Evaluation of Machining Parameters in Turning Al7075-T6 Aluminum Alloy Using Dry, Flooded, and Cryogenic Cutting Fluid Conditions

Abstract

Production industries create high-quality products through effective machining precision, lead times, productivity, cost benefits, and implementing sustainable manufacturing practices. This study compares the effect of cryogenic CO₂ as a cutting fluid with a flooded conventional system and dry turning on the surface roughness, early-stage tool phenomena (including adhesion, material transfer, and built-up edge (BUE) formation), and the chip morphology of aluminum 7075-T6. Taguchi’s L9 orthogonal array is applied to identify the optimal cutting parameters that minimize surface roughness (Ra). Cutting speed (Vc), feed rate (f), depth of cut (ap), and the type of cutting fluid condition were defined at three levels. The surface roughness (Ra) was determined, and the built-up edge (BUE) and chip morphology were evaluated. Moreover, SEM and energy-dispersive X-ray spectroscopy (EDX) were employed to characterize the machined surface and the cutting tools. The optimal values for the cryogenic cooling and cutting parameters are as follows: 220 m/min (Vc), 0.05 mm/rev (f), and 0.5 mm (ap). These conditions yield a surface roughness mean (Ra) of 0.736 µm, improving the surface roughness by 10.57% compared with the lowest Ra value from all of the tests. In addition, ANOVA showed the feed rate to be the most significant cutting parameter over surface roughness under the given conditions. Regarding chip morphology, snarled chip shapes are associated with low surface roughness values. The results indicate that cryogenic cutting fluid enhances the machined surface quality and reduces the built-up edge compared with dry and flooded conditions.

1. Introduction

Industry accounts for 31% of all energy consumption. Manufacturing accounts for about 60% of the total energy consumption in industry [1]. This significant energy demand has economic, environmental, and social implications, making sustainable production strategies in metal cutting industries an urgent priority. Reducing ecological harm is directly related to ensuring safer, cleaner, and more resource-efficient manufacturing processes [2,3]. Lightweight aluminum alloys, such as AI7075-T6, are extensively used in the aerospace, automotive, mold, and defense sectors due to their high strength-to-weight ratio. However, their relatively soft and adhesive nature presents notable machining challenges, including severe material adhesion, built-up edge formation, and difficulties in maintaining surface integrity [4,5]. These issues can compromise dimensional accuracy, surface finish, and tool life, making the selection of optimal cutting parameters and cooling/lubrication strategies critical [4]. The effectiveness of a cooling/lubrication system depends on the workpiece alloy, machining process, and cutting conditions [6].

Dry machining avoids the application of cooling/lubrication systems, thereby reducing chemical interaction, operational costs, and biological contamination [7,8]. However, for aluminum alloys, their lack of lubrication frequently leads to high cutting temperatures, the adhesion of workpiece material to the cutting tool, and BUE formation [1,9,10]. To mitigate these drawbacks, strategies such as self-lubricating tool coatings, conservative cutting regimes, and advanced cutting tool geometries have been proposed [10]. Nevertheless, some studies show that dry machining has a significant effect on the surface roughness of aluminum alloys. Approximately 20% of dry machining tests result in poorer finishes than flooded, MQL, and cryogenic methods at 90 to 150 m/min cutting speeds [11].

Conventional mineral-based cutting fluids, applied via the flooded method, remain common in industry but generate additional cleaning requirements, increase production costs, and pose health and environmental risks [12,13]. This method is particularly effective for aluminum alloys, which suffer from BUE formation and poor chip evacuation because of their ductility and sticky nature [14]. It can achieve a superior surface roughness quality under certain conditions [15,16,17]. Nevertheless, thermal shock, tool damage, and tip breakage are common problems attributed to the large volume of flood cooling [6].

Alternative strategies such as Minimum Quantity Lubrication (MQL) and cryogenic cooling have emerged as sustainable options.

Studies on aluminum alloys have shown that cryogenic cooling can reduce cutting temperature, enhance tool life, and improve surface finish compared with dry and MQL machining [11,18,19,20,21]. For example, CO₂ or liquid nitrogen cooling delays crack formation, reduces flank wear, and lowers cutting forces, with some reports indicating productivity and sustainability, resulting in a 72.24% longer tool life than dry machining and 38.68% longer tool life than flooded machining [20,22,23]. However, the high-cost handling requirements of liquid nitrogen limit its widespread adoption [24]. Despite this, CO₂-based cryogenic systems are considered industrially viable and environmentally friendly, offering rapid heat dissipation without leaving harmful residues.

