Deep Rolling Process Modeling Using Finite Element Analysis in Residual Stress Measurement on Rail Head UIC860 Surface

Autor: Siwasit Pitjamit, Wasawat Nakkiew, Pinmanee Insua, Adirek Baisukhan, Pattarawadee Poolperm
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Applied Sciences, Vol 14, Iss 23, p 11222 (2024)
Druh dokumentu: article
ISSN: 2076-3417
DOI: 10.3390/app142311222
Popis: This study investigates the effects of deep rolling parameters, pressure, speed, and offset, on the residual stress distribution and material deformation in UIC 860 Grade 900A railway rails. We will model deep rolling to simulate the process and predict the residual stress profile in railway rails. Subsequently, we will rigorously compare and analyze the FEM simulation results with experimental data to optimize deep rolling parameters for improved residual stress distribution. Using both experimental methods and finite element analysis via ANSYS 2023 R1, the study varied deep rolling parameters. Experimental deep rolling pressure was set at 150 bar, speed at 1800 mm/min, and offset at 0.1 mm, while FEA simulations predicted corresponding pressures of 157 bar and speed of 1796.52 mm/min. These parameter settings were chosen to induce significant surface compressive stresses that could enhance the material’s mechanical performance. The experimental results showed an average compressive residual stress of 498.9 MPa, closely aligning with the FEA-predicted value of 502.5 MPa. A paired t-test revealed no statistically significant difference between the two results, with a T-value of −0.22 and a p-value of 0.833, validating the reliability of the FEA model. The consistent deformation observed in both experimental and FEA simulations, especially with a 0.1 mm offset, confirmed that the rolling parameters were effective in producing uniform stress distribution, albeit with a slightly extended processing time due to the small offset. Overall, the findings confirm that optimizing the deep rolling parameters of pressure, speed, and offset leads to favorable residual stress distributions and improved material properties. The results indicate that FEA is a reliable tool for predicting the outcomes of deep rolling, and this study provides a strong foundation for further refinement of the process to enhance performance in practical applications, such as railway rail treatments.
Databáze: Directory of Open Access Journals