In the automotive industry, the clutch pressure plate is a critical component responsible for transmitting engine power to the transmission system. As a key part of the clutch assembly, its performance directly impacts driving safety and vehicle reliability. Typically manufactured from cast iron, specifically ductile cast iron, the pressure plate undergoes complex thermal and mechanical loads during operation. However, the casting process often introduces inhomogeneities due to uneven temperature fields, leading to non-uniform distribution of yield limits, elastic moduli, and thermal plasticity. These factors contribute to residual stresses that can compromise dimensional accuracy, service life, and overall quality of the casting. Therefore, investigating the formation and distribution of residual stresses in automotive clutch pressure plates is essential for optimizing casting processes and enhancing product performance.
This study employs finite element analysis via ProCAST software to simulate the filling and solidification processes of a ductile cast iron pressure plate casting. By analyzing temperature fields, stress evolution, and deformation patterns, we aim to characterize residual stress distributions and identify potential areas of failure. The insights gained will guide improvements in casting design and process parameters, ultimately boosting production efficiency and product quality. Throughout this work, the focus remains on ductile cast iron due to its widespread use in such applications, and the term ‘ductile cast iron’ will be frequently referenced to emphasize its relevance.
The numerical simulation begins with the establishment of a detailed 3D model of the pressure plate. The geometry resembles a circular disk with an outer diameter of 215.9 mm, an inner hole diameter of 116 mm, and six lugs—three larger and three smaller—arranged symmetrically. The average wall thickness is 10 mm, with maximum and minimum thicknesses of 11.9 mm at the lugs and 5.5 mm at the plate surface, respectively. The material is specified as ductile cast iron, with a chemical composition as shown in Table 1. The total mass of the casting is 2.06 kg, and the model is designed to replicate actual production conditions.
| Element | C | Si | Mn | Cr | Cu | Mg |
|---|---|---|---|---|---|---|
| Content (%) | 3.65 | 2.65 | 0.27 | 0.019 | 0.097 | 0.018 |
Mesh generation is a crucial step in finite element analysis, as mesh quality directly influences simulation accuracy. Using ProCAST’s Visual-Mesh module, the assembly—comprising the casting and gating system—is imported and checked for surface connectivity issues, overlaps, and intersections. After repairing any defects, surface meshing is performed with triangle elements, followed by volume meshing using tetrahedral elements. The mesh size is set to 3 mm for both the casting and gating system, balancing computational efficiency and precision. The final mesh consists of 157,390 surface elements, 3,225,544 volume elements, and 569,502 nodes. This refined mesh ensures reliable results for thermal and stress analyses.
Parameter setup involves defining material properties, boundary conditions, and process parameters. The casting material is ductile cast iron, while the mold is made of resin sand. Key parameters include a pouring temperature of 1419°C, pouring speed of 0.55 m/s, pouring time of 7.5 s, and ambient temperature of 25°C. The interface heat transfer coefficient between the casting and mold is set to 500 W/m²K, and cooling occurs via natural air convection. Gravity is applied in the negative y-direction with an acceleration of 9.8 m/s². The simulation runs for 50,000 steps, terminating when the temperature drops below 500°C. These settings are summarized in Table 2.
| Parameter | Value |
|---|---|
| Casting Material | Ductile Cast Iron |
| Mold Material | Resin Sand |
| Interface Heat Transfer Coefficient | 500 W/m²K |
| Pouring Temperature | 1419°C |
| Pouring Time | 7.5 s |
| Pouring Method | Gravity Pouring |
| Cooling Method | Natural Air Cooling |
| Mold Temperature | 25°C |
| Gravity Direction | Negative Y |
| Gravity Acceleration | 9.8 m/s² |
| Simulation Steps | 50,000 |
| Stopping Condition | Temperature < 500°C |
The temperature field during mold filling is analyzed to assess flow behavior and thermal gradients. Metal enters through the sprue, flows into runners and gates, and fills the cavity via the lugs. The filling completes in 7.35 s, with no signs of cold shuts or misruns. At 5.09 s, the lower part near the inner circle and small lugs cools to around 1300°C, while at the end of filling, the maximum temperature difference across the casting is approximately 124°C. All regions remain in the liquid state initially, but rapid cooling in thin sections creates significant thermal gradients. These gradients are critical for understanding residual stress formation in ductile cast iron.
To quantify thermal effects, the heat conduction equation governs temperature evolution:
$$ \rho c_p \frac{\partial T}{\partial t} = \nabla \cdot (k \nabla T) + Q $$
where \(\rho\) is density, \(c_p\) is specific heat, \(T\) is temperature, \(t\) is time, \(k\) is thermal conductivity, and \(Q\) represents internal heat sources. For ductile cast iron, these properties vary with temperature, influencing solidification patterns.
