Analysis of residual stresses under hard turning of AISI D2 hardened steel using TiAlN PVD-coated carbide insert for different cutting environments

Abstract

Although several studies have explored hard turning of difficult-to-machine steels, a comprehensive and comparative analysis of the residual stress behavior of AISI D2 steel under different advanced cooling and lubrication strategies, specifically nano Al₂O₃-based minimum quantity lubrication (MQL) and cryogenic liquid nitrogen (LN₂), remains limited. Existing literature primarily focuses on surface roughness (Ra) and tool wear, while systematic evaluation of subsurface residual stresses across multiple cutting environments using titanium aluminum nitride (TiAlN) PVD-coated carbide inserts is scarce. This gap motivates the present research, which aims to identify the most suitable cutting environment for achieving favorable residual stress profiles during hard turning of AISI D2. Here, AISI D2 hardened steel was turned using TiAlN-coated carbide inserts under four cutting environments: Dry, conventional wet cooling, nano Al₂O₃ MQL, and cryogenic LN₂. Cutting speed, feed rate, depth of cut, and rake angle were selected as control parameters common to all environments. The resulting Ra, cutting forces (N), and residual stresses were measured and analyzed. The influence of the machining parameters on the development of residual stresses was also examined under both favorable and extreme cutting conditions for each environment. The results clearly indicate that compressive residual stresses (σCRSs) were generated in all cutting environments, which is beneficial for enhancing fatigue resistance. Among the conditions studied, cryogenic cooling produced the maximum σCRS, demonstrating its superior capability to improve surface integrity during hard turning of AISI D2 steel. The current work focuses on important machining issues, particularly with AISI D2 steel work material. Sustainable manufacturing is supported by the use of cryogenic cooling and nano-based MQL. Adopting such machining conditions will increase the service life and dependability of components.

Keywords

Residual stresses AISI D2 steel hard turning nano MQL cryogenic

Introduction

In recent years, the hard turning of difficult-to-machine steels, especially high carbon and high alloy steels such as AISI D2, has gained significant interest due to its potential to replace grinding processes in manufacturing. Many researchers showed keen interest in hard turning because the technique of hard machining may reduce the final cost of machined part by up to 90% of the final cost. The hard turning can be treated as an alternative operation to the highly expensive and low-productive grinding operation of the heat-treated component.¹ Such an advantage of hard turning motivated us to pursue the current research work. As indicated above, AISI D2 steel is considered to be a difficult material to cut, but due to the presence of special alloying elements, the AISI D2 steel becomes very special. It possesses high resistance to wear, high strength, high hot hardness, rapid strain hardening, low thermal conductivity, and high toughness value.^{2, 3} Because of such properties, AISI D2 has wide application in the field of the tooling industries, such as in the manufacturing of forging dies, hot trimming dies, blanking dies, coining dies, lamination dies, deep draw dies, cold extrusion dies, pressure die casting, cutting dies, shear blades, burnishing tools, gauges, knurls, wear plate, and others. It also plays a significant role in the making of injection molds, transfer molds, and compression molds used for the plastic components. With such a good scope of applications, AISI D2 steel has a drawback related to machining. The machining of AISI D2 is generally considered to be difficult owing to several inherent properties of the material, which were mentioned earlier. During the machining of AISI D2, very high cutting forces are developed because of its high hardness value (55–64) Hardness Rockwell C [HRC]). Machining at high-speed conditions tends to generate high temperatures and excessive stresses, which result in severe plastic deformation and subsequent failure of the tool. Therefore, AISI D2 is considered a material that is difficult to cut.⁴ Due to the generation of high cutting forces while machining the AISI D2 steel, the residual stresses will come into existence. These residual stresses present a potential risk in terms of crack initiation, propagation, and fatigue failure of the final products, and it is necessary to remove tensile surface residual stresses or prevent them from occurring during machining processes.⁵ Despite several studies on the cutting mechanics and performance of hard-to-machine steels, an inclusive comparative analysis of residual stress behavior under various advanced cooling and lubrication strategies remains underexplored. Specifically, research on the residual stress patterns in AISI D2 steel under nano Al₂O₃-based minimum quantity lubrication (MQL) and cryogenic liquid nitrogen (LN₂) cooling remains scarce, even though these advanced cooling strategies are known to improve the overall machining performance. Most existing studies primarily focus on surface roughness (Ra) and tool wear, while systematic evaluations of subsurface residual stresses across multiple cutting environments are limited, particularly when using titanium aluminum nitride (TiAlN) PVD-coated carbide inserts. This knowledge gap is critical, as residual stress plays a crucial role in the performance, fatigue resistance, and long-term durability of machined parts. This study aims to fill this gap by performing a detailed investigation into the effect of various cooling and lubrication techniques on the residual stress behavior of AISI D2 steel during hard turning. The findings of this research may highlight the critical role that cooling and lubrication techniques play in the hard turning of AISI D2 steel. By generating compressive residual stresses (σCRSs), both cryogenic cooling and nano-based MQL may significantly improve surface integrity and extend the service life of the machined components. Such cooling techniques also support sustainable manufacturing practices by reducing the environmental hazard issues with conventional wet cutting fluids. The study emphasizes the importance of selecting the right cutting environment for achieving optimal residual stress profiles in hard turning, ultimately enhancing the reliability and durability of AISI D2 steel components used in demanding applications.

