Exploring the Synergistic Impact of Air Gun Cooling and Nanoparticle Application on Milling Surface Roughness

Milling is a critical machining process widely employed in manufacturing, particularly for shaping and finishing high-performance materials such as SKD 11 steel, which is known for its exceptional hardness, wear resistance, and dimensional stability, especially after undergoing heat treatment [1]. This makes SKD 11 steel a popular choice in industries requiring precision and durability, such as mold and die manufacturing [2-4]. However, the inherent hardness of SKD 11 poses significant challenges during machining, including increased tool wear, high cutting forces, and elevated temperatures [5]. For instance, according to Denkena et al. [5], machining hardened steels generates significant heat and cutting forces, which can adversely affect tool life and surface quality [6, 7]. The intense heat generated at the cutting zone not only accelerates tool degradation but also risks compromising the surface integrity and dimensional accuracy of the workpiece [8]. These challenges highlight the necessity for advanced cooling and lubrication strategies, such as Minimum Quantity Cooling Lubrication (MQCL), to manage thermal effects effectively, reduce tool wear, and ensure superior machining efficiency and product quality [9-11].

In traditional machining processes, various cooling and lubrication methods have been employed to mitigate the adverse effects of heat generation and reduce tool wear. Flood cooling, is one of the most common techniques, where large volumes of coolant are applied directly to the cutting zone. While effective in cooling the tool and workpiece, this method consumes a significant amount of coolant, leading to environmental concerns and increased costs, as well as challenges in cleaning and disposal. Additionally, flood cooling may not always provide adequate lubrication, which can contribute to high friction and wear, particularly in hard machining applications like SKD 11 steel [12, 13].

Another widely used technique is conventional minimum quantity lubrication (MQL), where a small amount of lubricant is supplied directly to the cutting area [13, 14]. MQL offers advantages in terms of reduced coolant usage and improved environmental sustainability. However, it still faces limitations in terms of cooling efficiency, particularly when machining high-strength materials such as hardened steels, where heat generation can exceed the cooling capacity of conventional MQL. This can result in tool wear and surface defects, which can impact the overall machining quality and tool life [15, 16].

In some cases, dry machining has been used as an alternative, eliminating the need for any cooling or lubrication altogether. While this method eliminates coolant-related issues, it often leads to increased tool wear, higher temperatures, and poorer surface finish, especially when machining materials with high hardness like SKD 11. Dry machining also fails to prevent built-up edge (BUE) formation, which can further degrade the machining performance and tool life [17].

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Figure 1. Cold air gun from Sunair brand

Among various cooling methods, air gun cooling has gained attention for its effectiveness in reducing heat at the cutting interface through the use of compressed air. The cold air used for cooling was supplied by a cold air gun system, consisting of a vortex tube, a hot muffler, and a cold muffler (Figure 1). The vortex tube separates compressed air into cold and hot streams. Compressed air enters the vortex tube through an inlet, where it is split into two lower-pressure air streams. These streams, one cold and one hot, are then expelled into the surrounding environment through the respective cold and hot mufflers. This technique not only lowers the temperature during milling but also helps prolong tool life by minimizing thermal degradation. Using cold air gun in fine grinding process, Chou et al. [18] observed that the cold air gun cooling system offered superior penetration into the grinding zone, providing effective cooling and lubrication between the grinding wheel and the workpiece surface. It minimized surface damage and facilitates the formation of fine, brittle chips with ease. Stachurski and Nadolny [19] suggested further investigation should focus on improving the lubricating and cooling performance of the MQL-CCA (i.e., minimum quantity lubrication and compressed cold air) method. Singh et al. [20] introduced a system that integrates a minimum quantity lubrication (MQL) setup with a Ranque-Hilsch vortex tube (RHVT) for use in the turning of grade two titanium. Their findings revealed that this combination resulted in a 15–18% improvement in surface roughness compared to the standalone MQL system. Furthermore, reductions in tool wear and cutting force were observed with the use of the MQL and RHVT approach.

