In the field of high-end manufacturing, the reliance on imported high-precision machine tools for critical sectors such as automotive, aerospace, rail, and defense has long been a challenge. A significant factor contributing to this dependency is the inadequate machining accuracy and precision retention of domestically produced machine tools. As a key foundational component, the cast iron bed directly influences the overall performance, with its dimensional accuracy and stability being paramount. Therefore, developing bed castings with superior precision retention is essential for improving the quality and competitiveness of domestic CNC precision machine tools. This study focuses on investigating the factors that affect the precision and precision retention of machine tool castings, specifically examining carbon equivalent (CE), residual stress, material rigidity, and structural rigidity.
To systematically analyze these factors, we designed and cast T-type bedway samples representing machine tool castings with two distinct structural dimensions: thin and thick sections. These samples were produced under controlled melting conditions, varying carbon equivalent and alloying elements for both gray cast iron and ductile iron. Different heat aging treatments were applied, and measurements of residual stress, straightness, and its variation over time were conducted. The objective is to provide a theoretical basis for optimizing the structure of machine tool castings, enhancing their rigidity, reducing residual stress, and ultimately improving their resistance to deformation and precision retention.
The stiffness of machine tool castings, encompassing both material stiffness (related to the elastic modulus of the material) and structural stiffness (related to the geometric design), along with residual stresses, are critical determinants of precision retention. Residual stresses, if not mitigated, can gradually release over time, leading to dimensional changes and loss of accuracy. In this study, we explore how adjusting the chemical composition and applying heat treatments can synergistically improve these properties for machine tool castings.
Materials and Experimental Methods
We utilized high-purity pig iron and pure iron as raw materials, melting them in a medium-frequency induction furnace. The chemical compositions were varied to achieve different carbon equivalent levels for gray cast iron and ductile iron samples. For gray cast iron, two groups were prepared: one with a lower CE (3.2%-3.4%) and another with a higher CE (3.6%-3.8%). Similarly, for ductile iron, samples with CE of 4.2%-4.3% and 4.4%-4.6% were produced. Alloying elements such as copper (Cu), tin (Sn), and chromium (Cr) were added in composite forms to some samples to study their effects.
The melting process involved superheating the iron melt to 1510-1520°C, after which alloys and ferrosilicon (75SiFe) were added to adjust the composition. The chemical composition was verified using an ARL direct-reading spectrometer. For gray iron, inoculation was performed at 1480-1500°C using a high-calcium barium inoculant (0.6% addition). For ductile iron, spheroidization was carried out using Elmag 5800 nodularizer (1.5% addition) via the sandwich method, followed by inoculation with the same inoculant. The melt was then poured at approximately 1350°C into molds for the T-type bedway samples and separately for standard test bars (Y-blocks for ductile iron). After casting, the samples were allowed to cool naturally to below 300°C before shakeout, without shot blasting, to avoid introducing additional stresses.
The heat aging treatment, referred to as the “multistep rising/falling temperature method,” was applied to selected samples. This process involves gradual heating and cooling stages with specific holding times to relieve residual stresses effectively. The temperature profile includes steps at 90°C, 190°C, 290°C, 390°C, 490°C, and 590°C, each with a 1-hour hold, and a controlled cooling rate of 30°C per hour down to room temperature.
Various measurements were taken to assess the properties of the machine tool castings. Mechanical properties, including tensile strength (Rm) and elongation (A%), were tested using a SHT4605 electro-hydraulic servo universal testing machine. The elastic modulus (E), a key indicator of material rigidity, was measured using a RUSpec ultrasonic resonance spectrometer. Microstructural analysis was conducted with a Leica DMILM optical microscope to observe graphite morphology and matrix structure.
Residual stresses on the guideway surfaces of the T-samples were measured via the blind-hole drilling method, employing a ZDL-II drilling device, YC-III stress instrument, and specialized strain gauges. Straightness measurements were performed using a GAVA1000-B air-flotation straightness measuring instrument on a grade 0 inspection platform. Each sample was divided into 10 segments with a span of 100 mm per segment, and straightness was recorded monthly to monitor changes over time.