While numerous studies have compared cooling strategies for aluminum alloys, most have focused on long-term wear modes such as flank and crater wear during extended machining trials. These approaches often obscure the influence of cooling/lubrication on early-stage tool degradation phenomena, including BUE formation, adhesion, and initial chip morphology. Furthermore, limited work has been conducted using controlled short-length turning tests to isolate immediate tool–material interactions under different cooling environments. Addressing this gap is essential for understanding the beginning of wear mechanisms and improving surface integrity in precision manufacturing.

This study investigates the machining of aluminum alloy 7075-T6 under three cooling/lubrication strategies: dry, flooded, and cryogenic CO₂. This research focuses on surface roughness (Ra), early-stage tool phenomena (BUE formation, adhesion, and material transfer), and chip morphology. Advanced characterization techniques, including scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX), are employed to analyze the machined surface and cutting tool. A Taguchi L9 orthogonal array, signal-to-noise (S/N) ratio analysis, and Analysis of Variance (ANOVA) are used to optimize cutting parameters and quantify their influence on surface quality. By isolating short-length turning tests, this work provides a precise comparison of immediate tool–material interactions across different cooling strategies, eliminating the masking effects of progressive wear. The findings offer actionable insights into optimizing cutting parameters for AA7075-T6 while advancing sustainable machining practices. This approach expands the understanding of cryogenic machining in lightweight alloys and supports its potential for broader industrial applications. Figure 1 shows the research methodology of the present work.

2. Materials and Methods

This study employs two cooling/lubrication strategies: carbon dioxide (CO₂) as a cryogenic cutting fluid and an oil–water emulsion for flooded conditions. The CO₂ mass flow rate (0.05 kg/s) and the volumetric flow rate of the flooded cutting fluid were kept constant during all experiments to ensure consistent test conditions. In both cooling approaches, the cutting fluid was applied directly at the tool–chip interface, targeting the primary cutting zone at the same location. This targeted fluid delivery is critical, as it directly influences heat removal efficiency and, consequently, the resulting surface quality. In addition, the direction and evacuation of chips play a critical role in the final surface finish. In this work, CO₂ was applied in its liquid state, stored under pressure, and released into the cutting zone. This cryogenic cooling mechanism enables efficient heat dissipation and may significantly influence chip morphology, surface roughness, and tool wear. The application setup for CO₂ delivery is illustrated in Figure 2.

Experimental tests were performed in a two-axis lathe machining center, VIWA 1640-T400 at the Manufacturing Processes Lab (ESPE) (Sangolquí, Ecuador). The composition of the aluminum alloy AI 7075-T6 is shown

Table 1. AI 7075-T6 material characterization.

Element (wt%)	Aluminum	Zinc	Copper	Iron
AI 7075-T6	89.71	6.13	1.78	0.18

. The material’s hardness was experimentally verified prior to machining. The mean hardness value of the specimen was 150 HB. The tool holder and insert specifications are summarized in

Table 2. Tool characteristics.

Tool insert	Specification	CNMG120408 P
	Grade	K313
	Characteristics	Hard, low binder content, unalloyed WC/Co fine-grained
	Manufacturer	Kennametal
	Workpiece material	Non-ferrous materials/high-temp alloys
	Chip braker	P
Tool holder	Specification	QS-PCLNR 2525 -12C
	Characteristics	Nozzle for cooling

.

2.1. Design of Experiments Based on the Taguchi Method

The Taguchi method was used to evaluate the effect of the cutting parameters on the surface roughness. Four factors and three levels were selected for the experiments (

Table 3. Factors and levels selected for the experiment.

	Factors	Levels
		1	2	3
A	Cutting speed (m/min)	180	200	220
B	Feed rate (mm/rev)	0.05	0.1	0.2
C	Depth of cut (mm)	0.5	1	1.5
D	Cutting fluid condition	Dry	Flooded	Cryogenic CO2

). An L9 orthogonal array was used to define the parameters for each trial with four machining parameters (factors) (cutting speed, feed rate, depth of cut, and cooling condition), each tested at three levels, as listed in

Table 4. Orthogonal array L9 parameters.

