In the manufacturing of high-precision machine tools, the quality and performance of cast components play a critical role in determining the overall accuracy, stability, and longevity of the equipment. Machine tool castings, such as beds, columns, crossrails, tables, and saddles, constitute approximately 70–80% of the total machine weight and are predominantly made from gray cast iron (e.g., HT250, HT300) or, for heavier-duty applications, ductile cast iron (e.g., QT450, QT600). However, these machine tool castings often face challenges like deformation, cracking, and loss of precision due to residual stresses, inadequate structural design, and suboptimal material properties. This study focuses on addressing these issues through a comprehensive investigation into casting processes, melting techniques, and heat treatment methods to achieve high-strength, low-stress cast iron suitable for advanced machine tool castings. By integrating simulation analysis, compositional optimization, and rigorous aging treatments, we aim to enhance the structural rigidity and material stiffness of machine tool castings, thereby improving their performance and reliability in demanding industrial applications.
The failure of machine tool castings is frequently attributed to excessive residual stresses, poor structural design, or inappropriate material selection. For instance, in one case, a large HT300 column cracked due to non-uniform wall thickness and lack of fillet transitions, leading to stress concentration. Another example involves an HT300 crossrail that developed贯穿裂纹 because of overly low carbon equivalent (CE) values, which increased manufacturing stresses. Similarly, a massive HT300 ring casting cracked during cooling due to insufficient保温 time and temperature gradients, while an HT250 table exhibited multiple cracks in its internal ribs after rapid cooling during aging. These cases highlight common issues in the production of machine tool castings, emphasizing the need for improved process controls. To mitigate such problems, we have explored various aspects of casting technology, including structural optimization through simulation, melting practices with high carbon equivalent and alloying, and advanced aging techniques to reduce residual stresses. The ultimate goal is to produce machine tool castings with high strength, low stress, and superior dimensional stability.
Structural rationality is paramount for the integrity of machine tool castings. Inadequate design can lead to stress concentrations, deformation, and cracking. To address this, we employed casting simulation software to analyze filling and solidification processes, identifying potential defect zones and optimizing geometries. For example, a long bed casting measuring 16 m × 1.9 m with a mass of 68 tons was initially designed with sharp transitions in rib connections, resulting in high residual stresses and a risk of cracking. Simulation results showed that the von Mises stress coefficient exceeded 0.8 at critical junctions, indicating a high probability of failure. By modifying the rib structure—adding横向加强筋 without increasing weight—the maximum von Mises coefficient was reduced to below 0.5, significantly lowering the crack risk. This demonstrates how simulation-driven design improvements can enhance the structural stiffness of machine tool castings. The relationship between stress and geometry can be expressed using the von Mises criterion, often applied in casting simulations: $$ \sigma_{vm} = \sqrt{\frac{(\sigma_1 – \sigma_2)^2 + (\sigma_2 – \sigma_3)^2 + (\sigma_3 – \sigma_1)^2}{2}} $$ where $\sigma_{vm}$ is the von Mises stress, and $\sigma_1, \sigma_2, \sigma_3$ are principal stresses. Minimizing $\sigma_{vm}$ through design changes is crucial for the durability of machine tool castings.
Beyond structural aspects, the material properties of machine tool castings are equally vital. Traditional gray cast iron for machine tool castings often exhibits fluctuations in composition and mechanical properties, leading to inconsistent performance. Our analysis of production data from 2018 to 2020 revealed that gray cast iron components had carbon (C) content ranging from 2.9% to 3.2%, silicon (Si) from 1.4% to 2%, and carbon equivalent (CE) from 3.4% to 3.7%, with corresponding tensile strengths varying between 294 MPa and 396 MPa. Such variability underscores the need for tighter compositional control. For machine tool castings, high carbon equivalent is a foundation for low-stress materials, as it promotes graphite formation and reduces shrinkage stresses. The carbon equivalent formula for cast iron is: $$ CE = C + \frac{1}{3}(Si + P) $$ where C, Si, and P are weight percentages. By targeting higher CE values—e.g., 3.6–3.8% for gray cast iron and 4.4–4.5% for ductile cast iron—we can achieve better stress relief while maintaining strength through alloying. Additionally, the silicon-to-carbon ratio (Si/C) influences microstructure; for HT300 machine tool castings, we found that increasing Si/C from 0.61 to 0.72 improved tensile strength from around 330 MPa to over 370 MPa, though hardness remained relatively stable. This highlights the importance of balancing composition for optimal performance in machine tool castings.
