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Article

Understanding the Relationship between Surface Quality and Chip Morphology under Sustainable Cutting Environments

by
Mustafa Günay
and
Mehmet Erdi Korkmaz
*
Department of Mechanical Engineering, Faculty of Engineering, Karabük University, 78050 Karabük, Turkey
*
Author to whom correspondence should be addressed.
Materials 2024, 17(8), 1826; https://doi.org/10.3390/ma17081826
Submission received: 28 March 2024 / Revised: 10 April 2024 / Accepted: 13 April 2024 / Published: 16 April 2024
(This article belongs to the Special Issue Advanced Machining Technology for Modern Engineering Materials (2nd Edition))
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Abstract

:
Although chip morphology changes according to the machining method and related cutting parameters, chip formation affects the quality of the machined surface. In this context, it is very important to understand the relationship between chip morphology and surface quality, especially in materials that are difficult to machine. In the presented study, the changes in chip morphology, surface morphology, and surface quality criteria (Ra and Rz) that occurred during the milling of precipitation-hardened steel in different cutting environments were analyzed. Milling experiments were carried out in dry, MQL (minimum quantity lubrication), nano-MQL (graphene), nano-MQL (hBN), Cryo, and Cryo-MQL environments using TiAlN-coated inserts and three different cutting speeds and feed rates. While the highest values in terms of Ra and Rz were measured in dry machining, the minimum values were obtained in a nano-MQL (hBN) cutting environment. Due to the lubrication and low friction provided by the MQL cutting environment, chips were formed in thinner segmented forms. This formation reduced the chip curve radius and thus provided a more stable surface morphology. On the other hand, Cryo-ambient gas could not effectively leak into the cutting zone due to the intermittent cutting process, but it increased the brittleness of the chips with the cooling effect and provided a similar surface morphology. The values of minimum Ra and Rz were obtained as 0.304 mm and 1.825 mm, respectively, at a 60 m/min cutting speed and 0.04 mm/rev feed. Consequently, the use of nano-MQL cutting medium is seriously recommended in terms of surface quality in milling operations of difficult-to-machine materials.

1. Introduction

PH13-8Mo steel, a material in the precipitation-hardened (PH) stainless steel group, exhibits high strength, good toughness, and high endurance to general corrosion and stress–corrosion cracking [1,2]. This material maintains its mechanical properties considerably even under harsh environmental conditions [3]. On the other side, poor thermal conductivity, extraordinary mechanical properties, ductility, and work hardening make the machinability of materials such as stainless steel difficult [4,5]. For these materials, selecting factors, for example, process parameters, cutting environment, and tool properties, are vital for optimizing machinability indicators such as surface properties, machining force, tool life, and consumed energy [6].
In machining methods with complex cutting mechanics, such as milling, the above-mentioned process becomes even more critical [7,8]. The side and back surfaces of the tool create a large frictional force in up-milling [9,10]. This force not only raises the cutting force and temperature, as well as causing material deformation, but also exacerbates tool wear and worsens the machined surface quality [11,12]. The cutting force is an essential machinability indicator for determining the loads and stress on tool and power consumption, and tool wear and surface integrity deteriorate when high [6,13]. At this point, MQL and nano-MQL cutting environments provide significant positive benefits on machinability outputs such as cutting forces, specific cutting energy, and tool wear compared to dry cutting in turning or milling applications [14,15,16]. This aligns with the nomenclature of the procedure, indicating that it pertains to the MQL method. A nozzle, which is part of an MQL system, is responsible for directing pressurized air into the specific region where the workpiece and the tool are situated, as shown in Figure 1 [17]. On the other hand, some studies stated that during milling in different cryogenic temperatures, cutting forces increase due to the increase in material microhardness, and surface roughness decreases due to the chip formation that varies according to thermo-mechanical combination impact [18,19,20].
Surface roughness is another important indicator, and different roughness patterns occur depending on the parameters of the machining methods [21,22]. In this context, surface roughness is a critical pointer of machined part quality, significantly affecting the components’ operating performance and mechanical properties, namely fatigue and corrosion resistance [23,24]. In particular, surface quality increases due to the narrowing of the tool adhesion zone, due to the lubrication formed in MQL and hybrid cutting environments [25,26]. At the same time, it was pointed out that the effect of the cutting environment may vary depending on the milling technique (up or down), and it has been emphasized that there is a need for optimization of cutting environments and cutting parameters for cleaner machining [27]. However, the surface quality deteriorates due to tool damage such as microfractures and chipping that occur on the cutting tool in the cryogenic environment [28,29]. On the other hand, it is emphasized that MQL + Cryo contributes to sustainable machining for outputs such as cutting force and surface quality in drilling applications [30]. As can be understood from the research in the literature, the effects of MQL, nano-MQL, cryogenic, or hybrid applications formed by combining them on the machinability indicators vary [31]. In this context, optimizing the cutting tool and cutting conditions according to the cutting environment is very important for sustainable machining [32,33]. In addition to these advantages, recycling of chips is also more economical since the chips obtained after sustainable machining are almost oil-free. Despite all these advantages, studies are being carried out to increase the performance of the MQL method, which has been compared with other cooling methods and dry machining [34].
In recent years, MQL, cryogenic cooling, and hybrid cooling/lubrication techniques have been conducted to increase processing efficiency and provide sustainable machining [35,36,37,38]. Sivaiah and Chakradhar [39] performed a turning process for AISI 630 stainless steel with dry, liquid cooling, cryogenic, and MQL methods at different cutting depths and examined tool wear and chip forms. In this paper, the best surface quality was obtained in cryogenic cooling, and the best surface quality after cryogenic cooling was obtained in the MQL method. As in surface quality, the least wear in free surface wear was obtained with cryogenic cooling, and the least wear values after cryogenic cooling were obtained in the MQL method. In their experimental study, Patole and Kulkarni [40] examined the turning of AISI 4340 material at different feed, flow rate, depth of cut, and cutting tip radius values using liquid cooling and the MWCNT (multi-walled carbon nanotube)-added MQL method. It has been stated that when applied correctly, the nano-MQL method provides serious improvements in cutting forces and surface quality and can be used as an alternative to liquid cooling. Marques et al. [41] performed a turning process for the superalloy Inconel 718 alloy with the MQL method by adding graphite and MoS2 into vegetable cutting oil and determined that the addition of graphite and MoS2 reduced the surface roughness. In their study, Paturi et al. [42] conducted a comparison between the MQL and nano-MQL procedures. The MQL method involved the addition of micro-sized WS2 (Tungsten disulfide) to the oil at a weight rate of 0.5% during the turning process of Inconel 718 alloy. The investigation revealed that the incorporation of WS2 into MQL oil resulted in a notable enhancement of surface quality, with an observed improvement of up to 35%. In their work, Rahim et al. [43] conducted a turning investigation on AISI 1045 material using both dry and MQL methods. The researchers employed synthetic ester as the oil in their experiment. The MQL approach has been found to substantially decrease cutting forces, cutting temperature, and tool–chip contact distance in comparison to dry machining. In their study, Kedare et al. [44] conducted a comparison between MQL and liquid cooling methods in the end milling of mold steel. They found that the MQL approach resulted in a significant improvement in surface quality, with an increase of up to 27%. The turning research conducted by Joshi et al. [45] studied the machining of the Inconel600 alloy and employed the dry, MQL, and nano Al2O3 (aluminum oxide) enhanced MQL technique. The nano-MQL approach has been reported to yield superior surface quality values compared to alternative cooling methods.
In light of the literature studies, lots of difficult-to-cut materials show better machinability in cryogenic and hybrid cryogenic MQL environments under different cutting conditions. However, PH13-8Mo steel distinguishes itself from other difficult-to-cut materials due to its exceptional strength, excellent toughness, and remarkable resistance to both general corrosion and stress–corrosion cracking. For that reason, cryogenic cooling may not be effective in improving this material’s machinability. The machinability of PH13-8Mo steel commonly used in the aircraft and nuclear industries poses significant challenges, as seen by the scarcity of research conducted on this particular material. Moreover, the combination of nanoparticle-added MQL (nano-MQL) and cryogenic cooling has not been studied yet. In this context, as a novelty of this study, the variation in surface roughness and chip morphology were comprehensively investigated during up-milling in sustainable cutting environments, namely, dry, MQL, nano-MQL, Cryo, and Cryo-MQL conditions. The originality of this study is to understand the detailed relationship between surface integrity and chip morphology in the cleaner milling of PH13-8Mo steel under sustainable cutting environments. This study will make an important contribution to sustainable machining by filling the gap in the literature.

