In the field of internal combustion engine design, the crankshaft serves as a critical component for transmitting torque and withstanding cyclic loads. With increasing demands for higher power density and efficiency, materials like QT800-6 ductile iron casting have emerged as innovative solutions. This material, a type of ductile iron casting, offers a unique combination of high tensile strength and considerable elongation, which is not typically found in conventional ductile iron grades. When paired with strengthening processes such as journal quenching and fillet rolling, QT800-6 ductile iron crankshafts can achieve bending fatigue performance close to that of forged steel crankshafts. However, as engine combustion pressures rise, engineering specifications mandate that these crankshafts must meet a safety factor of at least 1.8. Given the limitations of CAE analysis software—which often lacks material parameters for QT800-6 ductile iron casting and the effects of rolling processes—engineers require rapid calculation methods to preliminarily assess safety factors before detailed simulations. This need is particularly acute in industries where software licenses are costly or computational resources are limited for numerous components.
In my experience working with ductile iron casting applications, I have observed that the geometric shape coefficient of a crankshaft directly correlates with its strength. By leveraging this relationship, one can quickly estimate the safety factor without extensive CAE modeling. This approach is especially valuable for QT800-6 ductile iron casting, as it allows for early design validation and optimization. In this article, I will explore traditional calculation methods, compare their results with experimental data from over 40 crankshafts, and establish a reliable model for rapid safety factor assessment. The focus will be on how ductile iron casting properties interact with geometric parameters to influence fatigue life.
The importance of ductile iron casting in crankshaft manufacturing cannot be overstated. QT800-6, as a specific grade, exhibits a microstructure of spheroidal graphite embedded in a ferritic-pearlitic matrix, contributing to its mechanical properties. However, the fatigue strength of ductile iron casting components depends not only on material composition but also on geometric features like fillet radii, web thickness, and overlap. This interplay necessitates accurate shape factor calculations to predict performance. Over the years, several empirical methods have been developed for crankshaft design, each derived from specific structural configurations and testing conditions. For ductile iron casting crankshafts, selecting an appropriate method is crucial, as discrepancies can lead to over- or under-design, impacting cost and reliability.
Calculation Methodologies for Crankshaft Shape Coefficient
In my investigation, I considered five widely recognized calculation methods for determining the shape coefficient of crankshafts. These methods, originally formulated for various materials including steel, have been adapted for ductile iron casting applications. They rely on dimensionless parameters derived from key crankshaft dimensions: the crankpin diameter \(D\), fillet radius \(R\), web thickness \(H\), web width \(B\), and overlap \(S\) at the critical cross-section. The parameters are defined as follows:
- \( r = \frac{R}{D} \)
- \( h = \frac{H}{D} \)
- \( b = \frac{B}{D} \)
- \( s = \frac{S}{D} \)
These parameters encapsulate the geometric influence on stress concentration, which is pivotal for fatigue analysis in ductile iron casting components. Below, I present the formulas for each method, expressed in LaTeX format for clarity.
1. Stahl Method
The shape coefficient according to Stahl is given by:
$$ \text{Shape Coefficient} = 13.6 \cdot f(s) \cdot f(b) \cdot f(h) \cdot f(r) $$
where:
$$ f(s) = 1 – 0.259s – 1.98s^2 $$
$$ f(b) = 1.262 – 0.158b $$
$$ f(h) = 1.99 – 3.3h $$
$$ f(r) = 0.26r^{-0.45} $$
2. Iki-Takataki Method
This method uses the formula:
$$ \text{Shape Coefficient} = 5.0 \cdot f(l) \cdot f(s) \cdot f(b) \cdot f(h) \cdot f(r) $$
with:
$$ f(l) = 2.02 – 1.55l \quad \text{(where \( l \) is a length parameter, often derived from crank geometry)} $$
$$ f(s) = 1.02 – 0.22s – 0.47s^2 $$
$$ f(b) = 1.5 – 0.38b $$
$$ f(h) = 3.30 – 4.2h $$
$$ f(r) = 0.17 r^{-0.67} $$
3. Daimler-Benz (DB) Method
The DB method calculates shape coefficient as:
$$ \text{Shape Coefficient} = 11.85 \cdot f(s) \cdot f(b) \cdot f(h) \cdot f(r) $$
where:
$$ f(s) = 0.938 – 0.615s – 0.928s^2 + 0.867s^3 $$
$$ f(b) = 1.675 – 0.645b + 0.130b^2 $$
$$ f(h) = 3 \times 0.33h $$
$$ f(r) = 0.26 r^{-0.45} $$
4. Arai Junichi Method
Arai’s approach is more complex, accounting for additional factors:
$$ \text{Shape Coefficient} = 4.84 \cdot f(s, h) \cdot f(b) \cdot f(r) \cdot f(h) \cdot f(r, s) $$
with:
$$ f(s, h) = 1 – \frac{(s + 0.1)^2}{4h – 0.7} $$
$$ f(b) = 0.285(2.2 – b)^2 + 0.785 $$
$$ f(r) = 0.42 + 0.16 \left( r^{-1} – 6.864 \right)^{0.5} $$
$$ f(h) = 0.444 h^{-1.4} $$
$$ f(r, s) = 1 + 81 \cdot \left[0.769 – (0.407 – s)^2\right] \delta r^2 $$
Here, \( \delta \) represents the ratio of fillet undercut depth to fillet radius, a parameter specific to ductile iron casting processes like rolling.