| Year | Tensile Strength (MPa) | Hardness (HBW) | ω(C) (%) | ω(Si) (%) | CE (%) | ω(Si)/ω(C) |
|---|---|---|---|---|---|---|
| 2018 | 294–396 (avg 383) | 199–264 (avg 234) | 2.92–3.18 (avg 3.04) | 1.40–1.91 (avg 1.56) | 3.44–3.72 (avg 3.55) | 0.39–0.67 (avg 0.49) |
| 2019 | 317–388 (avg 376) | 203–270 (avg 235) | 2.90–3.15 (avg 3.01) | 1.43–1.88 (avg 1.58) | 3.47–3.67 (avg 3.57) | 0.39–0.60 (avg 0.50) |
| 2020 | 321–381 (avg 354) | 203–256 (avg 232) | 2.96–3.20 (avg 3.05) | 1.45–1.83 (avg 1.55) | 3.51–3.72 (avg 3.58) | 0.38–0.66 (avg 0.52) |
To achieve high-strength, low-stress machine tool castings, we optimized the melting process. This involved using a synthetic cast iron approach with 50–60% scrap steel and high-quality graphitizing carburizers, minimizing the use of pig iron to reduce遗传 effects. The molten iron was superheated to 1500–1550°C and held for 5–10 minutes to improve purity. Compositional targets were set for high carbon equivalent, coupled with alloying elements like copper (Cu), chromium (Cr), tin (Sn), and antimony (Sb) to enhance pearlite formation and strength. For instance, in HT300 machine tool castings, we aimed for ω(C): 3.05–3.10%, ω(Si): 1.60–1.80%, ω(Mn): 1.00–1.10%, with CE of 3.60–3.70%, and added ω(Cr): 0.20–0.30% and ω(Cu): 0.50–0.70%. Multiple-stage inoculation was applied, including ladle bottom, stream during tapping, and instantaneous pouring inoculation, to refine graphite and improve nucleation. For ductile iron machine tool castings, such as QT600-3, we increased CE to 4.40–4.50% and used pretreatment of molten iron with encapsulated inoculants to boost graphite nodule counts. Experimental results showed that pretreatment increased nodule density from 75–107/mm² to 75–135/mm², enhancing mechanical properties. The elastic modulus (E) is a key indicator of material stiffness; for machine tool castings, higher E values are desirable. We measured E for various alloys and found that adding Cu, Sb, or combinations significantly improved consistency. The relationship between alloying and elastic modulus can be modeled as: $$ E = E_0 + \sum k_i \cdot X_i $$ where $E_0$ is the base modulus, $k_i$ are coefficients, and $X_i$ are alloy concentrations. This approach ensures that machine tool castings meet rigorous performance standards.
| Alloy Type | Number of Samples with E < 160 GPa | Percentage (%) | Number of Samples with E ≥ 160 GPa | Percentage (%) |
|---|---|---|---|---|
| None | 37 | 54.4 | 31 | 45.6 |
| + Cu | 8 | 22.8 | 27 | 77.2 |
| + Sb | 4 | 17.4 | 19 | 82.6 |
| + Cu + Sb | 0 | 0 | 2 | 100 |
| + Cu + Mo | 0 | 0 | 2 | 100 |
| + Cu + Sn | 0 | 0 | 10 | 100 |
Heat treatment and aging processes are crucial for reducing residual stresses in machine tool castings. After pouring, castings must be cooled slowly in molds to minimize thermal gradients. We monitored the cooling rates of bed castings of different masses: for castings under 3 tons, the average cooling rate was about 25°C/h, while for those over 8 tons, it was around 13°C/h. Unloading temperature was kept below 300°C to reduce deformation. To further relieve stresses, we compared natural aging, thermal aging, and vibration aging. Natural aging involves long-term exposure to environmental temperature changes, reducing stresses by about 20% but taking months to years. Thermal aging heats castings to弹塑性转变温度 (e.g., 500–600°C for cast iron), holds for several hours, and cools slowly, achieving 50–70% stress reduction. Vibration aging applies cyclic stresses to induce microplastic deformation, reducing stresses by 30–50% in minutes. For machine tool castings, a combination of these methods can be effective; for instance, thermal aging followed by vibration aging and natural aging can achieve up to 78% stress relief. The stress relaxation during thermal aging can be described by the Arrhenius-type equation: $$ \sigma(t) = \sigma_0 \cdot e^{-kt} $$ where $\sigma(t)$ is residual stress at time t, $\sigma_0$ is initial stress, and k is a rate constant dependent on temperature. Optimizing these parameters is essential for producing low-stress machine tool castings.