Comparatively, advanced cooling methods, such as cryogenic cooling [20] and flood coolant systems, have also been explored in recent years. Cryogenic cooling [21], which utilizes liquid nitrogen, can achieve lower temperatures but may involve higher costs and safety concerns. In addition, EkiCi and Uzun found out that cryogenic treatment was unable to provide a significant difference in terms of cutting force or surface roughness based on grey relational analysis (GRA) method [22]. Flood coolant systems, while effective in heat dissipation, can lead to environmental issues and require extensive clean-up. In contrast, air gun cooling offers a cleaner, more straightforward approach that can be adapted to various machining environments.

As for the lubricating goal, recent researches have highlighted the potential benefits of incorporating nanoparticles into cooling strategies. In a comprehensive review, Kadirgama [23] systematically labeled different type of cutting fluids. The conclusion of the research summarized all the advantages of the nanofluids compared to other applications. Nano-sized Al₂O₃ particles, in particular, have been shown to improve thermal conductivity and reduce friction at the cutting edge, leading to enhanced machining performance. Dong et al. [24] compared the effectiveness of different nanofluids and concluded that Al₂O₃ and SiO₂ not only exhibited superior surface quality and cooling capacity but were also highly suitable as environmentally friendly additives. Various authors have contributed their studies on the performance of nanoparticles [25-28]. On the other hand, Dong et al. [29] introduced a machining setup utilizing a minimum quantity cooling lubrication (MQCL) technique with MoS2 nanofluid, integrated with a Ranque–Hilsch vortex tube, known as the Frigid-X Sub-Zero Vortex Tool Cooling Mist System. The researchers claimed that this setup provided excellent cooling and lubricating capabilities, allowing the use of standard cutters at 2.0 to 2.2 times the cutting speed recommended by the manufacturer. In an effort to enhance machining performance while supporting sustainable production, Celent et al. [30] introduced a compressed cold air cooling (CACC) system, which they referred to as smart manufacturing. This approach was compared to both dry machining (DM) and traditional machining with the use of cutting fluids (CFs). Through extensive experimental research, the CACC system achieved the best surface roughness results. The CACC system was also implemented with a Ranque–Hilsch vortex tube.

The application of cold air in machining has been considered by researchers for a long time [31, 32]. Previous studies have consistently demonstrated its undeniable benefits, including enhanced tool life, improved surface finish, and its role as an environmentally friendly alternative to conventional cooling methods. The combination of cold air cooling with nanoparticle-based minimum quantity lubrication (nano-MQL) presents an optimal approach for machining hard materials, leveraging both effective heat dissipation and superior lubrication. Therefore, further research is needed to explore this synergy in machining specific materials and to investigate the potential of novel nanoparticles and lubricant formulations for enhanced performance. This study investigates the combined effects of using a cold air gun and nano-sized Al₂O₃ particles during the milling of heat-treated SKD 11 steel, a material known for its hardness and wear resistance. Specifically, the research focuses on examining the impact of several key factors on the milling performance, including cooling conditions, nanofluid concentration, flow rate, and applied pressure. These variables are critical in determining the effectiveness of the cooling and lubrication mechanisms, which play a significant role in reducing tool wear and improving surface finish. The study aims to assess how different concentrations of Al₂O₃ nanoparticles, coupled with optimized cooling conditions, can minimize surface roughness, a key performance indicator. By analyzing these variables, the research seeks to identify the optimal parameters that not only enhance the machining quality but also improve the overall efficiency of the milling process. This approach promises to contribute to more effective, sustainable, and cost-efficient machining practices, addressing the challenges of high-performance material processing in modern manufacturing environments.

The experimental results, presented in Table 2, reflect the 18 runs performed according to the Taguchi method. The Taguchi design allowed for the systematic variation of key parameters, such as pressure, flow rate, and nanoparticle concentration, to assess their effects on surface roughness and tool performance during the milling of SKD11 steel. The surface roughness (Ra) ranged from a minimum of 0.141 µm (experiment 15) to a maximum of 0.333 µm (experiment 1). At the lowest Ra (experiment 15), the parameters were: cooling method CL=2 (cold air), nanoparticle concentration C=2% w, flow rate F=100 ml/h, and pressure P=3 kg/cm². Conversely, at the highest Ra (experiment 1), the parameters were: cooling method CL=1 (no cold air), nanoparticle concentration C=0% w, flow rate F=60 ml/h, and pressure P=3 kg/cm².