5. Pfündel (FVV) Method
The Pfündel method offers a simplified yet effective formulation:
$$ \text{Shape Coefficient} = \frac{K}{(r b h^2)^C} $$
where:
$$ K = 4.775 – 10.84(1-s) + 8.658(1-s)^2 – 2.22(1-s)^3 $$
$$ C = 1.7(1-s) – 0.243(1-s)^2 – 0.27(1-s)^3 – 0.484 $$
These methods were applied to a diverse set of crankshafts made from QT800-6 ductile iron casting. The goal was to evaluate which method best aligns with experimental fatigue data, ensuring reliable safety factor predictions for ductile iron casting applications.
Computational Results and Data Analysis
To validate the calculation methods, I collected data from 43 crankshafts that had undergone systematic bending fatigue testing. These crankshafts, all manufactured from QT800-6 ductile iron casting, cover a wide range of applications: passenger vehicle engines, commercial vehicle engines (light to heavy-duty), marine engines, construction machinery, and generator sets. The geometric parameters vary significantly, including different pin diameters, web widths, and center distances, making the dataset representative of industry standards. Among these, Crankshaft 0# serves as a reference, with over 300,000 units produced and consistent fatigue test results across multiple trials using the staircase method.
The table below summarizes the structural parameters of the crankshafts. Note that all dimensions are in millimeters, and the ductile iron casting material is QT800-6 with journal quenching and fillet rolling.
| Crankshaft ID | Main Journal Diameter (mm) | Crankpin Diameter \(D\) (mm) | Web Thickness \(H\) (mm) | Web Width \(B\) (mm) | Center Distance (mm) | Cylinder Bore (mm) | Main Journal Width (mm) | Crankpin Width (mm) |
|---|---|---|---|---|---|---|---|---|
| 0# | 85.66 | 66 | 26.85 | 116 | 56 | 110 | 38 | 40 |
| 1# | 85.66 | 69.9 | 25.4 | 114.8 | 66 | 112 | 38 | 42.7 |
| 2# | 85.66 | 69.9 | 25.4 | 118.8 | 66 | 112 | 38.1 | 42.6 |
| 3# | 85.66 | 66 | 26.85 | 116 | 60 | 110 | 38 | 40 |
| 4# | 140 | 100 | 32 | 143.2 | 82.5 | 145 | 50.5 | 61 |
| 5# | 129 | 92 | 34 | 130 | 77.5 | 123 | 46 | 46 |
| 6# | 118 | 100 | 40.8 | 134 | 82.5 | 145 | 50.5 | 61 |
| 7# | 114 | 79 | 23.75 | 134 | 68 | 114 | 43 | 46 |
| 8# | 103 | 90 | 31.5 | 139 | 66 | 123 | 40.5 | 40.5 |
| 9# | 100 | 82 | 29 | 145 | 72.5 | 123 | 40.5 | 40.5 |
| 10# | 100 | 82 | 29 | 132 | 72.5 | 120 | 46 | 46 |