2. Apparatus and Methods

2.1. Workpiece and Cutting Equipment

In milling operations, PH13-8Mo steel (Bircelik, İstanbul, Turkey) was used as the workpiece, which is the chemical compound of the 110 × 100 × 50 mm workpiece material given in Table 1. The tensile and yield strength, elongation, hardness, and thermal conductivity of PH13-8Mo steel are 1400 MPa, 1300 MPa, 14%, 42 HRC, and 14 W/m-K, respectively. The workpiece material is commonly used in producing valves and shafts operating under various chemical liquids, and substantial components in the aerospace and defense industries, due to its excellent compatibility of superior corrosion and mechanical strength.
TiAlN-coated inserts and a tool holder with a 90° approach angle were used. Up-milling tests were carried out at three different cutting speeds (Vc), three different feed rates (f), and at a constant axial depth, which are the control factors recommended by the cutting insert company (Table 2) under six different cutting environments by full factorial design.
In the milling experiments, the arithmetical roughness value (Ra) and the maximum height of profile (Rz) were measured with a cut-off of 0.8 mm and a sampling length of 4 mm by using the Mahr Perthometer M300 (Mahr, Göttingen, Germany). The roughness measurements were made from three locations, and the Ra was evaluated by averaging the measurements. Moreover, the chips after the machining under each condition were analyzed in CARL ZEISS ULTRA PLUS GEMINI-branded (Zeiss, Jena, Germany) scanning electron microscope (SEM) with a high-resolution FESEM with EDX, in-lens SE, SE2, BSE (EsB), and STEM detectors, and hot stage and load balancer modules at the Karabük University Iron-Steel Institute.

2.2. Cutting Environments

Dry, MQL, nano-MQL (graphene and hBN), Cryo, and Cryo-MQL environments were chosen as sustainable cutting environments, and a machining length of 110 mm was applied to change to new cutting inserts. MQL application was made with the Werte-STN15 lubrication system supplied by SBH Company. Cutting oil named WerteMist with a boiling point of 100 °C and a viscosity of 300 × 10−6 m2/s was sprayed at 5-bar pressure with the MQL system. The spray nozzle was located at a distance of 45 mm, and an angle of 45° to the tool tip, and the cutting fluid was sent to the cutting region at a flow speed of 100 mL/h. Cryo-cooled experiments were performed by a YDS-10 liquid nitrogen (LN2) tank belonging to the Lowtemp brand (Ankara, Turkey). LN2 was applied at 0.5-bar pressure with a 3 mm diameter nozzle.

2.3. Nano-MQL Preparation

The vegetable-based oil was supplemented with solid-state h-BN and graphene nanoparticles (Figure 2), which had an average size ranging from 40 to 50 nm. In order to achieve a uniform dispersion of nanofluid mixtures, a four-step mixing procedure was implemented. Initially, nanoparticles were introduced into the vegetable-based oil at certain volume rates (0.2% by weight) during the mixing process. During the second phase, the mixing process was carried out using a mechanical mixer operating at a speed of 300 revolutions per minute for a duration of 1 h. The third phase involved conducting the mixing process using a magnetic stirrer at a speed of 1000 rpm for a duration of 3 h. In the last stage, nanofluid mixtures exhibiting uniform dispersion of nanoparticles were obtained. In the process of nanofluid preparation, it is possible to incorporate supplementary chemical agents, such as surfactants, into the mixture to mitigate surface tension and achieve a uniform dispersion [46]. Nevertheless, the introduction of these supplementary substances can alter the characteristics of the cutting oil under elevated temperatures, impact its level of purity, and generate foam. The introduction of these substances can also result in a reduction in the thermal conductivity of the mixture due to the generation of thermal resistance [47,48]. The MQL device’s structural design effectively directs nanofluids to the cutting zone during the cooling process, resulting in the production of pressured aerosol mist vapor. In this investigation, no chemical additives were added to the nanofluid mixes due to the adverse effects of surfactants. The schematic representation of the experimental equipment and analysis technique is depicted in Figure 3.

3. Results and Discussion

3.1. Evaluation of Average Surface Roughness (Ra)

The examination of the surface finish of the machined workpiece is of utmost importance due to its influence on the dimensional precision and mechanical characteristics of the workpiece [51,52]. Moreover, the most important surface parameter for surface integrity is the average surface roughness (Ra) [53] as schematically shown in Figure 4.
The major focus of this study is the examination of surface roughness (Ra) across different machining environments, as seen in Figure 5. The results obtained from the conducted studies indicate that the dry machining technique yields the highest recorded surface roughness measurement of 0.804 µm. The subsequent processing techniques include Cryo, MQL, nMQL (graphene), Cryo-MQL, and n-MQL (hBN). The influence of comparing nanofluid with a decrease in Ra and dry to MQL may be observed. The nanofluid confining hBN exhibited enhancements of 33%, 25%, and 21% in comparison to dry, MQL, and nMQL (graphene). hBN nanoparticles possess remarkable thermal conductivity, chemical inertness, and tribological properties, rendering them a compelling candidate for use as a solid lubricant. In recent times, nanoparticles have been employed in the development of nanofluids [55]. hBN, also known as white graphite, is unaffected by the melting of metals that occurs during the cutting procedure. This can be attributed to the chemical stability and elevated melting temperature of the substance. This crystal structure has outstanding lubricating properties and possesses a low coefficient of friction. Based on these aforementioned attributes, it is expected that the system will exhibit enhanced performance under demanding machining conditions due to its increased lubrication and heat conduction properties [56]. Several studies have emphasized the performance-enhancing benefits of hBN additives in machining procedures including MQL [57,58].
Furthermore, as previously stated, the presence of nanoparticles in the lubricant mixture leads to a decrease in friction through the ball-bearing effect [59]. The capacity of nMQL to penetrate the cutting zone while providing lubrication is a crucial factor in enhancing the overall effectiveness of the substance. Furthermore, the utilization of pressured air in the MQL facilitates the seamless elimination of chips from the rake’s surface and the efficient dissipation of heat during the machining process. Furthermore, Moura et al. conducted a study that showed that the incorporation of nanoparticles into vegetable oil resulted in enhanced surface characteristics of the oil [60]. Moreover, it has been posited that the utilization of nanofluids enables the enhancement of surface quality, hence resulting in an amelioration of heat dissipation and cooling/lubricating characteristics. It is imperative to acknowledge that the incorporation of nanoparticles into a base fluid at a certain concentration has similar significance. However, it is crucial to acknowledge that the heightened concentration also impacts the lubricant mixture’s capacity to permeate [61]. Previous research has also demonstrated that exceeding a specific threshold of nanoparticle incorporation leads to a negative consequence, such as the formation of clusters at the interfaces of the machining process. This has been determined to be true [62]. Therefore, it is crucial to determine an appropriate nanoparticle concentration by evaluating thermo-physical characteristics [63].
On the other hand, nMQL (hBN) presented improvements of 26% and 10%, respectively, compared to Cryo and Cryo-MQL conditions. The improved outcomes found in nano-doped cutting oil, as compared to cryogenic cooling approaches, may be attributed to the lubricating properties of the cutting oil. The manifestation of this problem has been more apparent since the advent of solid lubricants. Solid lubricants have a stratified composition that functions as a lubricating agent. The aforementioned structure is associated with the inherent lubricating properties exhibited by solid lubricants. Even under elevated temperatures, the aforementioned structure is accountable for generating these characteristics. Furthermore, when comparing cryogenic cooling to cutting oils, it is seen that cutting oils exhibit a greater level of wettability. This characteristic leads to the formation of a film layer, hence reducing friction [64,65]. The overall results show that nano-MQL with hBN particles which are safe for operators provide the best surface quality. This approach can be considered a more sustainable method than the other lubrication and cryogenic methods since even a smaller amount of hBN (~0.2% by weight) nanoparticles increases the cooling and lubrication capacity more than the others, and it is also a cost-effective process.