Main effects plot for SN ratios presented on Figure 3. Applying the cold air gun, highest nanoparticle concentration (4%), highest flow rate (100 ml/h), and lowest pressure (3 kg/cm²) are likely to provide the best surface finish based on this S/N ratio analysis. The unexpected finding that higher pressure decreases surface quality challenges conventional thinking, offering a new avenue of investigation. It suggests that beyond a certain threshold, increasing pressure might induce turbulent flow, disrupting the cooling effect. A new investigation into the dynamics of the flow and pressure in vortex-based systems could lead to better designs that maintain cooling effectiveness without introducing excessive turbulence. This situation also aligns with the findings of Zhang et al. [33], who investigated the effect of nanoparticle concentration on the lubricating properties of nanofluids in MQL grinding of Ni-based alloys. Their study revealed that as the nanoparticle concentration increases beyond an optimal level, the viscosity of the nanofluid rises significantly, leading to diminished lubricating efficiency. This suggests that while increasing concentration initially enhances the cooling and lubricating effects, exceeding a saturation threshold induces adverse effects on machining performance.

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Figure 3. Main effects plot for SN ratios

Table 2. Experimental results

Table 3. Comparison of surface roughness

The optimal conditions obtained from the model, with CL at level 2, C at level 3, F at level 3, and P at level 1, are not included in the experimental table (Table 2). Therefore, a validation experiment needs to be conducted under these optimal conditions to determine the surface roughness. The results are then compared with the minimum surface roughness achieved in all experiments (in this case, experiment 15). The comparison is presented in Table 3. From this comparison, it can be observed that the surface roughness obtained under the identified optimal conditions is smaller than that of experiment 15. This demonstrates the reliability of the model.

Table 4 presents the response table for S/N ratios, which summarizes the effects of different levels of the factors CL, C, F, and P on the response. The table shows the S/N ratios for each factor at three levels. For CL, the S/N ratios at levels 1, 2, and 3 are 11.32, 13.07, and missing (indicated by "-"), respectively, with a delta of 1.74, ranking it fourth in terms of influence. Factor C has the second-lowest S/N ratio at level 1 (10.44), and the highest at level 2 (12.79), yielding a delta of 2.91, ranking it third. Factor F exhibits the highest delta of 3.09, with its S/N ratios at levels 1, 2, and 3 being 10.67, 12.17, and 13.76, respectively, earning it the top rank. Factor P has a delta of 3.08, with its S/N ratios being 13.84 at level 1, 12.00 at level 2, and 10.75 at level 3, ranking it second. This table highlights that factor F has the most significant impact on the response, followed by P, C, and CL.

Table 4. Response table for S/N ratios

Table 5. ANOVA

ANOVA (Table 5) is employed to examine both the individual and combined effects of the input parameters CL, C, F, and P on the output, specifically the surface roughness Ra of the machining process. The p-value in the ANOVA table serves to assess the statistical relevance of the model and the influencing factors. When the p-value is below 0.05, it indicates that the model or a specific factor has significant statistical influence. Additionally, the contribution percentage C (%) of each factor to the overall variation provides insight into the degree of impact that controllable parameters have on the model's response.

The ANOVA table shows that the regression model fits the data very well, with an R-squared value of 95.27%, indicating that the factors C, F, P, and CL explain most of the variation in the data. All of these factors have a significant impact, with a p-value of 0.000, meaning they all strongly affect the dependent variable. Specifically, factor C explains 27.11% of the variation, F accounts for 26.77%, P contributes 25.99%, and CL explains 15.38%. The F-value of the model is 65.40, which is much higher than the critical value, showing that the regression model outperforms random variation and is highly effective in predicting the outcome. These results demonstrate that the factors in the model play a crucial role in improving the research results.

