In the field of railway vehicle manufacturing, the bogie serves as a critical running gear that supports, guides, and brakes the vehicle, directly influencing stability, comfort, and safety. Steel castings are widely employed in bogie components due to their design flexibility, high structural integrity, good weldability, and low crack susceptibility. However, defects often occur during the casting process or in service, and repairing these defects through welding is a cost-effective alternative to scrapping, especially for steel castings where dimensional tolerances allow. Welding repair, however, introduces residual stresses, particularly tensile stresses, which can combine with operational stresses to initiate fatigue cracks, potentially reducing the service life of steel casting components. Therefore, understanding the residual stress and fatigue performance of welded joints in steel castings is essential for optimizing repair processes and ensuring long-term reliability in bogie applications.
This study investigates the residual stress and fatigue behavior of welded joints in E260-450-MS steel castings, which are commonly used in bogie frames. Three different pre- and post-weld treatment processes were applied to simulate repair scenarios, and their effects on residual stress distribution and fatigue performance were evaluated. The objective is to identify the optimal process that minimizes residual stress and maximizes fatigue life for steel castings, thereby enhancing the durability of bogie systems. Through detailed experimentation and analysis, this research provides insights into the interplay between welding parameters, residual stresses, and fatigue failure mechanisms in steel castings.
The base material used in this study is E260-450-MS steel casting, which complies with the UIC-80-2O—1981 standard for railway vehicle cast steel components. The welding wire employed for repair is A-G46 4M21 4Si1 (referred to as “4Si1”). The chemical composition and mechanical properties of both the steel casting base metal and the welding wire are summarized in Tables 1 and 2, respectively. These properties are critical as they influence the weldability and performance of the repaired steel casting joints.
| Material | C | Si | Mn | P | S | Cr | Cu | Ni | V |
|---|---|---|---|---|---|---|---|---|---|
| E260-450-MS | ≤0.25 | ≤0.50 | ≤1.00 | ≤0.040 | ≤0.040 | ≤0.25 | — | ≤0.35 | — |
| 4Si1 | 0.06–0.14 | 0.80–1.20 | 1.60–1.90 | ≤0.025 | ≤0.025 | ≤0.15 | ≤0.15 | ≤0.15 | ≤0.03 |
| Material | Yield Strength (MPa) | Tensile Strength (MPa) | Elongation A (%) | Impact Test Temperature (°C) | Impact Energy (J) |
|---|---|---|---|---|---|
| E260-450-MS | ≥260 | ≥450 | 20 | 20 | ≥25 |
| 4Si1 | ≥460 | 530–680 | 20 | -40 | ≥47 |
To simulate the repair of large cracks in steel castings, welding was performed on plates machined from E260-450-MS steel casting with dimensions of 500 mm × 150 mm × 12 mm. A V-groove with a single bevel angle of 30° and a 2 mm root face was prepared, resembling a butt-welding configuration. The welding method used was MAG (Metal Active Gas) welding, which is commonly applied in repair operations for steel castings due to its efficiency and control. A SAF OPTIPULS 500IW welding machine was employed, with a shielding gas mixture of argon-rich gas at a flow rate of 18 L/min. The welding parameters, including current, voltage, welding speed, and heat input, are detailed in Table 3. The interpass temperature was controlled to be below 180°C to prevent excessive heat buildup, which could adversely affect the steel casting microstructure.
| Weld Pass Order | Weld Layer | Current Type | Welding Current (A) | Welding Voltage (V) | Welding Speed (mm/s) | Heat Input (J/mm) |
|---|---|---|---|---|---|---|
| 1 | Root | DCEP | 155–170 | 17–19 | 3–4 | 527–861 |
| 2 | Fill | DCEP | 270–300 | 28–31 | 7–9 | 672–1063 |
| 3 | Fill | DCEP | 280–310 | 29–32 | 4–5 | 1299–1984 |
| 4 | Cap | DCEP | 280–310 | 29–32 | 4–5 | 1299–1984 |
Three distinct treatment processes were applied to the welded joints of the steel castings, as outlined in Table 4. These processes vary in pre-heating and post-weld cooling methods to assess their impact on residual stress and fatigue performance. Process E1 involved pre-heating at 120–150°C followed by natural cooling and subsequent stress relief heat treatment. Process E2 included pre-heating at 120–150°C with natural cooling but no post-weld heat treatment. Process E3 had no pre-heating and utilized insulated slow cooling after welding without heat treatment. The heat treatment for E1 consisted of heating the steel casting to 590±15°C at a rate not exceeding 150°C/h, holding for 3 hours, and cooling at a controlled rate of ≤120°C/h to below 200°C. This variety in processes allows for a comprehensive comparison of how different thermal histories affect the properties of welded steel castings.