| 11# | 100 | 82 | 29 | 147.44 | 65 | 126 | 46 | 46 |
| 12# | 98 | 76 | 24 | 150 | 67.5 | 114 | 43 | 46 |
| 13# | 91 | 80 | 28 | 135 | 70 | 113 | 44 | 40 |
| 14# | 88 | 81 | 26 | 112 | 67.5 | 110 | 38 | 38 |
| 15# | 88 | 81 | 26 | 140 | 67.5 | 110 | 38 | 38 |
| 16# | 87 | 70 | 28 | 135 | 66 | 108 | 44 | 40 |
| 17# | 85 | 70 | 28 | 136 | 62.5 | 106 | 37 | 42 |
| 18# | 85 | 70 | 28 | 140 | 62.5 | 105 | 44 | 40 |
| 19# | 85 | 70 | 28 | 135 | 62.5 | 108 | 44 | 40 |
| 20# | 85 | 70 | 28 | 136 | 62.5 | 106 | 37 | 42 |
| 21# | 85 | 70 | 28 | 150.5 | 62.5 | 106 | 37 | 42 |
| 22# | 85 | 70 | 28 | 140 | 62.5 | 108 | 44 | 40 |
| 23# | 85 | 70 | 28 | 116 | 57.5 | 108 | 44 | 40 |
| 24# | 85 | 70 | 28 | 130 | 66 | 108 | 36 | 40 |
| 25# | 83 | 69 | 22 | 98 | 60 | 102 | 37.5 | 39 |
| 26# | 83 | 69 | 22.75 | 95 | 57.5 | 102 | 35.5 | 39 |
| 27# | 83 | 69 | 22.75 | 100 | 60 | 105 | 35.5 | 39 |
| 28# | 83 | 69 | 22.75 | 108 | 60 | 105 | 35.5 | 39 |
| 29# | 80 | 64 | 23 | 130 | 59 | 105 | 36 | 38 |
| 30# | 80 | 56.5 | 23 | 110 | 50 | 94.4 | 29.2 | 31 |
| 31# | 80 | 64 | 23 | 130 | 59 | 105 | 36 | 38 |
| 32# | 76 | 64 | 23 | 96 | 50 | 102 | 36 | 38 |
| 33# | 71 | 60 | 22 | 118 | 52.5 | 98 | 33 | 33 |
| 34# | 70 | 56 | 23 | 110 | 50 | 92 | 32 | 32 |
| 35# | 70 | 53 | 21 | 110 | 51 | 93 | 31 | 33 |
| 36# | 70 | 56 | 23 | 110 | 51.5 | 100 | 32 | 32 |
| 37# | 68 | 52 | 19.5 | 88.7 | 52 | 85 | 26 | 28 |
| 38# | 65 | 54 | 22.7 | 90 | 53.5 | 94 | 30 | 30 |
| 39# | 65 | 53 | 20.2 | 94 | 47.3 | 89.9 | 30.3 | 29.5 |
| 40# | 64.7 | 52.7 | 19.8 | 94 | 43 | 89.9 | 30.3 | 29.5 |
| 41# | 52 | 41 | 17 | 70 | 38.5 | 71 | 25 | 27 |
| 42# | 48 | 44 | 17.5 | 70 | 45.75 | 81 | 23.5 | 20 |
Using the dimensionless parameters, I computed the shape coefficients for each crankshaft via the five methods. To relate these coefficients to safety factors, I normalized the results with respect to Crankshaft 0#, whose experimentally measured safety factor is 1.99 (derived from staircase method fatigue tests). The safety factor for any crankshaft was estimated as:
$$ \text{Safety Factor} = \text{SF}_{0#} + \left( \frac{\text{Shape Coefficient} – \text{Shape Coefficient}_{0#}}{\text{Shape Coefficient}_{0#}} \right) \times \text{SF}_{0#} $$
where \( \text{SF}_{0#} = 1.99 \). This transformation allows direct comparison between calculated and measured safety factors. The results are tabulated below.