The straightness error (fLS) was calculated according to the national standard GB/T 11336-2004, using the least squares method. The formulas for determining the coefficients of the least squares midline are as follows:
$$ a = \frac{\sum Z_i \sum X_i^2 – \sum X_i \sum X_i Z_i}{(n+1) \sum X_i^2 – (\sum X_i)^2} $$
$$ q = \frac{(n+1) \sum X_i Z_i – \sum X_i \sum Z_i}{(n+1) \sum X_i^2 – (\sum X_i)^2} $$
where \( n \) is the number of segments (n=9 for 10 points), \( X_i \) are the horizontal coordinates of the measured points, and \( Z_i \) are the vertical coordinates (height values) in micrometers. The transformed coordinates \( d_i \) are then computed:
$$ d_i = Z_i – a – q X_i $$
The straightness error is given by:
$$ f_{LS} = d_{max} – d_{min} $$
This method provides a quantitative measure of the geometric accuracy of the machine tool castings.
Results and Discussion
Structural Stiffness Analysis
Using SolidWorks SimulationXpress stress analysis tool, we performed linear static analyses on both thin and thick guideway models under identical loading conditions. The material properties were set as constant, the sides along the length were fixed, and a uniform pressure was applied perpendicular to the guideway surface. The displacement cloud charts revealed that the maximum displacement for the thin guideway was 0.964 mm, while for the thick guideway it was 0.582 mm. This demonstrates that the thick guideway design possesses higher structural stiffness, which is crucial for minimizing deformation under load in machine tool castings.
Gray Cast Iron Samples
The microstructure of the gray cast iron samples showed predominantly type A graphite with a pearlite matrix content exceeding 98%. Samples with lower CE exhibited finer graphite due to less carbon precipitation, whereas higher CE samples had coarser and longer graphite flakes distributed uniformly.
| Sample ID | Structure | C (%) | Si (%) | CE (%) | Mn (%) | Cu (%) | Sn (%) | Cr (%) | Rm (MPa) | E (GPa) |
|---|---|---|---|---|---|---|---|---|---|---|
| 1# | Thin | 2.758 | 1.777 | 3.36 | 1.174 | – | – | – | 365 | – |
| 2# | Thin | 3.054 | 1.799 | 3.66 | 1.194 | 0.569 | 0.062 | 0.353 | 346 | – |
| 3# | Thick | 2.695 | 1.504 | 3.21 | 1.188 | – | – | – | 357 | 114 |
| 4# | Thick | 3.250 | 1.880 | 3.88 | 1.138 | 0.672 | 0.029 | 0.342 | 322 | 129 |
Table 1 summarizes the chemical compositions and mechanical properties of the gray cast iron samples. Samples 2# and 4#, with higher CE and composite alloying (Cu-Sn-Cr), maintained high tensile strengths (346 MPa and 322 MPa, respectively) while achieving higher elastic moduli. Specifically, sample 4# had an elastic modulus of 129 GPa, compared to 114 GPa for sample 3#. This indicates that higher CE combined with alloying enhances material rigidity, which is vital for the stability of machine tool castings.
Residual stress measurements showed that in the as-cast state, samples with lower CE (1# and 3#) had higher residual stresses (56.5 MPa and 89.9 MPa, respectively), while higher CE samples (2# and 4#) had lower values (28.9 MPa and 34.3 MPa). After applying the multistep heat aging treatment, the residual stresses in samples 2# and 4# were further reduced to 18.6 MPa and 19.9 MPa, respectively. This confirms that increasing CE helps lower inherent residual stresses, and the heat aging process effectively relieves them, preventing long-term dimensional changes in machine tool castings.
Straightness measurements over time for the thin guideway samples are presented in Table 2. Sample 1# (lower CE, no alloying or aging) showed a straightness increase of 31.5 μm over three months, whereas sample 2# (higher CE, alloyed, and aged) increased by only 18.1 μm. The slower and smaller change in sample 2# demonstrates better precision retention, attributed to its higher material rigidity and lower residual stress.
| Sample ID | Month | Straightness fLS (μm) | Cumulative Change (μm) |
|---|---|---|---|
| 1# (Thin) | 1 | 324.3 | – |
| 2 | 344.6 | +20.3 | |
| 3 | 338.8 | -5.8 | |
| 4 | 355.8 | +31.5 | |
| 2# (Thin) | 1 | 365.0 | – |
| 2 | 365.8 | +0.8 | |
| 3 | 380.0 | +15.0 | |
| 4 | 383.1 | +18.1 |
For the thick guideway samples, straightness values were significantly lower. Samples 3# and 4# had straightness of 37.7 μm and 27.9 μm, respectively, which is about ten times smaller than their thin counterparts. This highlights the profound impact of structural stiffness: thicker sections with reduced wall thickness variations resist deformation more effectively, thereby enhancing the precision of machine tool castings.