Trials	A	B	C	D
L1	180	0.2	0.5	Dry
L2	180	0.1	1	Flooded
L3	180	0.05	1.5	Cryogenic
L4	200	0.2	1	Cryogenic
L5	200	0.1	1.5	Dry
L6	200	0.05	0.5	Flooded
L7	220	0.2	1.5	Flooded
L8	220	0.1	0.5	Cryogenic
L9	220	0.05	1	Dry

. The geometry of the specimen and the distribution of the tests are shown in Figure 3. A new insert was used in each test trial. This design allowed for a more precise comparison of immediate tool–material interactions under each fluid condition, which is often masked in extended trials by progressive wear.

An analysis of variance (ANOVA) was performed to determine if a parameter significantly impacts the final response. The signal-to-noise ratios (S/N) were determined to measure the quality characteristic deviating from the desired value. The “smaller the better” application criteria were used to minimize the response parameter (Ra) and is presented in (1) (signal-to-noise/smaller the better).

$$\left({\displaystyle \frac{S}{N}}\right)=-10{\log }_{10}\left({\displaystyle \frac{1}{n}}\sum _{i=1}^n{y_i}^2\right)$$

(1)

where n is the total samples, and y is the individual sample observations.

Finally, confirmation tests were carried out to validate the performance of the optimal parameter levels. The values are calculated based on Taguchi mean S/N ratios using (2).

$$\gamma p=\mathsf{\mu }+\sum _{i=1}^k({\mu }_i-\mu )$$

(2)

The predicted S/N ratio is indicated as $\gamma p$, the total mean S/N ratio is represented as $\mu$, and the mean S/N ratio at the optimal level is ${\mu }_i$. Here, $k$ refers to the number of input process parameters [23,25,26,27].

2.2. Response Parameters Measurement

2.2.1. Surface Roughness Assessment

A portable profilometer, MarSurf PS10 Elcometer 7062 tester (Materials Science Lab ESPE, Sangolquí, Ecuador), was utilized to measure surface roughness (Ra) between 0 and 0.350 μm for all the trials. The number of tests per surface trial was 3. The configuration of the settings of the profilometer is shown in

Table 5. Profilometer configuration setting.

Parameter	Value
Cut-off	0.8 mm
Measure Length	4.8 mm
Displacement Velocity	1 mm/s
Magnification	5000×

.

2.2.2. Morphological Characterization of Machined Surfaces, Tool Inserts, and Chip Morphology Analysis

SEM was utilized to analyze the workpiece and tool inserts after machining operations to recognize alterations due to the machining process under different cutting parameters. In addition, energy-dispersive X-ray spectroscopy (EDX) was used to detect the chemical elements of the tool insert and machined surfaces to relate the mechanisms behind material adhesion. SEM and energy-dispersive X-ray spectroscopy (EDX) tests were performed using a Bruker Nano GmbH spirit 1.9 (CENCINAT, Sangolquí, Ecuador).

Finally, an optical microscope was utilized to analyze the macro-morphology of the chips (Materials Science Lab ESPE, Sangolquí, Ecuador).

3. Results and Discussion

3.1. Surface Roughness Assessment

The mean surface roughness (Ra) of the machined surfaces is shown in

Table 6. Mean surface roughness using CNMG 120408 P.

	Parameters				Surface Roughness µm
	A	B	C	D	R1	R2	R3	Rmean	S/N
L1	180	0.2	0.5	Dry	1.459	1.45	1.457	1.455	−3.259
L2	180	0.1	1	Flooded	1.148	1.164	1.15	1.154	−1.244
L3	180	0.05	1.5	Cryogenic	0.903	0.901	0.898	0.901	0.909
L4	200	0.2	1	Cryogenic	1.476	1.463	1.459	1.466	−3.323
L5	200	0.1	1.5	Dry	1.202	1.205	1.205	1.204	−1.613
L6	200	0.05	0.5	Flooded	0.851	0.852	0.85	0.851	1.401
L7	220	0.2	1.5	Flooded	1.55	1.552	1.542	1.548	−3.795
L8	220	0.1	0.5	Cryogenic	0.822	0.826	0.82	0.823	1.696
L9	220	0.05	1	Dry	0.944	0.942	0.94	0.942	0.519

. The mean surface roughness values ranged from approximately 0.822 µm to 1.548 µm. The lowest roughness was obtained under conditions of a high cutting speed (220 m/min), a low feed rate (0.1 mm/rev), a small depth of cut (0.5 mm), and the use of cryogenic CO₂ as the cutting fluid (L8). In contrast, higher surface roughness values were recorded at increased feed rates and depths of cut, under non-cryogenic cutting conditions. This is in accordance with a previously reported study [24] where surface roughness values of machined 7075 aluminum alloy were lower compared with conventional wet machining conditions.