Hard turning

Hard turning refers to the machining of materials with a hardness above 45 HRC. Unlike traditional turning, which often requires cooling fluids and slower speeds, hard turning uses cutting tools made from high-performance materials such as cubic boron nitride or ceramic inserts. These tools are capable of withstanding the high stresses and temperatures generated when machining hard materials such as tool steels, bearing steels, or cast irons. The process is efficient because it eliminates the need for grinding in many cases, reducing cycle time and costs. It also offers good surface finish and dimensional accuracy, which are critical in precision engineering. However, hard turning presents challenges, such as high tool wear rates and the need for advanced machine tools with precise control over cutting parameters (speed, feed rate, and depth of cut). When done properly, hard turning can provide excellent results in terms of quality and productivity. In recent years, hard turning has become increasingly popular for manufacturing components from tool steels. This process involves using a single-point cutting tool to machine workpieces with a hardness greater than 45 HRC. Hard turning offers several advantages, including reduced machining time, lower costs, and improved product quality, which are key concerns for the manufacturing sector. The choice of hard turning parameters is influenced by the hardness of the tool steel being machined. One of the main benefits of hard turning is that it allows for the achievement of the desired shape and size in a single setup, eliminating the need for multiple operations. In contrast, the conventional approach to machining hardened tool steel typically involves roughing, followed by heat treatment, and then a final grinding operation. Hard turning can often replace grinding, making it a more efficient and cost-effective alternative for producing high-precision tool steel components. However, hard turning can significantly reduce the number of machining operations needed for the final shaping of the required component. Hard turning reduces production time, manufacturing cost, and achieves a high rate of productivity.⁶ Hard turning is a significant and possible process because all manufacturers are approaching to produce their components at a lower investment, rapid setups, lower cost with higher quality, and smaller tooling inventory while removing non-value-added activities. The various factors on which the performance of hard turning majorly depends are rigidity in the cutting tools and the tool clamping system. Even the usage of advanced cutting tool material also plays a vital role in hard turning operations, suitable cooling, or lubrication mechanisms. Though lots of advantages are reported by various researchers, there are some limitations associated with the hard tuning process, such as it results in white layer formation, which can lead to adverse effects on the service performance of the component. A highly rigid machine tool is needed to achieve a higher degree of accuracy. The quality of the produced surface and dimensional accuracy deteriorate with the progress of tool wear while performing hard turning. Hard turning of tool steel is an efficient machining process option, but it also introduces significant challenges related to residual stresses. During hard turning, the high cutting forces and temperatures generated can cause the material to undergo plastic deformation, leading to the development of residual stresses in the workpiece. These stresses, if not properly controlled, can affect the mechanical properties, dimensional stability, and overall performance of the component.

Residual stresses

The stresses that are in a self-equilibrium state existing inside the manufactured components are called residual stress. These stresses remain in the components even after the removal of any external load applied on it during manufacturing. These stresses are generated in the workpiece with almost all kinds of manufacturing operations, such as welding, grinding, milling, turning, casting, and foundry. Residual stresses are affected by various parameters in hard turning. Such parameters are cutting tool material, cutting parameters, and the type of cooling environment. These stresses may affect the working performance of the components. Similarly, the effect on fatigue life is one of the most vital consequences of these. Generally, fatigue life is increased with σCRS, while it decreases with tensile residual stresses(σTRS). The major sources of stress (residual) are mechanical, chemical, or structural/phase transformation induced.⁷

The residual stresses, which are compressive in nature, are beneficial for fatigue life and functional response of the component, while σTRS will lead to the early failure of the product. Hence, it is vital to study the nature of stresses(residual). It is well established that machining operations, regardless of type, inevitably induce residual stresses within the workpiece due to the localized plastic deformation and thermal gradients associated with the process. The three factors that generate the residual stresses are as follows.^8–10

Rubbing action and plastic deformation between the workpiece material and selected cutting insert are key responsible for generating the stresses(residual) which are compressive in nature. These stresses are considered to be mechanical stresses.