Ductile Cast Iron Samples
Ductile iron samples were investigated for their potential to offer superior properties for machine tool castings. Table 3 presents the chemical compositions, mechanical properties, and residual stresses for the thick ductile iron guideway samples.
| Sample ID | C (%) | Si (%) | CE (%) | Mn (%) | Cu (%) | Sn (%) | Rm (MPa) | E (GPa) | Residual Stress (MPa) As-cast / Aged |
|---|---|---|---|---|---|---|---|---|---|
| 5# | 3.56 | 2.05 | 4.26 | 0.51 | – | – | 443 | 161 | 108.8 / – |
| 6# | 3.77 | 1.99 | 4.44 | 0.53 | 0.496 | 0.042 | 705 | 176 | 88.1 / 26.3 |
Sample 6#, with higher CE and composite alloying (Cu-Mn-Sn), exhibited remarkable tensile strength (705 MPa) and elastic modulus (176 GPa), indicating high material rigidity. Its residual stress in the as-cast state was lower than that of sample 5# (88.1 MPa vs. 108.8 MPa), and after heat aging, it dropped to 26.3 MPa. This underscores the synergy of high CE, alloying, and heat treatment in achieving low-stress, high-stiffness ductile iron for machine tool castings.
Straightness measurements for these samples are shown in Table 4. Sample 5# had a straightness of 19.5 μm, while sample 6# was slightly better at 17.4 μm. Both values are lower than those of the gray iron thick guideways, attributable to the superior material rigidity of ductile iron. The minimal difference between samples 5# and 6# suggests that even small improvements in composition and treatment can enhance the precision of machine tool castings.
| Sample ID | Straightness fLS (μm) |
|---|---|
| 5# (Thick Ductile) | 19.5 |
| 6# (Thick Ductile) | 17.4 |
Mechanisms and Further Analysis
The relationship between carbon equivalent and material properties in machine tool castings can be explained through metallurgical principles. Higher CE promotes graphite precipitation, which can reduce shrinkage stresses during solidification, thereby lowering residual stresses. However, excessive graphite may weaken tensile strength. The addition of alloying elements like copper, tin, and chromium counters this by strengthening the matrix through solid solution hardening and promoting pearlite formation, thus maintaining high strength while increasing elastic modulus.
The elastic modulus (E) is a critical parameter for material rigidity, defined as the ratio of stress to strain in the elastic region: $$ E = \frac{\sigma}{\epsilon} $$ where \( \sigma \) is stress and \( \epsilon \) is strain. For machine tool castings, a higher E means less elastic deformation under load, contributing to better precision retention. Our data shows that composite alloying significantly boosts E, especially in higher CE irons.
Residual stress relief through heat aging follows the theory of stress relaxation at elevated temperatures. The multistep process allows gradual recovery and redistribution of locked-in stresses without causing new thermal gradients. The effectiveness can be modeled using the Larson-Miller parameter for creep, but empirically, we observe that holding at temperatures like 590°C enables dislocation motion and grain boundary sliding, reducing stresses to below 20 MPa.
Structural stiffness is governed by the geometry of the casting. For a beam-like structure such as a bedway, the deflection \( \delta \) under a uniformly distributed load \( w \) per unit length can be approximated by: $$ \delta = \frac{5 w L^4}{384 E I} $$ where \( L \) is the length, \( E \) is the elastic modulus, and \( I \) is the area moment of inertia. For rectangular sections, \( I = \frac{b h^3}{12} \), with \( b \) as width and \( h \) as thickness. Increasing \( h \) (as in thick guideways) dramatically increases \( I \), reducing deflection. This explains why thick samples exhibited much lower straightness errors, emphasizing the importance of design optimization for machine tool castings.