| Welded Sample ID | Welding Method | Pre-heat Condition | Post-weld Treatment |
|---|---|---|---|
| E1 | MAG | 120–150°C | Natural cooling + stress relief heat treatment |
| E2 | MAG | 120–150°C | Natural cooling + no heat treatment |
| E3 | MAG | No pre-heat | Insulated slow cooling + no heat treatment |
Residual stress measurement was conducted using X-ray diffraction with a portable μ-X360n automated system. This method is non-destructive and well-suited for assessing stresses in steel castings. A Cr target was used with a collimator diameter of 1 mm, operating at 30 kV·mA, and calibrated with zero-stress iron powder. Prior to testing, each measurement point on the steel casting samples was electrolytically polished using a MIR-EPLAB-01 electrolytic polishing corroder with a saturated NaCl solution to remove surface layers and ensure accurate readings. The measurement points were strategically located at distances from the weld center: 0 mm, ±5 mm, ±10 mm, ±20 mm, ±30 mm, ±45 mm, ±75 mm, and ±125 mm, covering the weld zone, heat-affected zone, and base metal of the steel casting. This setup enabled the mapping of residual stress distributions longitudinal (σ_x, parallel to the weld) and transverse (σ_y, perpendicular to the weld) to the welding direction.
Fatigue performance was evaluated through axial tensile fatigue tests in accordance with GB/T 3075-2021, which outlines methods for metal fatigue testing under axial force control. A QBG-250G high-frequency fatigue testing machine was used, operating at room temperature with sinusoidal loading, a stress ratio R=0, and a frequency range of 90–100 Hz. The fatigue limit was defined as the stress level at which specimens endured 10^7 cycles without failure. The fatigue specimens were machined from the welded steel casting plates, with a reduced cross-section to concentrate stress in the gauge area. The dimensions of the specimens were designed to standardize the tests and ensure reproducibility. The fatigue life data were analyzed using S-N curves, with double logarithmic linear fitting to derive fatigue life equations and determine median fatigue limits for each steel casting process.
The formation of residual stresses in welded steel castings can be modeled using thermal stress theory. During welding, localized heating causes non-uniform expansion, leading to plastic deformation. Upon cooling, constraint from surrounding material results in residual stresses. The longitudinal residual stress σ_x and transverse residual stress σ_y can be expressed as functions of position x from the weld center. A simplified empirical model for the distribution is given by:
$$ \sigma_x(x) = \sigma_m \exp\left(-\frac{x^2}{2c^2}\right) $$
where σ_m is the peak stress and c is a characteristic width parameter. Similarly, for transverse stress, a similar distribution may apply, but it is often more complex due to multidimensional effects. The heat input during welding also influences the stress magnitude, as higher heat input can increase the extent of the plastic zone. The total strain ε_total in the steel casting during welding comprises elastic, plastic, and thermal components:
$$ \epsilon_{\text{total}} = \epsilon_e + \epsilon_p + \alpha \Delta T $$
where ε_e is elastic strain, ε_p is plastic strain, α is the coefficient of thermal expansion, and ΔT is the temperature change. Upon cooling, the elastic strain recovery leads to residual stresses, which can be critical for fatigue performance in steel castings.
Fatigue life prediction often relies on the Basquin equation, which relates stress amplitude S to the number of cycles to failure N:
$$ S = A N^B $$
where A and B are material constants. In logarithmic form, this becomes:
$$ \lg S = \lg A + B \lg N $$
For the welded steel casting joints, the experimental data were fitted to this form to derive the fatigue life equations. The stress concentration factor K_t due to weld geometry or defects like micro-shrinkage porosity in steel castings can accelerate fatigue crack initiation. The modified stress amplitude S_a considering K_t is:
$$ S_a = K_t S $$
This emphasizes the importance of defect control in steel castings to enhance fatigue resistance.
Steel casting involves intricate processes of melting, molding, and solidification, where defects such as shrinkage porosity can form due to insufficient feeding during solidification. These defects act as stress concentrators and are particularly detrimental under cyclic loading.
The image illustrates typical equipment used in steel casting production, highlighting the complexity of manufacturing high-integrity components for bogies. Understanding these processes is essential for optimizing welding repairs and minimizing defects in steel castings.