Due to frictional heat and heat produced due to plastic deformation phenomena, the σTRS are produced. Such stresses are considered to be thermal origin stresses.

Due to a change in phase transformation within the materials during machining, σCRS will be generated.

Therefore, mechanical, thermal, and phase transformation effects are essential to be monitored while performing machining. They can be controlled by employing an optimal level of machining parameters.

The generation of residual stresses is primarily driven by mechanical, thermal, and phase transformation effects during machining. These stresses significantly impact the quality and performance of machined components. Therefore, careful monitoring and control of machining parameters are essential to minimize undesirable residual stresses. Optimal machining conditions can be employed to reduce the impact of these stresses, ensuring better component integrity and improved machining outcomes. Accurate measurement of residual stresses during the machining process is crucial for their effective management and minimization. The measurement of residual stresses provides valuable insight into how mechanical, thermal, and phase transformation effects influence the final state of the material. Various techniques, such as X-ray diffraction (XRD), neutron diffraction, and hole drilling methods, are commonly used to assess these stresses in the workpiece. By correlating the measurement data with the machining parameters, it becomes possible to identify the root causes of undesirable residual stresses and adjust the process accordingly. Through this approach, engineers can optimize machining conditions such as cutting speed, feed rate, and tool geometry to reduce the occurrence of residual stresses, thereby improving the overall quality and performance of the machined components. In this way, the integration of residual stress measurement into the machining process enables more precise control over the material’s integrity, ultimately leading to enhanced product reliability and lifespan.

Principle of stress (residual) measurement

Stress (residual) can be determined by various methods. The methods of measurement are classified into two categories, namely destructive type and non-destructive type. Some examples of destructive types are deep hole drilling, center hole drilling, and the contour method. Similarly, methods such as XRD, ultrasonic method, and magnetic method are categorized as non-destructive methods. However, in our study, stresses (residual) were calculated with the help of the XRD method. The XRD method was chosen because it provides high measurement accuracy and enables direct measurement of residual stresses from crystal lattice deformation using Bragg’s law, rather than indirectly estimating residual stresses, and this method is also suitable for machining and surface integrity studies. Calculating the strain value and stress value by using the theory of elasticity is the basis for the XRD method. The strain (ε) value is measured from the deviation found in the lattice structure, which is defined by the Bragg law as stated below.

n λ = 2 d \sin θ

(1)

where,

d is the interplanar spacing,

λ is the wavelength

θ is the angle of incidence that equals the angle of scattering (diffraction angle),

and,

n is an integer,

ε = \frac{d - d_{0}}{d_{0}} = \frac{\sin θ_{0}}{\sin θ} - 1

(2)

The sin²ψ method is well defined by Lu¹¹ and Noyan and Cohen.¹² The suggested method is extensively accepted to determine the stress (residual). The schematic of the configuration of the d versus sin² ψ method is illustrated in Figure 1. The residual stress in terms of Poisson ratio ν and Young’s modulus Е can be expressed as.^{13, 14}

Figure 1.

Diffraction planes at an angle ψ ϕ and direction of residual stress measurement.

\frac{d_{ψ} - d_{o}}{d_{o}} = \begin{matrix} \frac{1 + ν}{E} (σ_{1} \cos^{2} ϕ + r_{12} \sin 2 ϕ + σ_{2} \sin^{2} ϕ - σ_{3}) \sin^{2} ψ \\ + \frac{1 + ν}{E} σ_{3} - \frac{ν}{E} (σ_{1} + σ_{2} + σ_{3}) \\ + \frac{1 + ν}{E} (r_{13} \cos ϕ + r_{23} \sin ϕ) \sin 2 ψ \end{matrix}

(3)

where ψ is the angle through which the substrate is rotated,

dψ is the interplanar distance at ψ;

d_o is the strain-free d-spacing, respectively.

Let us consider an in-plane stress state,

that is, $σ_{3} = 0$ , $τ_{13} = 0$ .

Then, Equation (3) simplifies as

\frac{d_{ψ} - d_{o}}{d_{o}} = \frac{1 + ν}{E} (σ_{1} \cos^{2} ϕ + σ_{2} \sin^{2} ϕ) \sin^{2} ψ - \frac{ν}{E} (σ_{1} + σ_{2})

(4)

The residual stress σ₁ in any one direction by assuming a strain-free at $ψ = 0$ ,

can be expressed as:

σ_{1} = (\frac{E}{1 + ν}) m

where m is the gradient of d versus sin² ψ graph.