3.2. Evaluation of Maximum Height of Surface Profile (Rz)

The other important parameter of surface integrity is the maximum height of the surface profile (Rz). For that reason, the study also focuses on the analysis of Rz in various machining situations, as shown in Figure 6.
This study also aims at the analysis of Rz in various machining situations as shown in Figure 7. From the experimental results, the dry machining process has the maximum Rz measurement of 6.204 µm, followed by Cryo, MQL, n-MQL (graphene), Cryo-MQL, and n-MQL (hBN). The comparison between nanofluids with a decrease in Ra and dry and MQL reveals the observed influence. Compared to dry, MQL, and n-MQL (graphene), the nanofluid containing hBN exhibited improvements of 52%, 24%, and 17%, respectively. On the other hand, nMQL (hBN) presented improvements of 33% and 9%, respectively, compared to Cryo, and Cryo-MQL conditions. The effectiveness of nMQL can be attributed to its ability to penetrate the cutting zone with lubrication. Moreover, the improved outcomes seen in nano-doped cutting oil compared to cryogenic cooling processes can be accredited to the lubrication superiority of the lubricants as also stated by Ra previously [64,65].

3.3. Surface Morphology

The analysis and presentation of surface images generated under certain machining circumstances are likewise depicted in Figure 8. The surface finish obtained during machining determines the tribological parameters of the machined workpiece [66,67]. Surfaces generated with dry machining exhibit elevated levels of machining marks and scratches [68,69]. As anticipated, the process of dry machining led to significant heat production in the absence of cooling and lubrication. The offered comparison confirms that rapid tool wear led to higher Ra and Rz values. It has been found that the utilization of nanofluid-MQL (consisting of graphene and hBN) resulted in a significant reduction in machining marks on the surface of the workpiece, leading to the production of a smoother surface in comparison to both dry and MQL conditions. The layered structure of graphene and hBN is responsible for this phenomenon. The presence of these sliding layers decreases friction, resulting in improved surface topography. Şirin and Kıvak [70] made similar discoveries while utilizing different nanoparticles to perform the machining of nickel-based superalloy. They found that these materials resulted in improved surface topographies when nanofluids were applied. In addition, the utilization of nanofluid-MQL resulted in the effective cooling effect of MQL, which prevented the work material from sticking, simplified the chip removal, and decreased the friction, resulting in a higher surface quality [71].
Nevertheless, the application of nanoadditives coated with cutting oil and their subsequent injection into nanofluids containing hBN nanoplatelets results in the generation of a thin layer of fog that is atomized along the nozzle. This phenomenon stands in contrast to the preceding events. Consequently, it may rapidly reach the interface between the tool and the workpiece, leading to a significant enhancement in tribological properties by forming a thin film layer at that specific location [72]. During the hard-turning process, the resulting surface exhibits an increased level of anisotropy. The anisotropic-turned layer is characterized by its ability to exhibit a symmetrical and regular arrangement of peaks and valleys, setting it apart from the other layers. The evaluation of surface topography has significant importance due to its direct impact on the performance of component structures, including factors such as friction, wear, fatigue, and sealing [73]. The studies employing cryogenic freezing demonstrated a notable increase in surface stability compared to the dry condition, as seen in Figure 8. The correlation between surface quality and lubrication is evident in this specific occurrence, providing compelling proof of this relationship. The unique cooling system, referred to as cryogenic cooling, is characterized by its dual functionality of cooling and restricted lubrication. The surfaces created by cryogenic cooling exhibited a higher level of irregularity, roughness, and worse quality in comparison to those obtained using nanofluid methods. Research has demonstrated that the use of lubrication significantly mitigates the occurrence of cracks, excessive feed lines, and residue on the treated surface [74]. The investigations performed utilizing nanofluid revealed a more consistent peak-to-valley height. Conversely, it was discovered that the surfaces resulting from cryogenic cooling exhibited more complexity, irregularity, and surface damage.