The results of the ANOVA are presented in

Table 7. ANOVA results of the raw data taken from CNMG 120408 P for surface roughness.

	Surface Roughness
Factors	DF	S	V	F-Value	%P	p-Value
Cutting Velocity (A)	2	0.009	0.0046	22.907	1.396	0.042	Significance
Feed rate (B)	2	0.561	0.2806	1403.006	85.498	0.001	Significance
Depth of cut (C)	2	0.052	0.0261	130.538	7.955	0.008	Significance
Lubrication (D)	2	0.034	0.0169	84.537	5.152	0.012	Significance
Error	0
Total	8				100

. The results indicate that all of the examined parameters significantly influence surface roughness (p-value < 0.05). However, feed rate is found to be the most influential factor (85.5% of the total variability and p = 0.001). This is also observed in Figure 4, the main effect plots (values from

Table 8. Response table for roughness means.

	Surface Roughness
Level	A	B	C	D
1	1.170	1.490	1.043	1.200
2	1.174	1.060	1.187	1.184
3	1.104	0.898	1.218	1.063
Delta	0.069	0.592	0.175	0.137
Rank	1	4	3	2

), where the individual impact of each parameter can be visualized. The surface roughness mean values increase when varying feed rate (B). Cutting velocity, depth of cut, and cutting fluid condition do not significantly affect the final surface roughness because of the low percentage contribution to the final response. Nevertheless, the mean surface roughness in all conditions slightly decreases with increased cutting speed from level 1 to level 3.

In dry conditions, surface roughness varies because the heat generated between the cutting edge and the workpiece softens the material at certain high cutting speeds. This softening causes aluminum particles to stick to the tooltip. At high speeds, these particles can become unstable, detaching from the tool and landing on the workpiece surface, which results in a built-up edge (BUE) [26]. This phenomenon is described in Section 3.3. An increase in surface roughness results from a deeper cut. This occurs because greater cutting forces lead to heightened vibrations in the machine tool [27].

A 3-axis graph is presented in Figure 5 to show the variation in surface roughness as a function of feed rate and cutting fluid condition. Similar values in surface roughness are observed under dry, flooded, and cryogenic conditions when the lowest feed rate level is used. In general, surface roughness increases as feed rate also increases. This can be explained because when feed rate is raised, heat is also increased at the cutting zone and tooltip, resulting in grooved and abrasive marks on the machined surface [26].

A confirmation test was subsequently performed to validate the optimal process parameters. A response table of the signal-to-noise analysis with the optimum levels for running the confirmation test is presented in

Table 9. Response table for S/N means for CNMG 120408 P.

	S/N Ratio
Level	A	B	C	D
1	−1.198	−3.459	−0.054	−1.450
2	−1.177	−3.387	−1.349	−1.122
3	−0.526	0.943	−1.499	−0.239
Delta	0.671	0.943	1.445	1.211
Rank	4	3	1	2

. The results of the conducted test are shown in

Table 10. Confirmation test results for surface roughness.

	Initial Process Parameter	Optimal Process Parameters
		Prediction S/N Ratio	Experiment
Level	A3, B2, C1, D3	A3, B1, C1, D3	A3, B1, C1, D3
Surface roughness (µm)	0.823		0.736
S/N ratio	1.696	3.026	3.073
Improvement in S/N ratio	1.377
Percentage reduction in surface 3.2roughness	%10.57

. The predicted optimal cutting conditions resulted in a reduction in surface roughness by approximately 10.57% compared with the initial process parameters. The ideal cutting conditions are 220 m/min (Vc), 0.05 mm/rev (f), 0.5 mm (ap), and cryogenic lubrication reaching a surface roughness of 0.736 µm. It was confirmed that the combination of a lower feed rate and cryogenic cooling was associated with a marked improvement in surface finish.