In turning hardened steels under various cutting conditions, the dominance of cutting parameters on the stress (residual) beneath the work surface is discussed. Many researchers have found that cutting parameters are highly dominant in the creation of stress (residual). Cutting velocity is found to be a highly dominant parameter for the generation of stresses (residual) in the finished turning operation. The magnitude of stress (residual) majorly depends upon the mechanical-based properties of the workpiece. The cutting speed and heat generated during the machining operation are interrelated to each other. The increase in cutting speed will lead to a high strain rate value, inducing greater mechanical work, thereby resulting in increased compressive stress. But further increase in cutting velocity resulted in a decrease in the peak σCRS. This is because the workpiece receives greater heat as a result of the increased cutting velocity. Consequently, the workpiece will get softer thermally. The behavior of cutting variables on stresses(residual) generated on AISI 316 was predicted by M’saoubi et al.¹⁵ It was proven that increasing cutting speed values would result in greater stress (residual). Mohammadpour et al.¹⁶ examined the behavior of machining parameters on stress(residual) during the turning of AISI 1045. They observed that the increases in cutting speed and feed were accompanied by an increase in the σTRS. Capello⁵ performed an experiment to determine the effect of process variables on the surface stresses(residual) developed while turning of the AISI 1035 alloy. The recorded results concluded that nose radius and feed were the highly dominating parameters on the generation of residual stress(residual), while the depth of cut showed an insignificant effect on the stress(residual). Dahlman et al.¹⁷ carried out a research study to see the influence of process variables on the stress(residual). Negative rake angle and high feed promoted σCRS under the machined surface. Hua et al.¹⁸ found that high σCRS can be obtained in both circumferential and axial directions while choosing a high order of feed, but no substantial effect on stress(residual) was found with the increase in cutting speed. Caruso et al.¹⁹ examined those σCRS that were generated during the machining of H13 steel at higher levels of process parameters. Similarly, Xueping et al.²⁰ experimentally proved that cutting parameters are highly dominant for the generation of σCRS. From various studies conducted by the researchers, it can be confirmed that input process variables strongly affect the generation of residual stress in the hard turning process. Also, the shear angle is modeled using the plasticity theory based on the maximum shear stress criterion. To predict transient shear strength, the Johnson–Cook model is employed, which estimates shear strength under varying strain and strain rates in Enhanced Vibration Cutting.²¹ Gaitonde et al.²² focuses on the behavior of primary cutting process variables on tool wear, specific cutting force, and tool wear. They also created a response surface methodology-based mathematical model to elaborate the relationship between output and input variables for AISI D2 turning by CC650WG wiper ceramic inserts. A comparative analysis was performed to assess the effects of vibration-assisted machining versus traditional hard turning on machining performance. To better understand the influence of various process parameters, particularly the application of one-dimensional ultrasonic vibrations to the tool, a two-dimensional finite element model was created.²³ Prashanth et al.²⁴ considered the grinding process parameters under various cooling environments to monitor the impact on output Ra, tangential grinding forces, cutting temperature, and normal grinding forces. They established a machine learning model to predict the output variables for Inconel 751 during the grinding process. Experimental and simulation studies were conducted to address the precision machining of polycarbonate for optical applications. The impact of processing parameters such as tool feed rate, depth of cut, and spindle speed on surface finish and profile error was analyzed, with optimization performed for both responses. The effect of machining heat on surface quality was examined experimentally and through simulations, and the influence of vacuum clamping on profile accuracy was also discussed.²⁵ Ulutan and Özel²⁶ carried out the turning of nickel-alloy IN100 and utilized physics-based simulations for the prediction of residual stresses. Also, the optimization of process parameters has been done through a particle swarm optimization procedure and validated through confirmatory tests. The work-related optimization of Inconel 718 turning process parameters for output variables, Ra, and residual stress has been carried out.²⁷ Where the aim was to identify the suitable parameter values for Ra and σTRS using a hybrid technique. Nikouei et al.²⁸ explore the effect of the cutting speed along with the nanoparticle size and its content in base fluids on stresses (residual) during machining of Inconel 718. Nonconventional methods, such as laser-assisted machining, have been tried to reduce the residual stresses while machining difficult-to-cut materials, and the micro laser-assisted machining of silicon has also been explored, related to residual stresses developed during machining.²⁹ The poor choice of process parameters always leads to a detrimental effect on phase purity and residual stresses, so care must be taken while selecting the value of the controllable variable, which shows a great impact on residual stresses and phase purity. The authors formulated cutting fluids from Karanja oil using non-ionic surfactants. The results revealed that the formulated cutting fluid lowered the cutting force in contrast to the conventional cutting fluid. Additionally, better surface quality, roughness, and reduced wear of the tool flank were observed throughout the entire experiment.³⁰ Also, the cutting fluid formulated from neem oil was used by the authors, and results revealed that the Ra and the tool wear were lower than those of the conventional cutting fluid.³¹ New cooling techniques, such as Ti–Cu butt joint and heat sink-based bottom-channel cooling, have been developed to enhance heat dissipation during titanium alloy drilling. The Ti–Cu butt joint performs better overall, offering greater reductions in temperature, thrust forces, torque, and stresses compared to the bottom-channel cooling method.³² The better machining outcomes of the non-edible oils than the conventional fluids are due to the higher viscosity than the mineral oils, as reported by the authors.³³ Studies on turning hardened steels have shown that cutting parameters, especially cutting velocity, play a crucial role in generating residual stresses. Higher cutting speeds initially increase residual stresses due to higher strain rates, but beyond a certain point, thermal softening reduces stress levels. We found in the literature that higher cutting speeds lead to greater residual stresses, while the importance of nose radius and feed rate, with depth of cut having minimal effect, has been reported. Other studies also found that feed rate and rake angle significantly impact residual stresses. Optimization of machining parameters, using techniques such as particle swarm optimization and machine learning, has been explored to predict and control residual stresses. Nonconventional methods such as laser-assisted machining have been investigated to reduce residual stresses, especially in difficult-to-machine materials.²⁹ Additionally, the use of non-edible oils as cutting fluids has been shown to reduce cutting forces, tool wear, and improve surface quality, emphasizing the importance of proper parameter selection to minimize residual stresses.