3.4. Chip Morphology

Significant changes in chip morphology (color, shape, serration, etc.) reflect the quality of operations in machinability as also stated by Kouam et al. [75]. Obtaining minimal tool wear and good surface quality seems likely to be directly related to chip morphology [76,77]. Another issue regarding the structure of chips is their ability to dissipate heat from the cutting zone. Regular chip formation is important for better surface quality and minimum tool wear on the processed parts [78]. Cutting parameters for regular chip formation can include the chip removal mechanism, cutting geometry, and cooling/lubrication strategies. Therefore, it is necessary to determine the chip properties in order to determine the effect of all these effective parameters. Figure 9 shows SEM images displaying the influences of various cooling and lubrication environments on chip morphology. It has been seen in the studies carried out by Şap [8] that cutting speeds do not have a visible effect on chip morphology. For this reason, only chips taken from different cooling/lubrication environments were examined. It can be seen that the chips resulting from machining in a dry environment are larger and flatter than the chips formed during machining with MQL. It was observed that the chips formed in the MQL environment were smaller and more curved. The color of chips formed in a dry environment looks different than other chips due to higher temperatures. It can be said that the chips formed in the MQL environment fall into the group of chips defined as spontaneous breaking. This situation can be called a good environment from a tribological point of view [79]. The investigation of chip shapes generated during the process of machining is of utmost importance as it offers valuable perceptions of the underlying mechanism of chip formation. The attributes of the chip removed during the machining process serve as indicators of the impact of cooling and lubrication, hence providing insights into the quality of the machining process [80]. The chip morphology is influenced by the materials used for the tool and workpiece, the circumstances under which the machining takes place, and the parameters applied for cooling and lubrication, which mostly impact the chip morphology [81]. Multiple studies have documented the examination of chip morphology in relation to different cooling and lubrication settings [82]. In the context of machining, there is a preference for shorter and fragmented chips due to their potential to decrease surface roughness. The study examines the structures of the chips generated inside the machining settings under consideration, as seen in Figure 9.
The softening of the material in dry machining, caused by increased heat generation, can be attributed to the formation of tangled, continuous, and irregular chips. Dry machining leads to a lack of cooling, resulting in increased contact duration and friction between the chip and the tool. This ultimately leads to the uneven distribution of chips. The MQL process involves the generation of segmented fragmented chips, whereas the nMQL machining process demonstrates a decrease in the number of contact zones between the chip and the tool. The observed reduction in the curvature radius of the chips generated suggests that the use of nMQL led to enhanced lubrication. The results align with earlier research, indicating that chips with a reduced curling radius exhibit superior penetration and lubrication when combined with MQL and nMQL. This observation is consistent with the aforementioned results [83]. Darshan et al. [84] also observed comparable outcomes while employing continuous helical chips in dry machining, as well as tiny fragmented chips with hBN-aided MQL. These findings indicated improved lubrication and a decreased likelihood of built-up edge development. Moreover, an analysis is conducted on the chip morphology generated under various machining settings. The production of serrated chips is observed across various machining settings, as depicted in Figure 9. Under dry machining, the chips exhibited maximal serrations due to increased friction between the chip and tool contact and plastic deformation (Figure 9a). Conversely, research has demonstrated that chips with a small degree of serration had improved heat transfer from the machining area and reduced frictional impacts at the interface between the chip and the tool when exposed to MQL and nMQL. The chip surface was broken by the MQL application, resulting in the formation of an uneven lamella due to increased friction, as seen in Figure 9b. The utilization of MQL and nanoparticles has been seen to have a substantial effect on the regulation of friction at the interface between the chip and the tool. Chips exhibiting a fine lamella structure are generated with the utilization of nano-MQL (as seen in Figure 9c,d), owing to the concurrent decrease in friction and temperature. The application of nMQL results in enhanced chip morphology due to the efficient penetration of cutting fluid containing nanoparticles between chip–tool contacts, leading to a reduction in friction. Airao et al. [85] observed comparable findings in their study on conventional turning conducted under various circumstances, including dry, wet, MQL, and LCO2. In the context of dry machining, chips exhibiting non-uniform fractures were acquired, while chips with cracked surfaces were detected during MQL conditions.
However, the chip surfaces exhibit a higher degree of smoothness in the experimental trials that incorporate cryogenic cooling. The chip notch tooth rate exhibits an upward trend with increasing cutting speed in cryogenic cooling, indicating a higher level of efficiency compared to nanofluid (Figure 9). There exists a correlation between this particular situation and the efficacy of cryogenic cooling in delivering sufficient cooling. The utilization of cryogenic cooling, known for its efficacy in delivering efficient cooling during high-speed operations, played a pivotal role in delaying the deterioration of the cutting tool and facilitating the successful culmination of the cutting procedure. Prior studies have demonstrated that this situation exhibits similarities. Bermingham et al. [86] posited that the utilization of cryogenic cooling had favorable outcomes in terms of chip morphology. Ross et al. [87] found that cryogenic cooling improved the chip’s ability to break. The operational characteristics of cooling systems differ according to their inherent nature. In the case of nanofluid, the spray pressure was measured to be 8 bar, but the spray pressure for LN2 was found to be 15 bar. The chip’s susceptibility to breakage may have been heightened due to the application of elevated pressure within the cryogenic system, perhaps yielding a marginally advantageous outcome.

4. Conclusions and Recommendations

The study conducted an analysis of the alterations in chip morphology, surface morphology, and surface quality criteria (Ra and Rz) that took place during the milling process of precipitation-hardened steel (PH13-8Mo) under various cutting conditions. The milling studies were conducted in several settings, including dry, MQL, nano-MQL (graphene), nano-MQL (hBN), Cryo, and Cryo-MQL. TiAlN-coated inserts were used, and three different cutting speeds and feed rates were employed. The summarized results are indicated below:
  • Comparisons between nanofluid with reduced Ra and dry and MQL show that the nanofluid with hBN outperformed dry, MQL, and nano-MQL (graphene) by 33%, 25%, and 21%, respectively. Moreover, nano-MQL (hBN) improved by 26% and 10% over Cryo and Cryo-MQL, respectively.
  • Differences between nanofluid with reduced Rz and dry and MQL display the effect that the nanofluid with hBN outperformed dry, MQL, and nano-MQL by 52%, 24%, and 17%. Additionally, the nano-MQL (hBN) showed 33% and 9% improvements over the Cryo and Cryo-MQL conditions, respectively.
  • The ability of nMQL to infiltrate the cutting zone while simultaneously providing lubrication is a critical aspect in improving the overall efficiency of the drug. In addition, the use of compressed air in the MQL system allows for the effective removal of chips from the rake face and the efficient dispersal of heat during the machining process. Furthermore, the enhanced surface quality results observed in nano-doped cutting oil, in comparison to cryogenic cooling methods, can be ascribed to the lubricating characteristics of the cutting oil. The emergence of solid lubricants has made this problem more evident. Solid lubricants have a layered structure that serves as a lubricant.
  • In cryogenic cooling experiments, chip surfaces are smoother, and the chip notch tooth rate increases with the cutting speed, suggesting more efficiency than nanofluid. Cryogenic cooling’s ability to cool is attributed to the current situation. Cryogenic cooling, known for its efficiency in quick operations, was essential to keeping the cutting tool from degrading and completing the cutting.