| Crankshaft ID | Measured Safety Factor | Pfündel (FVV) | Stahl | Daimler-Benz | Iki-Takataki | Arai Junichi |
|---|---|---|---|---|---|---|
| 0# | 1.99 | 1.99 | 1.99 | 1.99 | 1.99 | 1.99 |
| 1# | 1.50 | 1.23 | 1.41 | 1.09 | 1.50 | 1.34 |
| 2# | 1.50 | 1.28 | 1.44 | 1.18 | 1.56 | 1.39 |
| 3# | 1.88 | 1.84 | 1.96 | 1.83 | 1.93 | 1.87 |
| 4# | 1.65 | 1.61 | 1.02 | 1.16 | 1.29 | 1.65 |
| 5# | 1.84 | 1.80 | 1.52 | 1.53 | 1.54 | 1.68 |
| 6# | 1.70 | 1.59 | 1.85 | 1.47 | 1.57 | 1.55 |
| 7# | 1.66 | 1.58 | 0.94 | 1.26 | 1.45 | 1.82 |
| 8# | 1.75 | 1.69 | 1.36 | 1.42 | 1.52 | 1.66 |
| 9# | 1.58 | 1.45 | 1.40 | 1.38 | 1.64 | 1.49 |
| 10# | 1.45 | 1.31 | 1.34 | 1.13 | 1.46 | 1.36 |
| 11# | 1.71 | 1.78 | 1.48 | 1.71 | 1.77 | 1.79 |
| 12# | 1.56 | 1.40 | 1.13 | 1.55 | 1.70 | 1.46 |
| 13# | 1.35 | 1.24 | 1.31 | 1.11 | 1.49 | 1.32 |
| 14# | 1.20 | 0.66 | 0.87 | 0.42 | 0.99 | 0.73 |
| 15# | 1.20 | 1.05 | 1.03 | 0.94 | 1.39 | 1.16 |
| 16# | 1.73 | 1.78 | 1.91 | 1.90 | 2.00 | 1.82 |
| 17# | 1.89 | 1.86 | 1.93 | 2.01 | 2.05 | 1.88 |
| 18# | 1.77 | 1.89 | 1.95 | 2.11 | 2.10 | 1.90 |
| 19# | 1.78 | 1.85 | 1.93 | 1.98 | 2.03 | 1.88 |
| 20# | 1.86 | 1.86 | 1.93 | 2.01 | 2.05 | 1.88 |
| 21# | 1.92 | 1.98 | 2.00 | 2.41 | 2.25 | 1.93 |
| 22# | 1.83 | 1.89 | 1.95 | 2.11 | 2.10 | 1.90 |
| 23# | 1.73 | 1.85 | 1.88 | 1.78 | 1.85 | 1.85 |
| 24# | 1.82 | 1.69 | 1.88 | 1.74 | 1.92 | 1.77 |
| 25# | 1.10 | 0.80 | 0.88 | 0.53 | 1.05 | 0.86 |
| 26# | 1.22 | 1.05 | 1.01 | 0.75 | 1.12 | 1.05 |
| 27# | 1.18 | 0.95 | 1.01 | 0.69 | 1.15 | 1.00 |
| 28# | 1.29 | 1.08 | 1.07 | 0.85 | 1.28 | 1.14 |
| 29# | 1.71 | 1.62 | 1.55 | 1.87 | 1.92 | 1.62 |
| 30# | 2.20 | 2.14 | 2.07 | 2.31 | 2.19 | 2.13 |
| 31# | 1.74 | 1.62 | 1.55 | 1.87 | 1.92 | 1.62 |
| 32# | 1.75 | 1.60 | 1.41 | 1.35 | 1.49 | 1.56 |
| 33# | 1.59 | 1.66 | 1.61 | 1.82 | 1.90 | 1.68 |
| 34# | 1.85 | 1.97 | 2.06 | 2.15 | 2.13 | 1.98 |
| 35# | 1.86 | 1.88 | 1.93 | 2.20 | 2.14 | 1.87 |
| 36# | 1.85 | 1.92 | 2.04 | 2.10 | 2.11 | 1.94 |
| 37# | 1.38 | 1.34 | 1.55 | 1.26 | 1.62 | 1.46 |
| 38# | 1.45 | 1.55 | 2.00 | 1.49 | 1.81 | 1.72 |
| 39# | 1.55 | 1.64 | 1.69 | 1.60 | 1.78 | 1.68 |
| 40# | 1.68 | 1.83 | 1.68 | 1.80 | 1.85 | 1.84 |
| 41# | 1.71 | 1.75 | 2.00 | 1.71 | 1.88 | 1.82 |
| 42# | 1.14 | 0.93 | 1.67 | 0.94 | 1.53 | 1.39 |
To quantify the accuracy of each method, I calculated the percentage deviation between computed and measured safety factors:
$$ \text{Deviation} = \frac{\text{Calculated SF} – \text{Measured SF}}{\text{Measured SF}} \times 100\% $$
Positive deviations indicate overestimation, while negative ones indicate underestimation. The deviations for all crankshafts are summarized in the following table, along with statistical aggregates.
