Construction machinery plays a vital role in infrastructure development, mining operations, and military engineering. Hydraulic excavators represent the pinnacle of engineering in this field, where operational efficiency and fuel consumption are critical performance metrics that define competitive advantage. Since most energy consumption during excavation relates to overcoming digging resistance, optimizing the bucket tooth—the primary soil-engaging component—becomes essential for performance enhancement. This study presents an integrated approach combining multi-body dynamics, discrete element modeling, and orthogonal experimental design to optimize bucket tooth geometry.
Discrete Element Simulation Framework
The Discrete Element Method (DEM) models granular materials as independent particles governed by Newtonian mechanics. We utilized EDEM software with the Hertz-Mindlin contact model with JKR V2 cohesion to simulate soil behavior. Calibration involved physical tests including angle of repose, slump, and shear tests to match real-world soil properties. Key parameters are summarized below:
| Material | Poisson’s Ratio | Shear Modulus (Pa) | Density (kg/m³) |
|---|---|---|---|
| Soil Particles | 0.35 | 1×10⁷ | 3,100 |
| Steel (Geometry) | 0.30 | 7×10¹⁰ | 7,800 |
| Contact Pair | Restitution Coefficient | Static Friction | Rolling Friction |
|---|---|---|---|
| Particle-Particle | 0.8 | 1.2 | 0.06 |
| Particle-Steel | 0.8 | 1.2 | 0.20 |
Integrated Multi-Body Dynamics and DEM Simulation
RecurDyn software modeled the excavator’s kinematic chain, including boom, arm, and bucket hydraulic actuators. The joint simulation coupled RecurDyn’s motion solver with EDEM’s particle dynamics using co-simulation interfaces. This enabled real-time interaction between the excavator and soil during digging cycles. Validation against field data showed cylinder pressure deviations under 25%, confirming model accuracy:
Hydraulic force error metrics:
$$E_{\text{arm}} = \max \left| \frac{P_{\text{sim}} – P_{\text{real}}}{P_{\text{real}}} \right| \times 100\% \leq 17.3\%$$
$$E_{\text{boom}} \leq 21.7\% , \quad E_{\text{bucket}} \leq 25.0\%$$
Simulation outputs quantified performance through:
– Bucket fill mass \(M_f\) (kg): Excavated material per cycle
– Energy consumption \(E_c\) (J): Total digging energy
– Specific energy \(E_s\) (J/kg):
$$E_s = \frac{E_c}{M_f}$$
Orthogonal Test Optimization Methodology
Nine geometric parameters define the bucket tooth design. A Plackett-Burman screening test identified four dominant factors influencing digging performance:
| Factor | Symbol | Influence Rank |
|---|---|---|
| Lower Angle | R₂ | 1 |
| Tip Width | R₃ | 2 |
| Tip Height | R₄ | 3 |
| Groove Angle | θ | 4 |
An L9(3⁴) orthogonal array tested factor-level combinations. Response trends revealed optimal design principles:
Performance sensitivity to key factors:
$$\begin{aligned}
&\frac{\partial E_s}{\partial R_4} < 0,\quad \frac{\partial M_f}{\partial R_4} > 0 \\
&\frac{\partial E_s}{\partial R_3} \: \text{non-monotonic} \\
&\frac{\partial E_s}{\partial R_2} < 0,\quad \frac{\partial M_f}{\partial R_2} > 0 \\
&\frac{\partial E_s}{\partial \theta} < 0,\quad \frac{\partial M_f}{\partial \theta} < 0
\end{aligned}$$
| Factor | Original | Optimized |
|---|---|---|
| Lower Angle R₂ (°) | 12 | 12 |
| Tip Width R₃ (mm) | 101 | 110 |
| Tip Height R₄ (mm) | 8 | 4 |
| Groove Angle θ (°) | 1 | 0 |
Experimental Validation
The optimized bucket tooth was manufactured using metal 3D printing—a pioneering application in construction machinery. Field tests under standardized conditions measured efficiency (m³/hr) and fuel consumption (L/hr) across operational gears:
| Gear | Efficiency Gain | Fuel Consumption Change |
|---|---|---|
| Gear 6 | +3.8% | +0.1% |
| Gear 7 | +4.9% | -0.2% |
| Gear 10 | +4.2% | -0.2% |
Results demonstrated that the optimized bucket tooth increased productivity while maintaining or reducing fuel usage. Specific energy decreased by 2.45% in simulations, aligning with field observations.
Conclusions
The joint DEM-MBD framework accurately simulates soil-tool interactions, enabling data-driven bucket tooth optimization. Orthogonal testing efficiently identified critical geometric factors: tip height reduction lowered specific energy, while increased tip width improved fill capacity. Field validation confirmed that the optimized bucket tooth enhanced operational efficiency by up to 4.9% without compromising fuel economy. This methodology establishes a replicable workflow for earthmoving equipment performance enhancement, with metal 3D printing enabling rapid design iteration.