Long-term precision retention involves time-dependent deformation due to residual stress relaxation and possible creep. The straightness change over time \( \Delta f(t) \) can be described by an exponential decay model: $$ \Delta f(t) = \Delta f_0 \left(1 – e^{-t/\tau}\right) $$ where \( \Delta f_0 \) is the initial potential deformation due to residual stress, and \( \tau \) is a time constant related to material properties. Samples with lower residual stress and higher rigidity have smaller \( \Delta f_0 \) and larger \( \tau \), leading to slower changes, as observed in our experiments.
Comprehensive Comparison and Implications
To consolidate the findings, Table 5 provides a holistic comparison of all sample types based on key metrics relevant to machine tool castings.
| Sample Type | CE Range | Alloying | Heat Aging | Avg. Rm (MPa) | Avg. E (GPa) | Avg. Residual Stress (MPa) | Avg. Straightness (μm) | Precision Retention Rating |
|---|---|---|---|---|---|---|---|---|
| Gray Thin (Low CE) | 3.2-3.4 | None | No | 365 | – | 56.5 | 355.8 | Low |
| Gray Thin (High CE) | 3.6-3.8 | Cu-Sn-Cr | Yes | 346 | – | 18.6 | 383.1 | Medium |
| Gray Thick (Low CE) | 3.2-3.4 | None | No | 357 | 114 | 89.9 | 37.7 | Medium-High |
| Gray Thick (High CE) | 3.8-4.0 | Cu-Sn-Cr | Yes | 322 | 129 | 19.9 | 27.9 | High |
| Ductile Thick (Med CE) | 4.2-4.3 | Mn only | No | 443 | 161 | 108.8 | 19.5 | High |
| Ductile Thick (High CE) | 4.4-4.6 | Cu-Mn-Sn | Yes | 705 | 176 | 26.3 | 17.4 | Very High |
The data clearly indicates that the best precision retention for machine tool castings is achieved with high carbon equivalent, composite alloying, and multistep heat aging, particularly in ductile iron with thick structural designs. Such combinations yield high tensile strength, high elastic modulus, and low residual stress simultaneously.
From a manufacturing perspective, these results suggest that foundries producing machine tool castings should optimize their processes to embrace higher CE levels, judicious alloy additions, and controlled heat treatments. While higher CE might traditionally be associated with reduced strength, our work shows that with proper alloying, strength can be maintained while gaining benefits in stress reduction and stiffness. Moreover, design engineers should prioritize structural stiffness by minimizing wall thickness variations and adding ribs or increasing sections in critical areas, without compromising functionality.
Further research could explore the effects of other alloying elements like molybdenum or nickel on the properties of machine tool castings. Additionally, advanced simulation techniques such as finite element analysis (FEA) could be integrated with these experimental findings to predict long-term deformation under operational loads. The correlation between microstructure features (e.g., graphite nodule count in ductile iron) and precision retention also warrants deeper investigation.
Conclusions
In this study, we have systematically investigated the factors influencing the precision and precision retention of machine tool castings through experimental analysis of gray and ductile iron T-type bedway samples. The key conclusions are as follows:
- Increasing the carbon equivalent in cast irons helps lower the inherent residual stresses in machine tool castings. When combined with composite alloying (e.g., Cu, Sn, Cr), it is possible to achieve high tensile strength alongside high elastic modulus, enhancing material rigidity.
- The multistep rising/falling temperature heat aging process is highly effective in relieving residual stresses, reducing them to below 20 MPa. This minimizes the risk of time-dependent deformation, thereby improving the precision retention of machine tool castings.
- Structural stiffness plays a dominant role in geometric accuracy. Thicker sections with reduced wall thickness differences exhibit significantly lower straightness errors (by an order of magnitude) compared to thin sections, underscoring the importance of optimal design in machine tool castings.
- Ductile iron, with its inherently higher material rigidity, demonstrates superior straightness and precision retention compared to gray iron, especially when processed with high CE, alloying, and heat aging. Sample 6# (high CE, alloyed, aged ductile iron) showed the best overall performance with a straightness of 17.4 μm.
- The synergy of high carbon equivalent, composite alloying, and appropriate heat treatment is a key technological approach to producing high-stiffness, low-stress iron castings. This approach directly contributes to enhanced precision and long-term stability in machine tool castings, which is vital for advancing domestic manufacturing capabilities.
Overall, this research provides a foundation for improving the quality of machine tool castings by addressing material and structural factors. Implementing these findings in industrial practice can lead to more accurate and reliable machine tools, reducing dependency on imports and supporting technological self-sufficiency.