Low-pressure casting, as a mature forming technology, offers smooth mold filling, low cost, and high-quality castings, making it widely adopted for automotive components like wheel hubs. However, lightweight initiatives outlined in national manufacturing strategies have accelerated the use of aluminum and magnesium alloys, presenting new challenges for traditional casting methods. This study optimizes process parameters for A356 aluminum alloy wheel hubs using the Taguchi method, signal-to-noise ratio analysis, and numerical simulation, targeting minimized porosity, reduced secondary dendrite arm spacing (SDAS), and shortened solidification time. The parameters investigated include pouring temperature, upper/lower die preheating temperatures, and filling time.
The orthogonal array L9(34) was implemented with four factors at three levels (Table 1). Numerical simulations evaluated porosity, SDAS, and solidification time for each combination. Signal-to-noise (S/N) ratios, calculated using the smaller-the-better characteristic, quantified performance robustness:
$$S/N = -10 \times \log\left(\frac{1}{n}\sum_{i=1}^{n}Y_i^2\right)$$
where \(n\) is the number of trials and \(Y_i\) is the measured response value.
| Level | A: Pouring Temp. (°C) | B: Upper Die Temp. (°C) | C: Lower Die Temp. (°C) | D: Filling Time (s) |
|---|---|---|---|---|
| 1 | 650 | 200 | 260 | 20 |
| 2 | 680 | 240 | 300 | 25 |
| 3 | 710 | 280 | 340 | 30 |
| Trial | A | B | C | D | Porosity (%) | Porosity S/N | SDAS (μm) | SDAS S/N | Solidification Time (s) | Solidification S/N |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 1 | 1 | 1 | 10.040 | -20.003 | 58.210 | -35.300 | 367.1 | -51.3 |
| 2 | 1 | 2 | 2 | 2 | 1.907 | -5.607 | 59.190 | -35.445 | 390.0 | -51.8 |
| 3 | 1 | 3 | 3 | 3 | 0.833 | 1.587 | 60.440 | -35.626 | 419.3 | -52.5 |
| 4 | 2 | 1 | 2 | 3 | 2.199 | -6.523 | 59.060 | -35.426 | 409.3 | -52.2 |
| 5 | 2 | 2 | 3 | 1 | 1.139 | 1.130 | 60.900 | -35.692 | 433.1 | -52.7 |
| 6 | 2 | 3 | 1 | 2 | 1.977 | -6.008 | 60.320 | -35.609 | 425.4 | -52.6 |
| 7 | 3 | 1 | 3 | 2 | 0.872 | 1.190 | 61.200 | -35.735 | 455.1 | -53.2 |
| 8 | 3 | 2 | 1 | 3 | 2.233 | -6.978 | 60.580 | -35.647 | 446.1 | -53.0 |
| 9 | 3 | 3 | 2 | 1 | 1.301 | -2.286 | 61.400 | -35.904 | 473.9 | -53.5 |
Mean response and range analysis (Table 3) revealed the influence hierarchy of parameters. For porosity, the dominant factor was lower die temperature (C), followed by upper die temperature (B), pouring temperature (A), and filling time (D). Higher die and pouring temperatures generally reduced porosity by improving melt fluidity and feeding capacity, though excessively long filling times had detrimental effects. In contrast, SDAS was most sensitive to pouring temperature (A). Higher temperatures increased SDAS, coarsening the microstructure and degrading mechanical properties – a critical consideration when comparing against processes like squeeze casting where pressure refines microstructure. Shorter filling times slightly improved SDAS. Solidification time was primarily governed by pouring temperature (A), where lower temperatures accelerated solidification but risked premature freezing.
| Response | Level | A: Pouring Temp. | B: Upper Die Temp. | C: Lower Die Temp. | D: Filling Time | Ranking (Dominance) |
|---|---|---|---|---|---|---|
| Porosity S/N | k1 | -8.008 | -8.445 | -10.996 | -7.053 | C > B > A > D |
| k2 | -3.799 | -3.818 | -4.805 | -3.475 | ||
| k3 | -2.691 | -2.236 | 1.303 | -3.971 | ||
| Range (R) | 5.317 | 6.209 | 12.299 | 3.578 | ||
| SDAS S/N | k1 | -35.457 | -35.487 | -35.519 | -35.632 | A > B > C > D |
| k2 | -35.576 | -35.595 | -35.592 | -35.600 | ||
| k3 | -35.762 | -35.704 | -35.684 | -35.566 | ||
| Range (R) | 0.305 | 0.217 | 0.165 | 0.066 | ||
| Solidification S/N | k1 | -51.9 | -52.2 | -52.3 | -52.5 | A > B > C > D |
| k2 | -52.5 | -52.5 | -52.5 | -52.5 | ||
| k3 | -53.2 | -52.8 | -52.8 | -52.6 | ||
| Range (R) | 1.30 | 0.6 | 0.5 | 0.1 |
Three candidate optimal parameter sets were identified:
* **Set 1 (A3B3C3D2):** Maximizes porosity reduction (710°C Pouring, 280°C Upper Die, 340°C Lower Die, 25s Fill).
* **Set 2 (A1B1C1D1):** Minimizes SDAS and solidification time (650°C Pouring, 200°C Upper Die, 260°C Lower Die, 20s Fill).
* **Set 3 (A1B3C3D1):** Balanced solution prioritizing quality and productivity (650°C Pouring, 280°C Upper Die, 340°C Lower Die, 20s Fill).
Numerical validation confirmed Set 3’s superiority. While Set 1 achieved the lowest porosity (0.833%), it exhibited the highest SDAS (60.44 μm) and longest solidification time (419.3 s), indicating poor mechanical properties and low productivity. Set 2 offered the finest microstructure (SDAS 58.21 μm) and fastest solidification (367.1 s) but suffered severe porosity (10.04%), rendering it unacceptable. Set 3 successfully eliminated major porosity (0.835%) while maintaining acceptable SDAS (60.48 μm) and reducing solidification time (439.8 s) compared to Set 1, demonstrating an optimal balance suitable for production. The thermal management strategy in Set 3 – moderate pouring temperature combined with high, controlled die temperatures – proved highly effective for feeding complex wheel hub geometries, contrasting with the pressure-assisted feeding mechanism central to squeeze casting.
Key conclusions are:
1. Process parameters significantly impact A356 wheel hub quality. Lower die temperature dominates porosity control. Pouring temperature critically influences SDAS and solidification time. Filling time has the least impact across all responses.
2. The optimal parameter combination for the studied hub is: **Pouring Temperature = 650°C, Upper Die Temperature = 280°C, Lower Die Temperature = 340°C, Filling Time = 20 s (A1B3C3D1)**.
3. This optimized set effectively eliminates porosity defects while achieving a favorable compromise between mechanical integrity (SDAS) and production efficiency (solidification time). Further refinement of SDAS and solidification time remains possible, potentially drawing on techniques like squeeze casting for localized microstructure enhancement under pressure.
This methodology provides a robust framework for optimizing low-pressure casting processes, offering significant quality and efficiency gains over traditional trial-and-error approaches. The understanding of parameter interactions, especially thermal gradients and feeding dynamics, is also valuable for process development in related high-integrity casting techniques like squeeze casting.