Author Contributions

Conceptualization, M.E.K.; Methodology, M.G.; Investigation, M.G. and M.E.K.; Data curation, M.G.; Writing—original draft, M.E.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data used in this work can be requested by contacting the first author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Rosenauer, A.; Brandl, D.; Ressel, G.; Lukas, S.; Monschein, S.; Stockinger, M.; Schnitzer, R. Influence of Delta Ferrite on the Impact Toughness of a PH 13-8 Mo Maraging Steel. Mater. Sci. Eng. A 2022, 856, 144024. [Google Scholar] [CrossRef]
  2. Yaşar, S.A.; Uzun, G.; Korkut, İ. Investigation of the Effects of Different Coating Types on Cutting Forces in Turning of 17-4 PH Stainless Steels. Nevsehir J. Sci. Technol. 2019, 8, 1–11. [Google Scholar] [CrossRef]
  3. Öndin, O.; Kıvak, T.; Sarıkaya, M.; Yıldırım, Ç.V. Investigation of the Influence of MWCNTs Mixed Nanofluid on the Machinability Characteristics of PH 13-8 Mo Stainless Steel. Tribol. Int. 2020, 148, 106323. [Google Scholar] [CrossRef]
  4. Mohanty, A.; Gangopadhyay, S.; Thakur, A. On Applicability of Multilayer Coated Tool in Dry Machining of Aerospace Grade Stainless Steel. Mater. Manuf. Process. 2016, 31, 869–879. [Google Scholar] [CrossRef]
  5. Yaşar, S.A.; Uzun, G.; Korkut, İ. Investigation of the Effect of Cutting Parameters on Cutting Force and Surface Roughness in Turning of 17-4 PH and 15-5 PH Stainless Steels. Karaelmas Sci. Eng. J. 2020, 10, 71–81. [Google Scholar] [CrossRef]
  6. Günay, M. Modeling and Multiple Optimization in Face Milling of Hardfacing Welding Applied Steel: Force, Roughness, Power. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 2022, 236, 6652–6664. [Google Scholar] [CrossRef]
  7. Şap, S. Understanding the Machinability and Energy Consumption of Al-Based Hybrid Composites under Sustainable Conditions. Lubricants 2023, 11, 111. [Google Scholar] [CrossRef]
  8. Usca, Ü.A.; Uzun, M.; Şap, S.; Kuntoğlu, M.; Giasin, K.; Pimenov, D.Y.; Wojciechowski, S. Tool Wear, Surface Roughness, Cutting Temperature and Chips Morphology Evaluation of Al/TiN Coated Carbide Cutting Tools in Milling of Cu–B–CrC Based Ceramic Matrix Composites. J. Mater. Res. Technol. 2022, 16, 1243–1259. [Google Scholar] [CrossRef]
  9. Uzun, G.; Çakıroğlu, R. Investigation of Machinability Parameters in High-Feed Milling Process. Manuf. Technol. Appl. 2020, 1, 34–41. [Google Scholar]
  10. Ekici, E.; Uzun, G. Effects on Machinability of Cryogenic Treatment Applied to Carbide Tools in the Milling of Ti6AI4V with Optimization via the Taguchi Method and Grey Relational Analysis. J. Brazilian Soc. Mech. Sci. Eng. 2022, 44, 270. [Google Scholar] [CrossRef]
  11. Bai, X.; Li, C.; Dong, L.; Yin, Q. Experimental Evaluation of the Lubrication Performances of Different Nanofluids for Minimum Quantity Lubrication (MQL) in Milling Ti-6Al-4V. Int. J. Adv. Manuf. Technol. 2019, 101, 2621–2632. [Google Scholar] [CrossRef]
  12. Binali, R. Parametric Optimization of Cutting Force and Temperature in Finite Element Milling of AISI P20 Steel. J. Mater. Mechatron. A 2023, 4, 244–256. [Google Scholar] [CrossRef]
  13. Płodzień, M.; Żyłka, Ł.; Sułkowicz, P.; Żak, K.; Wojciechowski, S. High-Performance Face Milling of 42crmo4 Steel: Influence of Entering Angle on the Measured Surface Roughness, Cutting Force and Vibration Amplitude. Materials 2021, 14, 2196. [Google Scholar] [CrossRef] [PubMed]
  14. Çamlı, K.Y.; Demirsöz, R.; Boy, M.; Korkmaz, M.E.; Yaşar, N.; Giasin, K.; Pimenov, D.Y. Performance of MQL and Nano-MQL Lubrication in Machining ER7 Steel for Train Wheel Applications. Lubricants 2022, 10, 48. [Google Scholar] [CrossRef]
  15. Korkmaz, M.E.; Gupta, M.K.; Yilmaz, H.; Ross, N.S.; Boy, M.; Sivalingam, V.K.; Chan, C.K.; Raman, J. Towards Specific Cutting Energy Analysis in the Machining of Inconel 601 Alloy under Sustainable Cooling Conditions. J. Mater. Res. Technol. 2023, 27, 4074–4087. [Google Scholar] [CrossRef]
  16. Ross, N.S.; Gopinath, C.; Nagarajan, S.; Gupta, M.K.; Shanmugam, R.; Kumar, M.S.; Boy, M.; Korkmaz, M.E. Impact of Hybrid Cooling Approach on Milling and Surface Morphological Characteristics of Nimonic 80A Alloy. J. Manuf. Process. 2022, 73, 428–439. [Google Scholar] [CrossRef]
  17. Pervaiz, S.; Ahmad, N.; Ishfaq, K.; Khan, S.; Deiab, I.; Kannan, S. Implementation of Sustainable Vegetable-Oil-Based Minimum Quantity Cooling Lubrication (MQCL) Machining of Titanium Alloy with Coated Tools. Lubricants 2022, 10, 235. [Google Scholar] [CrossRef]
  18. Zhao, W.; Ren, F.; Iqbal, A.; Gong, L.; He, N.; Xu, Q. Effect of Liquid Nitrogen Cooling on Surface Integrity in Cryogenic Milling of Ti-6Al-4 V Titanium Alloy. Int. J. Adv. Manuf. Technol. 2020, 106, 1497–1508. [Google Scholar] [CrossRef]
  19. Gong, L.; Zhao, W.; Ren, F.; He, N.; Li, L.; Xu, Q.; Khan, A.M. Experimental Study on Surface Integrity in Cryogenic Milling of 35CrMnSiA High-Strength Steel. Int. J. Adv. Manuf. Technol. 2019, 103, 605–615. [Google Scholar] [CrossRef]
  20. Binali, R.; Patange, A.D.; Kuntoğlu, M.; Mikolajczyk, T.; Salur, E. Energy Saving by Parametric Optimization and Advanced Lubri-Cooling Techniques in the Machining of Composites and Superalloys: A Systematic Review. Energies 2022, 15, 8313. [Google Scholar] [CrossRef]
  21. Çakıroğlu, R.; Günay, M. Comprehensive Analysis of Material Removal Rate, Tool Wear and Surface Roughness in Electrical Discharge Turning of L2 Tool Steel. J. Mater. Res. Technol. 2020, 9, 7305–7317. [Google Scholar] [CrossRef]
  22. Ross, N.S.; Rai, R.; Ananth, M.B.J.; Srinivasan, D.; Ganesh, M.; Gupta, M.K.; Korkmaz, M.E.; Królczyk, G.M. Carbon Emissions and Overall Sustainability Assessment in Eco-Friendly Machining of Monel-400 Alloy. Sustain. Mater. Technol. 2023, 37, e00675. [Google Scholar] [CrossRef]
  23. Panwar, V.; Kumar Sharma, D.; Pradeep Kumar, K.V.; Jain, A.; Thakar, C. Experimental Investigations and Optimization of Surface Roughness in Turning of En 36 Alloy Steel Using Response Surface Methodology and Genetic Algorithm. Mater. Today Proc. 2020, 46, 6474–6481. [Google Scholar] [CrossRef]