In summary, it was confirmed that the feed rate predominantly governs the surface quality during the turning of aluminum alloy 7075, with the depth of cut, lubrication condition, and cutting speed also being significant.

3.2. Modeling of Surface Roughness (Exploratory Regression)

A multiple linear regression model was developed to quantify the effect of the cutting parameters on surface roughness (Ra). The model explained 98.2% of the variance ($R^2=0.982$) with an adjusted $R^2$ of 0.95. The regression equation obtained was as follows:

$$Rmean=0.888-0.0016\times A+3.994\times B+0.174\times C-0.016\times Df-0.137Dc$$

(3)

where

A = cutting speed (m/min).

B = feed rate (mm/rev).

C = depth of cut (mm).

Df = flooded cooling (1 if flooded, 0 otherwise).

Dc = cryogenic cooling (1 if cryogenic, 0 otherwise).

(Dry is the baseline condition when both Df and Dc = 0).

The regression coefficients, along with their statistical metrics, are summarized in

Table 11. Regression coefficients and statistical metrics for Rmean model. Coefficient represents the estimated change in Ra (µm) for a one-unit change in the predictor, holding other variables constant. Std. Error indicates the uncertainty of each coefficient estimate; smaller values indicate more precise estimates. t is the test statistic (coefficient divided by its standard error). p-value quantifies the probability of observing such an effect if the true effect were zero; p < 0.05 is conventionally considered statistically significant.

Term	Coefficient	Std. Error	t	p-Value
Intercept	0.888000	0.263198	3.374	0.0433
A	−0.001642	0.001264	−1.298	0.2850
B	3.994286	0.331088	12.064	0.0012
C	0.174667	0.050575	3.454	0.0408
Flooded	−0.016000	0.050575	−0.316	0.7725
Cryogenic	−0.137000	0.050575	−2.709	0.0732

.

From Table 11, feed rate (B) had the strongest effect, showing a highly significant positive association with surface roughness (p = 0.001). Depth of cut (C) was also significant (p = 0.041), while cutting speed (A) did not reach statistical significance (p = 0.285). Cryogenic cooling showed a negative coefficient, suggesting a potential reduction in surface roughness, although the effect was marginally non-significant (p = 0.073).

Diagnostic plots (predicted vs. observed and residual plots, Figure 6 and Figure 7) indicate that the residuals were randomly distributed without obvious heteroscedasticity or curvature. However, given the small sample size (n = 9) inherent in the L9 orthogonal array and the potential multicollinearity (condition number ≈ 3.27 × 10³), these findings should be interpreted as exploratory rather than confirmatory. The model is suitable for generating hypotheses and guiding future experiments with larger sample sizes and full factorial designs.

3.3. Morphological Characterization of Machined Surface Under Dry, Flooded, and Cryogenic Conditions

The surface topography of the machined workpiece was examined using SEM and EDX. A comparison of feed rate marks and the causes of and effects on the machined surfaces for dry, flooded, and cryogenic conditions is shown in Figure 8, Figure 9 and Figure 10. The SEM images in Figure 8 show the effects of the cutting parameters on the surface roughness of trial L1 with a high surface roughness of 1.455 µm under dry conditions, showing their textures and characteristics. The dry condition images show prominent plowing marks and high side flow at points (1) and (6). This is an indication that the removal mechanism is associated with the feed rate, which leads to an inferior surface quality. In addition, a worn surface at point (2), microchip adhesion at point (4), and cavities at point (3) can be seen. The EDX in Figure 8 shows the higher peak count of aluminum particles followed by magnesium and silicon. The peak intensity of silicon count, followed by magnesium, may trigger particle adhesion phenomena during machining [26].

In contrast to the dry and flooded conditions, the feed rate marks at point (1) are slightly noticeable under a cryogenic environment. Nevertheless, chip adhesion at point (2) and cavities at point (3) are visible. The cryogenic condition notably lacks the plowing paths from the feed rate footprint from trial L3, with a surface roughness of 0.901 µm, as shown in Figure 9. Under flooded conditions, some adhered aluminum particles are washed away, resulting in a smoother machined surface compared with dry conditions, as seen in Figure 9. At a cutting speed of 220 m/min, a feed rate of 0.2 mm/rev, and a depth of cut of 1.5 mm, with a flooded cutting fluid condition, the machined surface exhibits a significant deep depression at point (2), cavities at point (3), chip adhesion at point (4), and worn surfaces at point (1), resulting in a surface mean roughness of 1.548 µm. This may be attributed to the formation of BUE.