Furthermore, using Minitab software and the experimental data, the second-order regression equations corresponding to the two levels of the CL factor are as follows:

For conditions without the cold air nozzle (CL at level 1):

Ra=0.285−0.0512*C−0.00270*F+0.0779*P+0.00592*C²+0.000004*F²−0.00442*P²+0.000096*C*F−0.00050*C*P

For conditions with the cold air nozzle (CL at level 2):

Ra=0.243−0.0473*C−0.00283*F+0.0766*P+0.00592*C²+0.000004*F²−0.00442*P²+0.000096*C*F−0.00050*C*P

The roles of factors such as flow rate (F), compressed air pressure (P), and nanoparticle concentration (C) in influencing surface roughness (Ra) are clearly demonstrated in Figure 4. The contour plot gradients in Figures 4(a) and 4(b) suggest that no single factor overwhelmingly dominates, indicating that these factors have relatively similar impacts on Ra. This observation aligns with the analysis presented in the ANOVA table (Table 5).

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Figure 4. Plot contour

In Figure 4(a), with nanoparticle concentration (C) and lubrication condition (CL) fixed, increasing the flow rate (F) consistently reduces Ra. This can be explained by the increased flow delivering more nanofluid into the cutting zone, thereby enhancing lubrication and cooling efficiency. Similar findings were reported by Nguyen and Do [4], who observed improved surface finish when nanofluid flow was optimized [13]. Additionally, Figure 4(a) reveals an inverse relationship between pressure (P) and Ra, attributed to excessive pressure causing the cutting fluid to be blown away from the machining zone. This reduces the cooling and lubricating effectiveness, as supported by Tuan et al. [34], who highlighted the challenges of high-pressure MQL setups in open machining environments.

Figure 4(b), with pressure (P) and lubrication condition (CL) fixed, provides an additional insight. It shows that increasing the nanoparticle concentration (C) enhances the lubrication and cooling efficiency, leading to reduced surface roughness. This highlights the effectiveness of nanofluids in improving machining performance. The improved performance stems from the incorporation of nanoparticles, which augment the thermal conductivity and wetting behavior of the cutting fluid [4, 35]. This enhancement facilitates efficient heat dissipation through an improved heat exchange mechanism [36, 37].

Additionally, the lubrication efficiency of nanofluids is explained by four critical mechanisms: the rolling action of nanospheres [38, 39], which reduces friction; the self-repairing effect [40, 41], where nanoparticles fill micro-cracks to restore surfaces; the formation of a protective tribo-film that minimizes wear; and the polishing effect, which smoothens the surface during machining [42]. Collectively, these mechanisms contribute to the superior machining quality achieved with nanofluids compared to traditional cutting fluids.

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Figure 5. Probability plot of Ra

Moreover, the probability plot of Ra in Figure 5 shows that the data points (blue dots) closely follow the straight line, which suggests that the surface roughness values are approximately normally distributed. Although, there are some deviations, particularly near the higher percentiles), but they are not extreme. The red curves represent the 95% confidence intervals. Most data points fall within these boundaries, further supporting the normality assumption.

The scalability of the cooling and lubrication system employed in this study shows promising potential for industrial applications. The modular design of the cold air gun and MQL system allows for flexible adaptation in large-scale CNC machining environments. Economically, the system significantly reduces coolant consumption and disposal costs. While the initial cost of nanoparticles is higher, the long-term benefits in terms of reduced tool wear, improved surface finish, and lower maintenance expenses make this solution cost-effective. Furthermore, the cleaner machining environment aligns with sustainable manufacturing practices, offering potential regulatory and environmental advantages. These factors suggest that the combined cooling and lubrication approach used in this study can be effectively implemented in industrial production settings for enhanced efficiency and sustainability.

The cooling and lubrication system employed in this study offers a sustainable alternative to traditional machining processes. By utilizing a combination of cold air gun cooling and nanoparticle-enhanced MQL, the system significantly reduces the environmental impact associated with coolant usage and disposal. The cleaner machining environment not only supports health and safety standards but also aligns with global trends toward eco-friendly manufacturing practices. The scalability and cost-effectiveness of this approach make it a viable solution for industrial adoption, promoting both operational efficiency and sustainability.

This study provides valuable insights into the combined effects of air gun cooling and nanoparticle-based lubrication on surface roughness during the milling of heat-treated SKD11 steel. Future research could explore the long-term effects of nanoparticle-infused coolants on tool wear and dimensional accuracy to validate the durability and stability of this approach over extended machining operations. Moreover, the development of advanced nanoparticle formulations with superior cooling and lubrication properties holds promise for further enhancing machining performance.