Residual stress measurements on machine tool castings revealed significant improvements with optimized processes. Initially, HT300 beds in as-cast condition showed maximum residual stresses up to 175 MPa, which were unevenly distributed. After thermal aging, stresses reduced to a maximum of 79.7 MPa, but variations persisted. By implementing high-carbon-equivalent compositions and复合合金化, we produced five machine tool castings (e.g., tables and beds) and measured their residual stresses using strain gauge methods. Results indicated that stresses were predominantly compressive, with tensile stresses at low levels. For example, on a worktable, measured strains ranged from -154 to 286 µε, corresponding to residual stresses between -227.3 MPa and 112.5 MPa, with most values below 100 MPa. This demonstrates the effectiveness of our approach in minimizing detrimental tensile stresses. The general formula for calculating residual stress from strain measurements is: $$ \sigma_x = \frac{E}{1-\nu^2} (\epsilon_x + \nu \epsilon_y) $$ where $\sigma_x$ is stress in the x-direction, E is elastic modulus, $\nu$ is Poisson’s ratio, and $\epsilon_x, \epsilon_y$ are strains. Ensuring low residual stresses is critical for the dimensional stability of machine tool castings during machining and service.
| Casting ID | Material | Maximum Tensile Stress (MPa) | Maximum Compressive Stress (MPa) | Average Elastic Modulus (GPa) |
|---|---|---|---|---|
| Worktable 01 | HT300 | 11.7 | -136.4 | 121 |
| Worktable 02 | HT300 | -33.3 | -118.1 | 119 |
| Bed 01 | HT300 | 11.7 | -234.4 | 111 |
| Bed 02 | HT300 | 32.1 | -227.3 | 112 |
| Bed 03 | HT300 | 25.7 | -82.8 | 112 |
Further material tests on high-strength, low-stress machine tool castings confirmed the benefits of our工艺. For six castings—including boxes, turntables, slides, columns, and beds—made from QT600-3 and HT300, we measured tensile strengths, hardness, and elastic modulus. The QT600-3 castings exhibited tensile strengths around 700 MPa, elongation of 3.0–4.0%, and elastic modulus of 160–170 GPa, while HT300 castings showed tensile strengths of 329–332 MPa and elastic modulus of 111–112 GPa. These values meet or exceed industry standards for machine tool castings. The maturity (RG) and relative hardness (RH) parameters, calculated as $$ RG = \frac{\sigma_u}{1000 \cdot (Sc – 0.25)} $$ and $$ RH = \frac{HB}{100 \cdot (Sc – 0.25)} $$ where $\sigma_u$ is tensile strength (MPa), HB is Brinell hardness, and Sc is共晶度, were also within optimal ranges, indicating good material quality. By consistently applying these techniques, we can produce machine tool castings with enhanced performance and longevity.
In summary, the production of high-strength, low-stress machine tool castings requires a holistic approach encompassing design, melting, and heat treatment. Through simulation analysis, we can optimize geometries to reduce stress concentrations in machine tool castings. Employing high carbon equivalent and复合合金化 in melting ensures adequate strength while minimizing stresses. Advanced aging methods, such as thermal and vibration treatments, further relieve残余 stresses. Our experiments demonstrate that these strategies yield machine tool castings with superior elastic modulus, low residual stresses, and excellent dimensional stability. Future work could explore additional alloy combinations or real-time monitoring systems for even better control. Ultimately, improving the manufacturing processes for machine tool castings is vital for advancing the precision and reliability of industrial machinery, contributing to more efficient and durable manufacturing equipment worldwide.