  24. Sivalingam, V.; Zhou, Q.; Selvam, B.; Sun, J.; Pandiyan, K.; Gupta, M.K.; Korkmaz, M.E. A Mathematical Approach of Evaluating Sustainability Indicators in Milling of Aluminium Hybrid Composite by Different Eco-Friendly Cooling Strategies. Sustain. Mater. Technol. 2023, 36, e00605. [Google Scholar] [CrossRef]
  25. Seid Ahmed, Y.; Ryon, A. Tribological Performance of a Hybrid CryoMQL System on Ti6Al4V Milling. Int. J. Adv. Manuf. Technol. 2022, 120, 8185–8199. [Google Scholar] [CrossRef]
  26. Ross, N.S.; Srinivasan, N.; Amutha, P.; Gupta, M.K.; Korkmaz, M.E. Thermo-Physical, Tribological and Machining Characteristics of Hastelloy C276 under Sustainable Cooling/Lubrication Conditions. J. Manuf. Process. 2022, 80, 397–413. [Google Scholar] [CrossRef]
  27. Ross, N.S.; Mashinini, P.M.; Sherin Shibi, C.; Kumar Gupta, M.; Erdi Korkmaz, M.; Krolczyk, G.M.; Sharma, V.S. A New Intelligent Approach of Surface Roughness Measurement in Sustainable Machining of AM-316L Stainless Steel with Deep Learning Models. Measurement 2024, 230, 114515. [Google Scholar] [CrossRef]
  28. Park, K.H.; Suhaimi, M.A.; Yang, G.D.; Lee, D.Y.; Lee, S.W.; Kwon, P. Milling of Titanium Alloy with Cryogenic Cooling and Minimum Quantity Lubrication (MQL). Int. J. Precis. Eng. Manuf. 2017, 18, 5–14. [Google Scholar] [CrossRef]
  29. Gupta, M.K.; Niesłony, P.; Korkmaz, M.E.; Kuntoğlu, M.; Królczyk, G.M.; Günay, M.; Sarikaya, M. Comparison of Tool Wear, Surface Morphology, Specific Cutting Energy and Cutting Temperature in Machining of Titanium Alloys Under Hybrid and Green Cooling Strategies. Int. J. Precis. Eng. Manuf. Green Technol. 2023, 10, 1393–1406. [Google Scholar] [CrossRef]
  30. Sivalingam, V.; Liu, H.; Selvam, B.; Kai, G.; Kumar, P.G.; Gupta, M.K.; Korkmaz, M.E.; Sun, J. Towards Sustainability Assessment, Energy Consumption, and Carbon Emissions in Cryogenic Drilling of Alloy 20: A New Approach towards Sustainable Future and Challenges. Int. J. Adv. Manuf. Technol. 2024, 131, 1151–1165. [Google Scholar] [CrossRef]
  31. Kuntoğlu, M.; Kaya, K.; Binali, R. Investigation of Surface Roughness Changes in The Machining of Carbon Steel under Sustainable Conditions. Int. Conf. Pioneer Innov. Stud. 2023, 1, 163–167. [Google Scholar] [CrossRef]
  32. Şap, S. Machining and Energy Aspect Assessment with Sustainable Cutting Fluid Strategies of Al–12Si Based Hybrid Composites. Int. J. Precis. Eng. Manuf. Technol. 2024, 11, 33–53. [Google Scholar] [CrossRef]
  33. Şap, S.; Usca, Ü.A.; Uzun, M.; Kuntoğlu, M.; Salur, E.; Pimenov, D.Y. Investigation of the Effects of Cooling and Lubricating Strategies on Tribological Characteristics in Machining of Hybrid Composites. Lubricants 2022, 10, 63. [Google Scholar] [CrossRef]
  34. Çakıroğlu, R.; Günay, M. Analysis of Surface Roughness and Energy Consumption in Turning of C17500 Copper Alloy under Different Machining Environments and Modellings with Response Surface Method. Proc. Inst. Mech. Eng. Part E J. Process Mech. Eng. 2022, 237, 095440892211013. [Google Scholar] [CrossRef]
  35. Şirin, E.; Şirin, Ş. Investigation of the Performance of Ecological Cooling/Lubrication Methods in the Milling of AISI 316L Stainless Steel. Manuf. Technol. Appl. 2021, 2, 75–84. [Google Scholar]
  36. Manimaran, G.; Anwar, S.; Rahman, M.A.; Korkmaz, M.E.; Gupta, M.K.; Alfaify, A.; Mia, M. Investigation of Surface Modification and Tool Wear on Milling Nimonic 80A under Hybrid Lubrication. Tribol. Int. 2021, 155, 106762. [Google Scholar] [CrossRef]
  37. Çakıroğlu, R. Machinability Analysis of Inconel 718 Superalloy with AlTiN-Coated Carbide Tool under Different Cutting Environments. Arab. J. Sci. Eng. 2021, 46, 8055–8073. [Google Scholar] [CrossRef]
  38. Korkmaz, M.E.; Gupta, M.K.; Çelik, E.; Ross, N.S.; Günay, M. A Sustainable Cooling/Lubrication Method Focusing on Energy Consumption and Other Machining Characteristics in High-Speed Turning of Aluminum Alloy. Sustain. Mater. Technol. 2024, 40, e00919. [Google Scholar] [CrossRef]
  39. Sivaiah, P.; Chakradhar, D. Effect of Cryogenic Coolant on Turning Performance Characteristics during Machining of 17-4 PH Stainless Steel: A Comparison with MQL, Wet, Dry Machining. CIRP J. Manuf. Sci. Technol. 2018, 21, 86–96. [Google Scholar] [CrossRef]
  40. Patole, P.B.; Kulkarni, V.V. Optimization of Process Parameters Based on Surface Roughness and Cutting Force in MQL Turning of AISI 4340 Using Nano Fluid. Mater. Today Proc. 2018, 5, 104–112. [Google Scholar] [CrossRef]
  41. Marques, A.; Guimarães, C.; da Silva, R.B.; da Penha Cindra Fonseca, M.; Sales, W.F.; Machado, Á.R. Surface Integrity Analysis of Inconel 718 after Turning with Different Solid Lubricants Dispersed in Neat Oil Delivered by MQL. Procedia Manuf. 2016, 5, 609–620. [Google Scholar] [CrossRef]
  42. Paturi, U.M.R.; Maddu, Y.R.; Maruri, R.R.; Narala, S.K.R. Measurement and Analysis of Surface Roughness in WS2 Solid Lubricant Assisted Minimum Quantity Lubrication (MQL) Turning of Inconel 718. Procedia CIRP 2016, 40, 138–143. [Google Scholar] [CrossRef]
  43. Fernando, W.L.R.; Sarmilan, N.; Wickramasinghe, K.C.; Herath, H.M.C.M.; Perera, G.I.P. Experimental Investigation of Minimum Quantity Lubrication (MQL) of Coconut Oil Based Metal Working Fluid. Mater. Today Proc. 2020, 23, 23–26. [Google Scholar] [CrossRef]
  44. Kedare, S.B.; Borse, D.R.; Shahane, P.T. Effect of Minimum Quantity Lubrication (MQL) on Surface Roughness of Mild Steel of 15HRC on Universal Milling Machine. Procedia Mater. Sci. 2014, 6, 150–153. [Google Scholar] [CrossRef]
  45. Kishore Joshi, K.; Behera, R.K. Anurag Effect of Minimum Quantity Lubrication with Al2O3 Nanofluid on Surface Roughness and Its Prediction Using Hybrid Fuzzy Controller in Turning Operation of Inconel 600. Mater. Today Proc. 2018, 5, 20660–20668. [Google Scholar] [CrossRef]
  46. Devendiran, D.K.; Amirtham, V.A. A Review on Preparation, Characterization, Properties and Applications of Nanofluids. Renew. Sustain. Energy Rev. 2016, 60, 21–40. [Google Scholar] [CrossRef]
  47. Babita; Sharma, S.K.; Gupta, S.M. Preparation and Evaluation of Stable Nanofluids for Heat Transfer Application: A Review. Exp. Therm. Fluid Sci. 2016, 79, 202–212. [Google Scholar] [CrossRef]
  48. Bandarra Filho, E.P.; Mendoza, O.S.H.; Beicker, C.L.L.; Menezes, A.; Wen, D. Experimental Investigation of a Silver Nanoparticle-Based Direct Absorption Solar Thermal System. Energy Convers. Manag. 2014, 84, 261–267. [Google Scholar] [CrossRef]