| Crankshaft ID | Pfündel (FVV) | Stahl | Daimler-Benz | Iki-Takataki | Arai Junichi |
|---|---|---|---|---|---|
| 0# | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% |
| 1# | -18.27% | -5.83% | -27.36% | -0.23% | -10.90% |
| 2# | -14.58% | -4.22% | -21.42% | 3.82% | -7.60% |
| 3# | -2.24% | 4.02% | -2.67% | 2.47% | -0.29% |
| 4# | -2.22% | -37.97% | -29.42% | -21.56% | -0.19% |
| 5# | -2.41% | -17.26% | -16.98% | -16.46% | -8.53% |
| 6# | -6.44% | 8.69% | -13.45% | -7.50% | -8.95% |
| 7# | -4.77% | -43.64% | -24.20% | -12.43% | 9.34% |
| 8# | -3.53% | -22.55% | -19.13% | -12.89% | -5.33% |
| 9# | -8.40% | -11.29% | -12.95% | 3.53% | -5.38% |
| 10# | -9.61% | -7.89% | -22.11% | 0.95% | -6.23% |
| 11# | 4.25% | -13.54% | 0.02% | 3.79% | 4.79% |
| 12# | -10.56% | -27.35% | -0.57% | 9.23% | -6.73% |
| 13# | -8.46% | -3.18% | -18.07% | 10.67% | -2.22% |
| 14# | -45.31% | -27.53% | -64.83% | -17.43% | -38.95% |
| 15# | -12.21% | -13.98% | -21.69% | 16.08% | -3.75% |
| 16# | 2.67% | 10.37% | 9.97% | 15.75% | 5.49% |
| 17# | -1.55% | 2.30% | 6.23% | 8.22% | -0.30% |
| 18# | 6.97% | 10.36% | 19.26% | 18.70% | 7.58% |
| 19# | 3.99% | 8.38% | 11.35% | 14.06% | 5.49% |
| 20# | 0.04% | 3.95% | 7.95% | 9.97% | 1.31% |
| 21# | 3.02% | 4.37% | 25.68% | 16.94% | 0.55% |
| 22# | 3.46% | 6.74% | 15.35% | 14.81% | 4.05% |
| 23# | 7.18% | 8.59% | 2.79% | 6.68% | 7.21% |
| 24# | -7.06% | 3.10% | -4.22% | 5.44% | -2.81% |
| 25# | -27.32% | -19.75% | -51.62% | -4.41% | -21.70% |
| 26# | -13.92% | -17.01% | -38.14% | -8.21% | -14.25% |
| 27# | -19.55% | -14.01% | -41.40% | -2.53% | -15.54% |
| 28# | -16.57% | -17.25% | -34.42% | -0.65% | -11.74% |
| 29# | -5.46% | -9.17% | 9.61% | 12.51% | -5.43% |
| 30# | -2.69% | -6.12% | 5.00% | -0.59% | -3.40% |
| 31# | -7.09% | -10.73% | 7.72% | 10.57% | -7.06% |
| 32# | -8.55% | -19.16% | -22.61% | -14.62% | -10.99% |
| 33# | 4.54% | 1.14% | 14.64% | 19.73% | 5.44% |
| 34# | 6.56% | 11.29% | 16.38% | 15.35% | 7.21% |
| 35# | 1.27% | 3.83% | 18.52% | 15.13% | 0.60% |
| 36# | 3.75% | 10.45% | 13.34% | 14.12% | 5.07% |
| 37# | -2.67% | 12.39% | -8.99% | 17.25% | 5.94% |
| 38# | 7.00% | 37.89% | 3.09% | 24.84% | 18.81% |
| 39# | 5.77% | 8.72% | 3.39% | 15.16% | 8.59% |
| 40# | 9.21% | 0.17% | 6.96% | 10.16% | 9.42% |
| 41# | 2.15% | 16.80% | -0.25% | 10.00% | 6.34% |
| 42# | -18.73% | 46.08% | -17.16% | 34.17% | 21.87% |
From the deviation data, I derived aggregate statistics to assess the overall performance of each method for QT800-6 ductile iron casting crankshafts. The table below shows the percentage of crankshafts within specified deviation ranges, as well as the proportion of negative deviations (indicating conservative estimates).