Solidification analysis reveals the sequence of phase change. The solid fraction distribution shows that solidification initiates at the inner and outer circles, progresses toward the central plate, and ends at the large lugs connected to risers. This pattern results from chilling effects at the mold-casting interface. Specific points on the casting, as shown in Figure 5 (not referenced explicitly), are selected to plot solid fraction versus time curves. Points near the inner circle solidify first, followed by the plate surface, and finally the large lugs, with a time difference of about 90 s between the first and last points. This sequential solidification contributes to stress buildup in ductile cast iron.
The residual stress field is evaluated using von Mises equivalent stress. Results indicate that the casting surface is predominantly under tensile stress, with higher values near the inner circle and large lugs—reaching up to 360 MPa. This elevated stress poses a cracking risk. The stress distribution is non-uniform, generally decreasing from the inner to outer regions. To analyze stress evolution, key points are monitored over time. The equivalent stress curve exhibits three stages: a rapid increase during initial cooling, a decrease due to secondary phase transformations, and a gradual rise as residual stresses accumulate after complete solidification. The stress evolution can be modeled using:
$$ \sigma = E \epsilon + \sigma_{th} $$
where \(\sigma\) is stress, \(E\) is the elastic modulus (temperature-dependent for ductile cast iron), \(\epsilon\) is strain, and \(\sigma_{th}\) is thermal stress from differential contraction.
Deformation analysis focuses on displacement in the z-direction (normal to the plate surface). The results show an “outer convex, inner concave” pattern, where the outer rim displaces outward and the inner rim displaces inward, reducing flatness accuracy. Table 3 lists displacement values for 14 points along a horizontal line, and the data is plotted to reveal symmetrical deformation. This distortion is directly linked to residual stresses in the ductile cast iron casting.
| Point Number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Displacement (10⁻³ cm) | -1.4 | -1.0 | -0.4 | 0.3 | 1.08 | 1.78 | 2.5 | 2.18 | 1.58 | 0.74 | 0.07 | -0.41 | -0.78 | -0.83 |
The deformation behavior can be described by the strain-displacement relationship:
$$ \epsilon_z = \frac{\partial w}{\partial z} $$
where \(\epsilon_z\) is normal strain in the z-direction and \(w\) is displacement. For ductile cast iron, the anisotropic cooling leads to complex strain fields.
Discussion of the results highlights the impact of process parameters on residual stresses. The pouring temperature of 1419°C and cooling rate create steep thermal gradients, which are inherent in ductile cast iron casting. The gating design, with filling through the lugs, promotes turbulence-free flow but also causes uneven solidification. The large lugs, acting as hot spots due to their connection to risers, solidify last, leading to high tensile stresses. These findings align with known challenges in ductile cast iron production, where controlling residual stress is vital for preventing cracks and ensuring dimensional stability.
To further understand stress formation, we can consider the yield criterion for ductile cast iron. The von Mises stress is given by:
$$ \sigma_{vm} = \sqrt{\frac{1}{2}[(\sigma_1 – \sigma_2)^2 + (\sigma_2 – \sigma_3)^2 + (\sigma_3 – \sigma_1)^2]} $$
where \(\sigma_1, \sigma_2, \sigma_3\) are principal stresses. High \(\sigma_{vm}\) values indicate regions prone to plastic deformation or cracking.
In terms of optimization, adjusting process parameters such as pouring temperature, cooling rate, or riser design could mitigate residual stresses. For instance, lowering the pouring temperature might reduce thermal gradients, but it must be balanced against fluidity requirements for ductile cast iron. Additionally, post-casting treatments like stress relief annealing could be evaluated through simulation.
The use of ProCAST software proves effective for predicting residual stresses in ductile cast iron components. By integrating thermal, fluid flow, and stress modules, the simulation provides a comprehensive view of casting behavior. Future work could involve experimental validation using techniques like X-ray diffraction for stress measurement, further refining the model for ductile cast iron applications.
In conclusion, this numerical study demonstrates that residual stresses in automotive clutch pressure plates made of ductile cast iron are significantly influenced by solidification sequences and cooling conditions. The stress distribution is non-uniform, with high tensile stresses near the inner circle and large lugs, posing crack risks. Deformation in the z-direction reduces flatness accuracy, affecting machining and performance. These insights underscore the importance of simulation-driven design for ductile cast iron castings, enabling process improvements that enhance product reliability and longevity. The repeated reference to ductile cast iron throughout this analysis emphasizes its critical role in automotive casting applications.