  49. Dadvand, M.; Savadogo, O. Effect of HBN on Corrosion and Wear Performances of DC Electrodeposited NiW and NiW–SiC on Brass Substrates. Coatings 2022, 12, 1011. [Google Scholar] [CrossRef]
  50. Kausar, A. Avant-Garde Polymer and Nano-Graphite-Derived Nanocomposites—Versatility and Implications. C 2023, 9, 13. [Google Scholar] [CrossRef]
  51. Kivak, T.; Uzun, G.; Ekici, E. An Experimental and Statistical Evaluation of the Cutting Parameters on the Machinability of Hadfield Steel. Gazi Univ. J. Sci. 2016, 29, 9–17. [Google Scholar]
  52. Ekici, E.; Motorcu, A.R.; Uzun, G. An Investigation of the Effects of Cutting Parameters and Graphite Reinforcement on Quality Characteristics during the Drilling of Al/10B4C Composites. Measurement 2017, 95, 395–404. [Google Scholar] [CrossRef]
  53. Korkmaz, M.E.; Günay, M. Experimental and Statistical Analysis on Machinability of Nimonic80A Superalloy with PVD Coated Carbide. Sigma J. Eng. Nat. Sci. 2018, 36, 1141–1152. [Google Scholar]
  54. Mínguez-Martínez, A.; Maresca, P.; Caja, J.; Oliva, J.D.V.Y. Results of a Surface Roughness Comparison between Stylus Instruments and Confocal Microscopes. Materials 2022, 15, 5495. [Google Scholar] [CrossRef] [PubMed]
  55. Şirin, Ş.; Sarıkaya, M.; Yıldırım, Ç.V.; Kıvak, T. Machinability Performance of Nickel Alloy X-750 with SiAlON Ceramic Cutting Tool under Dry, MQL and HBN Mixed Nanofluid-MQL. Tribol. Int. 2021, 153, 106673. [Google Scholar] [CrossRef]
  56. Yıldırım, Ç.V.; Sarıkaya, M.; Kıvak, T.; Şirin, Ş. The Effect of Addition of HBN Nanoparticles to Nanofluid-MQL on Tool Wear Patterns, Tool Life, Roughness and Temperature in Turning of Ni-Based Inconel 625. Tribol. Int. 2019, 134, 443–456. [Google Scholar] [CrossRef]
  57. Sarıkaya, M.; Şirin, Ş.; Yıldırım, Ç.V.; Kıvak, T.; Gupta, M.K. Performance Evaluation of Whisker-Reinforced Ceramic Tools under Nano-Sized Solid Lubricants Assisted MQL Turning of Co-Based Haynes 25 Superalloy. Ceram. Int. 2021, 47, 15542–15560. [Google Scholar] [CrossRef]
  58. Kannan, V. Machining of Different Superalloys under MQL Using HBN Nanofluids. Adv. Mater. Process. Technol. 2023, 9, 1775–1810. [Google Scholar] [CrossRef]
  59. Rapoport, L.; Leshchinsky, V.; Lvovsky, M.; Nepomnyashchy, O.; Volovik, Y.; Tenne, R. Mechanism of Friction of Fullerenes. Ind. Lubr. Tribol. 2002, 54, 171–176. [Google Scholar] [CrossRef]
  60. Moura, R.R.; da Silva, M.B.; Machado, Á.R.; Sales, W.F. The Effect of Application of Cutting Fluid with Solid Lubricant in Suspension during Cutting of Ti-6Al-4V Alloy. Wear 2015, 332–333, 762–771. [Google Scholar] [CrossRef]
  61. Sayuti, M.; Sarhan, A.A.D.; Salem, F. Novel Uses of SiO2 Nano-Lubrication System in Hard Turning Process of Hardened Steel AISI4140 for Less Tool Wear, Surface Roughness and Oil Consumption. J. Clean. Prod. 2014, 67, 265–276. [Google Scholar] [CrossRef]
  62. Sharma, A.K.; Tiwari, A.K.; Dixit, A.R. Progress of Nanofluid Application in Machining: A Review. Mater. Manuf. Process. 2015, 30, 813–828. [Google Scholar] [CrossRef]
  63. Etri, H.E.L.; Singla, A.K.; Özdemir, M.T.; Korkmaz, M.E.; Demirsöz, R.; Gupta, M.K.; Krolczyk, J.B.; Ross, N.S. Wear Performance of Ti-6Al-4 V Titanium Alloy through Nano-Doped Lubricants. Arch. Civ. Mech. Eng. 2023, 23, 147. [Google Scholar] [CrossRef]
  64. Jamil, M.; Khan, A.M.; Hegab, H.; Gong, L.; Mia, M.; Gupta, M.K.; He, N. Effects of Hybrid Al2O3-CNT Nanofluids and Cryogenic Cooling on Machining of Ti–6Al–4V. Int. J. Adv. Manuf. Technol. 2019, 102, 3895–3909. [Google Scholar] [CrossRef]
  65. Yıldırım, Ç.V. Investigation of Hard Turning Performance of Eco-Friendly Cooling Strategies: Cryogenic Cooling and Nanofluid Based MQL. Tribol. Int. 2020, 144, 106127. [Google Scholar] [CrossRef]
  66. Yıldırım, Ç.V.; Kıvak, T.; Sarıkaya, M.; Erzincanlı, F. Determination of MQL Parameters Contributing to Sustainable Machining in the Milling of Nickel-Base Superalloy Waspaloy. Arab. J. Sci. Eng. 2017, 42, 4667–4681. [Google Scholar] [CrossRef]
  67. Demirsöz, R.; Yaşar, N.; Korkmaz, M.E.; Günay, M.; Giasin, K.; Pimenov, D.Y.; Aamir, M.; Unal, H. Evaluation of the Mechanical Properties and Drilling of Glass Bead/Fiber-Reinforced Polyamide 66 (PA66)-Based Hybrid Polymer Composites. Materials 2022, 15, 2765. [Google Scholar] [CrossRef]
  68. Demirsöz, R.; Boy, M. Measurement and Evaluation of Machinability Characteristics in Turning of Train Wheel Steel via CVD Coated-RCMX Carbide Tool. Manuf. Technol. Appl. 2022, 3, 1–13. [Google Scholar] [CrossRef]
  69. Asiltürk, İ.; Kuntoğlu, M.; Binali, R.; Akkuş, H.; Salur, E. A Comprehensive Analysis of Surface Roughness, Vibration, and Acoustic Emissions Based on Machine Learning during Hard Turning of AISI 4140 Steel. Metals 2023, 13, 437. [Google Scholar] [CrossRef]
  70. Şirin, Ş.; Kıvak, T. Performances of Different Eco-Friendly Nanofluid Lubricants in the Milling of Inconel X-750 Superalloy. Tribol. Int. 2019, 137, 180–192. [Google Scholar] [CrossRef]
  71. Yücel, A.; Yıldırım, Ç.V.; Sarıkaya, M.; Şirin, Ş.; Kıvak, T.; Gupta, M.K.; Tomaz, Í.V. Influence of MoS2 Based Nanofluid-MQL on Tribological and Machining Characteristics in Turning of AA 2024 T3 Aluminum Alloy. J. Mater. Res. Technol. 2021, 15, 1688–1704. [Google Scholar] [CrossRef]
  72. Singh, R.K.; Sharma, A.K.; Dixit, A.R.; Tiwari, A.K.; Pramanik, A.; Mandal, A. Performance Evaluation of Alumina-Graphene Hybrid Nano-Cutting Fluid in Hard Turning. J. Clean. Prod. 2017, 162, 830–845. [Google Scholar] [CrossRef]
  73. Waikar, R.A.; Guo, Y.B. A Comprehensive Characterization of 3D Surface Topography Induced by Hard Turning versus Grinding. J. Mater. Process. Technol. 2008, 197, 189–199. [Google Scholar] [CrossRef]
  74. Bagherzadeh, A.; Budak, E. Investigation of Machinability in Turning of Difficult-to-Cut Materials Using a New Cryogenic Cooling Approach. Tribol. Int. 2018, 119, 510–520. [Google Scholar] [CrossRef]
  75. Kouam, J.; Songmene, V.; Balazinski, M.; Hendrick, P. Effects of Minimum Quantity Lubricating (MQL) Conditions on Machining of 7075-T6 Aluminum Alloy. Int. J. Adv. Manuf. Technol. 2015, 79, 1325–1334. [Google Scholar] [CrossRef]
