further validating the FEA model as a reliable tool for predicting stress outcomes. By addressing the limitations and incorporating more advanced measurement techniques, future studies can further refine the deep rolling process for enhanced material performance, particularly in applications such as railway rail treatments. Conceptualization, S.P. and W.N. ; methodology, S.P., P.I., W.N. and A.B. ; validation, S.P. and W.N. ; formal analysis, S.P. ; investigation, P.I. ; resources, W.N. and A.B. ; data curation, S.P. ; writing--original draft preparation, S.P. and P.P. ; writing--review and editing, S.P. and W.N. ; visualization, S.P. and W.N. ; supervision, W.N. and A.B. ; project administration, S.P. ; funding acquisition, W.N. All authors have read and agreed to the published version of the manuscript. This research was partially supported by Chiang Mai University, Thailand. Not applicable.