Numerical Simulation and Parameter Optimization of Shot Peening Residual Stress Field for 9310 Steel Spiral Bevel Gears

Shot peening is a critical surface enhancement process for spiral bevel gears, inducing compressive residual stresses to improve fatigue life. This study establishes a coupled discrete element method (DEM) and finite element method (FEM) model to predict residual stress distributions in AISI 9310 steel spiral bevel gears. Experimental validation confirms the model’s accuracy with errors below 10%, enabling systematic exploration of shot peening parameter effects.

Fundamental Mechanisms and Modeling Approach

The residual stress generation mechanism follows the relationship:

$$ \sigma_{\text{res}} = f(E, \varepsilon_p, \dot{\varepsilon}, T) $$

where \( E \) represents Young’s modulus, \( \varepsilon_p \) plastic strain, \( \dot{\varepsilon} \) strain rate, and \( T \) temperature. The Johnson-Cook constitutive model describes material behavior:

$$ \sigma = (A + B\varepsilon^n)\left(1 + C\ln\frac{\dot{\varepsilon}}{\dot{\varepsilon}_0}\right)\left(1 – (T^*)^m\right) $$

with material constants for 9310 steel: \( A = 1234.38 \, \text{MPa} \), \( B = 881 \), \( n = 0.238 \), \( C = 0.018 \), \( m = 0.686 \).

Coupled DEM-FEM Simulation Framework

The integrated modeling approach consists of three phases:

Phase Component Key Parameters
DEM Particle dynamics Shot velocity: 30–50 m/s
Diameter: 0.18–0.42 mm
Coverage: 98–200%
Data Mapping Impact statistics Velocity vectors
Impact density: 360–720/mm²
FEM Stress analysis Element size: 10 μm
Infinite elements: CIN3D8
Damping: \( \alpha = 6 \times 10^6 \, \text{s}^{-1} \)

Experimental Validation

Residual stress measurements at critical positions demonstrate strong agreement between simulation and X-ray diffraction results:

Position σx Surface (MPa) σy Surface (MPa) Error
Convex (b) -824.2 -805.3 2.3%
Concave (b) -837.9 -849.7 1.4%

Parameter Sensitivity Analysis

The spiral bevel gear’s residual stress profile shows distinct responses to process parameters:

1. Peening Duration Effects

$$ N_{\text{imp}} = \frac{\dot{m}t}{\frac{4}{3}\pi r^3\rho} $$

where \( \dot{m} \) = mass flow rate (5 kg/min), \( r \) = shot radius. At 200% coverage:

Depth (μm) 72 s 144 s Δ Stress
Surface -798 -826 3.5%
20 -1045 -1128 8.0%
40 -672 -743 10.6%

2. Shot Velocity Impact

Velocity calculation from pneumatic parameters:

$$ v = \frac{163.5P}{1.53q_m + 10P} + \frac{295P}{0.598d + P} + 48.3P $$

Increasing velocity from 30 to 50 m/s enhances subsurface compression:

Velocity (m/s) σmax (MPa) Depth (μm)
30 -1104.7 20
40 -1144.9 25
50 -1167.3 30

3. Shot Diameter Optimization

Larger shots increase penetration depth while maintaining surface finish:

Diameter (mm) Surface Sa (μm) σmax (MPa) Depth (μm)
0.18 0.32 -893.6 10
0.30 0.39 -1145.0 30
0.42 0.47 -1251.5 40

Process Optimization Guidelines

For spiral bevel gears requiring >107 cycle fatigue life:

$$ \text{Optimal Parameters} = \begin{cases}
v \geq 40 \, \text{m/s} \\
d = 0.30 \, \text{mm} \\
t \geq 120 \, \text{s}
\end{cases} $$

This combination achieves surface compression >-800 MPa with 30–40 μm effective depth, balancing processing efficiency and surface integrity.

Industrial Implementation

The validated model enables predictive process design for spiral bevel gears without iterative trials. Key implementation steps:

  1. Import gear CAD model
  2. Define material properties
  3. Set DEM boundary conditions
  4. Run coupled simulation
  5. Extract residual stress tensor
  6. Verify against fatigue requirements

Future development will integrate machine learning for real-time parameter adjustment during spiral bevel gear peening operations.

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