  76. Demirpolat, H.; Binali, R.; Patange, A.D.; Pardeshi, S.S.; Gnanasekaran, S. Comparison of Tool Wear, Surface Roughness, Cutting Forces, Tool Tip Temperature, and Chip Shape during Sustainable Turning of Bearing Steel. Materials 2023, 16, 4408. [Google Scholar] [CrossRef]
  77. Binali, R.; Demirpolat, H.; Kuntoğlu, M.; Sağlam, H. Machinability Investigations Based on Tool Wear, Surface Roughness, Cutting Temperature, Chip Morphology and Material Removal Rate during Dry and MQL-Assisted Milling of Nimax Mold Steel. Lubricants 2023, 11, 101. [Google Scholar] [CrossRef]
  78. Binali, R.; Demirpolat, H.; Kuntoğlu, M.; Salur, E. Different Aspects of Machinability in Turning of AISI 304 Stainless Steel: A Sustainable Approach with MQL Technology. Metals 2023, 13, 1088. [Google Scholar] [CrossRef]
  79. Korkmaz, M.E.; Gupta, M.K.; Günay, M.; Boy, M.; Yaşar, N.; Demirsöz, R.; Ross, K.N.S.; Abbas, Y. Comprehensive Analysis of Tool Wear, Surface Roughness and Chip Morphology in Sustainable Turning of Inconel-601 Alloy. J. Manuf. Process. 2023, 103, 156–167. [Google Scholar] [CrossRef]
  80. Yılmaz, B.; Karabulut, Ş.; Güllü, A. A Review of the Chip Breaking Methods for Continuous Chips in Turning. J. Manuf. Process. 2020, 49, 50–69. [Google Scholar] [CrossRef]
  81. Musavi, S.H.; Davoodi, B.; Niknam, S.A. Effects of Reinforced Nanoparticles with Surfactant on Surface Quality and Chip Formation Morphology in MQL-Turning of Superalloys. J. Manuf. Process. 2019, 40, 128–139. [Google Scholar] [CrossRef]
  82. Shah, P.; Khanna, N. Chetan Comprehensive Machining Analysis to Establish Cryogenic LN2 and LCO2 as Sustainable Cooling and Lubrication Techniques. Tribol. Int. 2020, 148, 106314. [Google Scholar] [CrossRef]
  83. Mishra, S.K.; Ghosh, S.; Aravindan, S. Machining Performance Evaluation of Ti6Al4V Alloy with Laser Textured Tools under MQL and Nano-MQL Environments. J. Manuf. Process. 2020, 53, 174–189. [Google Scholar] [CrossRef]
  84. Darshan, C.; Jain, S.; Dogra, M.; Gupta, M.K.; Mia, M.; Haque, R. Influence of Dry and Solid Lubricant-Assisted MQL Cooling Conditions on the Machinability of Inconel 718 Alloy with Textured Tool. Int. J. Adv. Manuf. Technol. 2019, 105, 1835–1849. [Google Scholar] [CrossRef]
  85. Airao, J.; Nirala, C.K.; Bertolini, R.; Krolczyk, G.M.; Khanna, N. Sustainable Cooling Strategies to Reduce Tool Wear, Power Consumption and Surface Roughness during Ultrasonic Assisted Turning of Ti-6Al-4V. Tribol. Int. 2022, 169, 107494. [Google Scholar] [CrossRef]
  86. Bermingham, M.J.; Kirsch, J.; Sun, S.; Palanisamy, S.; Dargusch, M.S. New Observations on Tool Life, Cutting Forces and Chip Morphology in Cryogenic Machining Ti-6Al-4V. Int. J. Mach. Tools Manuf. 2011, 51, 500–511. [Google Scholar] [CrossRef]
  87. Sworna, K.N.; Manimaran, R.G. Effect of Cryogenic Coolant on Machinability of Difficult-to-Machine Ni—Cr Alloy Using PVD—TiAlN Coated WC Tool. J. Braz. Soc. Mech. Sci. Eng. 2019, 3, 44. [Google Scholar] [CrossRef]
Materials 17 01826 g001
Figure 1. Schematic of MQL mechanism [17].
Figure 1. Schematic of MQL mechanism [17].
Materials 17 01826 g001
Materials 17 01826 g002
Figure 2. The crystal structure of, (a) h-BN, (b) graphene [49,50].
Figure 2. The crystal structure of, (a) h-BN, (b) graphene [49,50].
Materials 17 01826 g002
Materials 17 01826 g003
Figure 3. Experiment and analysis procedure.
Figure 3. Experiment and analysis procedure.
Materials 17 01826 g003
Materials 17 01826 g004
Figure 4. Geometric description of average surface roughness (Ra) [54].
Figure 4. Geometric description of average surface roughness (Ra) [54].
Materials 17 01826 g004
Materials 17 01826 g005
Figure 5. Assessment of Ra under different cooling/lubrication methods at the cutting speeds of: (a) 40 m/min, (b) 60 m/min, (c) 80 m/min.
Figure 5. Assessment of Ra under different cooling/lubrication methods at the cutting speeds of: (a) 40 m/min, (b) 60 m/min, (c) 80 m/min.
Materials 17 01826 g005
Materials 17 01826 g006
Figure 6. Geometric description of maximum height of surface profile (Rz) [54].
Figure 6. Geometric description of maximum height of surface profile (Rz) [54].
Materials 17 01826 g006
Materials 17 01826 g007aMaterials 17 01826 g007b
Figure 7. Assessment of Rz under different cooling/lubrication methods at the cutting speeds of: (a) 40 m/min, (b) 60 m/min, (c) 80 m/min.
Figure 7. Assessment of Rz under different cooling/lubrication methods at the cutting speeds of: (a) 40 m/min, (b) 60 m/min, (c) 80 m/min.
Materials 17 01826 g007aMaterials 17 01826 g007b
Materials 17 01826 g008
Figure 8. Surface morphology under different cutting environments.
Figure 8. Surface morphology under different cutting environments.
Materials 17 01826 g008
Materials 17 01826 g009aMaterials 17 01826 g009bMaterials 17 01826 g009c
Figure 9. SEM images via STEM detectors for chip morphology under different cutting environments.
Figure 9. SEM images via STEM detectors for chip morphology under different cutting environments.
Materials 17 01826 g009aMaterials 17 01826 g009bMaterials 17 01826 g009c
Table 1. The chemical composition of the workpiece material (wt. %).
Table 1. The chemical composition of the workpiece material (wt. %).
CMnSiPNiCrMoAlNSBalance
0.050.10.10.018.513.22.20.960.010.08Fe
Table 2. Cutting parameters.
Table 2. Cutting parameters.
Cutting Speed, m/minFeed Rate, mm/ToothCutting Depth, mm
40–60–800.04–0.08–0.120.8
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Günay, M.; Korkmaz, M.E. Understanding the Relationship between Surface Quality and Chip Morphology under Sustainable Cutting Environments. Materials 2024, 17, 1826. https://doi.org/10.3390/ma17081826

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Günay M, Korkmaz ME. Understanding the Relationship between Surface Quality and Chip Morphology under Sustainable Cutting Environments. Materials. 2024; 17(8):1826. https://doi.org/10.3390/ma17081826

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Günay, Mustafa, and Mehmet Erdi Korkmaz. 2024. "Understanding the Relationship between Surface Quality and Chip Morphology under Sustainable Cutting Environments" Materials 17, no. 8: 1826. https://doi.org/10.3390/ma17081826

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