| Deviation Range | Pfündel (FVV) | Stahl | Daimler-Benz | Iki-Takataki | Arai Junichi |
|---|---|---|---|---|---|
| Negative Deviations | 61.90% | 50.00% | 54.76% | 30.95% | 54.76% |
| Within ±5% | 42.86% | 23.81% | 21.43% | 23.81% | 28.57% |
| Within ±10% | 76.19% | 45.24% | 33.33% | 50.00% | 76.19% |
| Within ±15% | 85.71% | 69.05% | 50.00% | 66.67% | 88.10% |
| Within ±20% | 95.24% | 83.33% | 69.05% | 92.86% | 92.86% |
| Within ±30% | 97.62% | 90.48% | 88.10% | 97.62% | 97.62% |
Interpretation and Discussion
The analysis reveals that the Pfündel (FVV) method exhibits the highest concordance with experimental data for QT800-6 ductile iron casting crankshafts. Specifically, 76.19% of its predictions fall within ±10% deviation, and 85.71% within ±15%. Moreover, it has a negative deviation proportion of 61.90%, meaning it tends to yield conservative safety factors—a desirable trait for design safety. The Arai Junichi method also performs well, with 76.19% within ±10% and 88.10% within ±15%, though it has a slightly lower negative deviation rate of 54.76%. These two methods are thus most suitable for rapid assessment of ductile iron casting crankshafts.
In contrast, the Stahl, Daimler-Benz, and Iki-Takataki methods show larger discrepancies. For instance, the Daimler-Benz method has only 33.33% of predictions within ±10%, and the Iki-Takataki method has a low negative deviation rate of 30.95%, indicating a tendency to overestimate safety factors, which could lead to non-conservative designs. Notably, Crankshaft 14# exhibits extreme deviations across all methods; this crankshaft was originally designed for forged steel and may have geometric features unsuitable for ductile iron casting, highlighting the importance of material-specific validation.
The relationship between shape coefficient and safety factor is inverse: a lower shape coefficient corresponds to higher fatigue strength and thus a higher safety factor. This principle allows engineers to use shape coefficient calculations as a proxy for strength evaluation. For QT800-6 ductile iron casting, which combines material robustness with rolling reinforcement, the Pfündel and Arai methods effectively capture the geometric influences. I have implemented these methods in an Excel-based model that automates the calculation of shape coefficients and safety factors, enabling rapid iteration during design phases. This tool is particularly useful for screening multiple crankshaft variants before committing to costly CAE analyses or physical tests.
It is important to acknowledge that all empirical methods have limitations based on their original test conditions. The Pfündel method, for example, was developed from European engine studies, while Arai’s method incorporates Japanese design practices. However, their applicability to QT800-6 ductile iron casting is validated by this extensive dataset. Engineers should use these methods for preliminary assessments and then verify critical designs with detailed simulations or fatigue testing, especially for novel configurations or extreme operating conditions.
Conclusions and Recommendations
Based on my analysis of 43 QT800-6 ductile iron casting crankshafts, I conclude that the shape coefficient method offers a viable rapid assessment technique for fatigue safety factors. Among the five traditional methods, the Pfündel (FVV) calculation provides the best balance of accuracy and conservatism, closely followed by the Arai Junichi method. These methods align well with experimental fatigue data, with over 85% of predictions within ±15% deviation. Therefore, for initial design evaluations of ductile iron casting crankshafts, I recommend using the Pfündel method as a primary tool, supplemented by the Arai method for cross-validation.
To facilitate adoption, I have developed a combined Excel model that integrates both methods, allowing engineers to input crankshaft dimensions and quickly obtain estimated safety factors. This approach addresses the gap left by CAE software that lacks material parameters for QT800-6 ductile iron casting and rolling effects. By enabling early identification of potential weak points, it streamlines the design process and ensures compliance with the 1.8 safety factor requirement for high-pressure engines.
Future work could focus on refining these methods through additional data from advanced ductile iron casting grades or incorporating machine learning techniques to account for complex interactions between geometry, material, and processing. Nonetheless, the current methodology provides a robust foundation for rapid safety factor assessment in the realm of ductile iron casting crankshafts, contributing to more efficient and reliable engine development.