3.4. Morphological Characterization of Tool Inserts

Abrasive and diffusive wear formations due to high temperatures in the cutting zone are the main factors that influence the wear characteristics during the machining process [28]. Adhesion occurs on the cutting tool edge, generating a premature tool failure, influencing machining operations, increasing cutting forces, and varying product quality [11,28,29]. Examining factors such as cutting speed and feed rate can provide valuable information to reduce material adhesion and improve tool life [26]. By utilizing advanced techniques such as surface characterization and wear measurement, researchers can investigate tool design and machining strategies. Figure 11, Figure 12 and Figure 13 show the SEM and EDX images of the tool insert surface at different cutting parameters under dry, flooded, and cryogenic environments. Figure 11a,c,d display a notable footprint material adhesion at point (1) of approximately 160.94 µm in size and 74.39% of the AI element, followed by Mg and C near the tool wear in dry conditions. The presence of Al and Mg may be due to the diffusion of the workpiece material on the tool insert at a high feed rate and lower velocity.

Chip plastering occurs on the cutting edge of the tool insert after 2.61 s of machining, as shown in Figure 11a,e,f. This is due to the high feed rate, which improves heat energy transfer, causing the material to soften and adhere to the tooltip as a BUE [26]. The peak of chip plastering for dry conditions is about 151.52 µm, as can be seen in Figure 11f.

Under cryogenic conditions and in trial L3, Figure 12a,b reveal a minor footprint at point (1), approximately 20.15 µm and comprising 61.21% of the AI element, followed by W, Mg, and C, located near the edge chipping. The SEM images in Figure 12e,f illustrate the development of the BUE around the cutting edge after 10.47 s of turning.

The peak of chip plastering under cryogenic conditions reaches around 113.39 µm. In flooded conditions and during trial L7, the EDX results depicted in Figure 13a,c,d indicate a footprint at point (1) measuring approximately 156.86 µm. The elemental composition shows that the AI element comprises 83.41%, followed by Mg, Mn, Ag, and C, all located near the wear region. The SEM images in Figure 13e,f demonstrate the formation of the BUE around the cutting edge after 2.14 s of turning. Additionally, under cryogenic conditions, the peak of chip plastering reaches approximately 85.56 µm.

Cooling techniques help reduce the wear effects at the cutting tool and workpiece interfaces [26]. However, this study could not eliminate chip plastering on the cutting edge regardless of whether dry, flooded, or cryogenic conditions were employed, as can be seen on the EDX analysis and chip plastering views in Figure 11, Figure 12 and Figure 13. A comparative analysis of material adhesion from Figure 11, Figure 12 and Figure 13 utilizing EDX from a top view under dry, cryogenic, and flooded conditions reveals that the cryogenic condition exhibits a greater resistance to wear and adhesion, making it superior for extending tool life compared with the other methods applied. Thus, the built-up edge (BUE) was the dominant mechanism influencing tool–workpiece interaction, particularly under lower cutting speeds.

3.5. Chip Morphology Analysis

The generation of chips during the turning of aluminum alloys is a critical factor that significantly impacts machining efficiency and the surface roughness of the workpiece [7,30]. Aluminum chips produced by turning at different feed rates, cutting velocities, and depths of cut under dry, flooded, and cryogenic conditions exhibit various sizes and shapes resulting from the machining operations L1–L9, as shown in Figure 14. Chip shapes are analyzed according to ISO 3685. Chip morphology under optimized conditions was not included and is proposed as part of future work.