The residual stress measurements for the three steel casting processes revealed distinct distributions and magnitudes. The longitudinal and transverse residual stresses are summarized in Table 5, which shows the peak values observed in the weld and base metal regions. For all processes, the residual stress distribution followed a similar trend: tensile stresses dominated near the weld center, decreasing to compressive stresses in the base metal area of the steel casting. This pattern is consistent with welding-induced thermal gradients and constraint effects.
| Sample ID | Longitudinal Residual Stress Peak σ_x (MPa) | Transverse Residual Stress Peak σ_y (MPa) | Location of Peak Stress |
|---|---|---|---|
| E1 | 95 | -87 | Weld center and base metal |
| E2 | 209 | 91 | Weld center |
| E3 | 309 | 187 | Weld center |
Process E1, which included post-weld heat treatment, exhibited the lowest residual stress levels, with longitudinal peaks below 100 MPa and transverse stresses primarily compressive. This reduction is attributed to stress relief during heat treatment, which allows for recovery and redistribution of stresses in the steel casting. In contrast, processes E2 and E3, without heat treatment, showed significantly higher tensile stresses, with E3 having the highest peaks due to the absence of pre-heating and faster cooling rates. The longitudinal residual stress for E3 reached 309 MPa, indicating that insufficient thermal management can lead to severe stress concentrations in steel castings. The transverse stresses also followed this trend, with E3 showing a peak of 187 MPa, compared to 91 MPa for E2. These results underscore the effectiveness of pre-heating in reducing welding residual stresses for steel castings, as it minimizes thermal gradients and mitigates plastic strain accumulation.
The fatigue test results for the steel casting welded joints are presented in the form of S-N curves, with the double logarithmic linear fitting equations provided in Table 6. The median fatigue limits σ_0 at R=0 were determined from these curves, representing the stress amplitude for 10^7 cycles. Process E2 achieved the highest fatigue limit of 204.46 MPa, followed by E3 at 173.94 MPa and E1 at 164.17 MPa. The ratio of fatigue limit to tensile strength (σ_0 / R_m) was also calculated, with E2 showing the best performance at 0.409, indicating superior fatigue resistance relative to its strength in the steel casting.
| Sample ID | Stress Ratio R | Linear Fitting Equation lgS-lgN | Median Fatigue Limit σ_0 (MPa) | Average Tensile Strength R_m (MPa) | σ_0 / R_m |
|---|---|---|---|---|---|
| E1 | 0 | lg S = 2.9958 – 0.1115 lg N | 164.17 | 475.7 | 0.345 |
| E2 | 0 | lg S = 2.8461 – 0.0765 lg N | 204.46 | 500.3 | 0.409 |
| E3 | 0 | lg S = 2.9142 – 0.0964 lg N | 173.94 | 469.7 | 0.370 |
Fractographic analysis of the fatigue specimens revealed that all failures originated in the base metal of the steel casting, specifically at near-surface locations. The fatigue sources were identified as micro-shrinkage porosity, a common casting defect in steel castings that acts as a stress concentrator. Under cyclic loading, these defects initiate cracks that propagate through the material, leading to fracture. The fatigue crack growth regions exhibited characteristic fatigue striations, and the final fracture zones showed ductile dimple patterns, consistent with overload failure. This highlights the critical role of casting quality in determining the fatigue performance of welded steel castings, as even optimized welding processes cannot fully compensate for inherent material defects.
The discussion of results focuses on the interplay between residual stress, fatigue performance, and process parameters for steel castings. Process E2, with pre-heating and natural cooling without heat treatment, demonstrated the best overall performance, combining moderate residual stresses and the highest fatigue limit. This suggests that pre-heating at 120–150°C effectively reduces thermal gradients and residual stresses in steel castings, while avoiding post-weld heat treatment prevents potential microstructural changes that could embrittle the material. In contrast, process E1, despite having the lowest residual stresses, showed reduced fatigue limits, possibly due to microstructural alterations from heat treatment or the presence of casting defects. Process E3, without pre-heating, resulted in the highest residual stresses and intermediate fatigue performance, indicating that insulated slow cooling alone is insufficient to compensate for the lack of pre-heating in steel castings.
The fatigue life equations derived from the S-N data can be used to predict the service life of steel casting components under cyclic loading. For instance, the equation for E2, $$ \lg S = 2.8461 – 0.0765 \lg N $$, allows for the estimation of allowable stress levels for a given number of cycles. Integrating this with residual stress data, the effective stress range Δσ_eff considering residual stress σ_res can be expressed as:
$$ \Delta \sigma_{\text{eff}} = \Delta \sigma + \sigma_{\text{res}} $$
where Δσ is the applied stress range. This approach helps in assessing the combined effect of operational and residual stresses on fatigue life in steel castings. Moreover, the presence of micro-shrinkage porosity as fatigue initiators underscores the need for stringent quality control in steel casting production, such as improved feeding systems or non-destructive testing to detect and mitigate defects.
In conclusion, this study demonstrates that the welding repair process significantly influences the residual stress and fatigue performance of steel castings used in bogie applications. Among the three processes evaluated, pre-heating at 120–150°C followed by natural cooling without post-weld heat treatment (E2) yields the optimal balance, with lower residual stresses and superior fatigue resistance. This process is recommended for practical applications involving steel castings, as it enhances service life while maintaining cost-effectiveness. Future work could explore advanced welding techniques or real-time monitoring to further improve the reliability of steel casting repairs in railway components.