Understanding chip formation mechanisms can help optimize cutting parameters, select tools, and apply coolant, all of which significantly influence chip shape [31]. Observations indicate that reduced chip thickness decreases with feed rate and speed. This, in turn, leads to lower cutting forces and tool vibration, ultimately improving surface quality [7]. Distinct saw-tooth shapes are recognized as primary serrated teeth at point (1) along the upper free edge, secondary serrated teeth at point (2) at the lower edge, large serrations at point (3), small serrations at point (4), and large scratches at point (5) from trial 1 to 9 in Figure 14. Large and small serrations occur under all conditions, as seen in Figure 9. Plastic deformation, cyclic cracking, and shear localization are the key characteristics of chip serration [7,30,31]. Significant scratches arise from intense friction between the tool rake surface and the chip interface, indicating inadequate lubricity at the cutting edge [32]. Under dry conditions, chips with a snarled shape were obtained using medium and high cutting velocities and low and medium feed rate parameters, as seen in Figure 14, for trials L5 and L9. Conversely, a short helical shape was produced when a low cutting velocity, high feed rate, and low depth of cut were employed (Figure 14, L1).

Under flooded conditions, chips with short coil shapes were obtained using the minimum and the higher cutting velocity range, medium and high feed rates, and medium and high depth of cut, as seen in Figure 14, for trials L2 and L7. Nevertheless, a snarled shape is recognized when a low feed rate and depth of cut are used (Figure 14, L6) reaching a low surface roughness.

Under cryogenic conditions, chips with a snarled shape (Figure 14, L8) were obtained using a high cutting speed, medium feed rate, and low depth of cut, resulting in the lowest surface roughness value in this study. Meanwhile, a short helical shape was produced when a medium cutting velocity, high feed rate, and medium depth of cut, as shown in Figure 14, in trial L4, resulted in a high surface finish roughness. In addition, a low surface roughness value was obtained when a low cutting speed, feed rate, and depth of cut were applied, as seen in Figure 14, L3. A snarled chip shape is associated with low surface roughness values regardless of environmental conditions. Conversely, short helical shapes are linked to elevated surface roughness values, irrespective of environmental conditions.

4. Conclusions

This study aimed to compare the effects of dry, flooded, and cryogenic cutting fluid conditions on the turning machining of aluminum alloy AI 7075-T6, focusing on adhesion, material transfer, BUE accumulation, surface roughness, and chip morphology during short-length machining. The selected cutting parameters include cutting speed, feed rate, depth of cut, and the cutting fluid condition. The analyzed responses are surface roughness, tool life, and chip morphology. Based on the experiments, data analysis, and examination of SEM and EDX results, the following conclusions can be drawn from this study:

The optimal machining parameters were a cutting speed of 220 m/min, a feed rate of 0.05 mm/rev, and a depth of cut of 0.5 mm, employed under cryogenic CO₂ cutting fluid conditions. An improvement of 10.57% in surface roughness was achieved relative to the initial parameters. The feed rate was the dominant factor influencing the final surface quality.

The cutting fluid condition had a major impact on surface integrity and the BUE, as determined by the morphology of the machined surface and tool insert. While dry machining resulted in noticeable plowing and side flow markings, cryogenic circumstances generated superior surface finishes with less feed rate marks, and flooded conditions produced deep residual marks even though the surface was rather smooth.

The machining conditions have an impact on chip morphology. While short helical chip shapes are linked to greater surface roughness values, snarled chip shapes are linked to lower surface roughness values. Furthermore, chip plastering and built-up edge development were seen as indicators of early-stage phenomena under all conditions; flooded conditions showed the lowest peak height of chip plastering. In the machining of AI 7075-T6, differences in chemical composition were also found, including the production of oxide layers and the adhesion of particles like silicon, manganese, and magnesium.

The machining process needs to take occupational health and environmental factors into account. Because of the flexibility of aluminum alloys and the heat process that occurs during machining, dry machining was shown to not be the best option regarding BUE. On the other hand, it is commonly known that flooded systems are harmful to the environment and the health of machine operators. In order to determine viability and cost advantages under various cutting fluid conditions, the next stage is to evaluate power consumption during turning operations.

Future work will incorporate additional surface texture parameters, such as Rz and Rsm, to enrich the evaluation of machined surface integrity. Additionally, extended cutting tests and quantitative wear measurements will be conducted to accurately track the evolution of tool wear, distinguish it from built-up edge formation, and provide a more comprehensive assessment of tool performance over time. We also recognize the value of incorporating finite element simulation (FEM) to enhance the theoretical understanding of tool wear and chip formation mechanisms across the three machining conditions. Although FEM was not included in the present study due to its experimental focus and time constraints, future work will integrate simulations to complement the experimental results, support the observed trends, and provide predictive insights