This study aims to fill this gap by performing a detailed investigation into the effect of various advanced cooling techniques on the machining parameters, such as cutting forces, cutting temperature, and residual stress. The research specifically compares four different cutting environments: Dry cutting, conventional wet cooling, nano Al₂O₃-based MQL, and cryogenic LN₂ cooling. To perform the experiments, a MQL setup was designed, which consisted of a nozzle to deliver a high-velocity jet of lubricant onto the tool rake surface, a flowmeter to regulate the lubricant flow rate, and an air compressor to supply the required operating pressure. Under these machining environments, AISI D2 steel was turned using TiAlN-coated carbide inserts with CNMG 120408 geometry, where different effective rake angles were obtained by selecting different insert grades as per the experimental design. TiAlN-coated carbide inserts consist of a carbide substrate as the base material and a TiAlN coating material. The TiAlN coating provides high hot hardness, high compressive strength, good fracture toughness to resist edge chipping, high wear resistance, and excellent thermal stability. The key control parameters for the experiments included cutting speed, feed rate, depth of cut, and rake angle, which were kept consistent across all environments to ensure a fair comparison. Ra was measured using a type surface tester, and cutting forces were recorded using a piezoelectric-based cutting force dynamometer. Residual stresses were measured using a Proto iXRD system. The study primarily focused on the generation of residual stresses under all machining conditions, especially σCRS induced during machining. Additionally, the surface topography of the machined samples was analyzed using a stereo zoom microscope.

Workpiece materials and methods

AISI D2 steel was selected as the workpiece material, which was cylindrical in shape, having dimensions of 300 mm in length and 300 mm in diameter. The workpiece is manufactured by Villares Metals, Brazil. Initially, the material was not subjected to any heat treatment process, and it was soft in nature. Now, to induce hardness in the workpiece, some heat treatment processes were performed. The details of the complete heat treatment cycle are shown in Figure 2.

Figure 2.

Heat treatment cycle adopted.

After attaining the desired hardness of 55 ± 2 HRC, all the experimental runs were conducted. Energy Dispersive X-ray Spectroscopy (EDS) is an X-ray technique that was performed to determine the chemical composition of the elements present in the selected workpiece and to compare the obtained results with the literature. Figures 3 and 4 show the results of the EDS test, whereas Table 1 depicts the weight percentage of the elements present in the selected workpiece material (AISI D2 steel). The results clearly indicate that the weight percentage of chromium is the highest among all the alloying elements present in the material. Similarly, a high percentage of carbon is also observed in the workpiece.

Figure 3.

Results of the EDS test of the AISI D2 sample.

Figure 4.

Sample selection for EDS testing.

Table 1.

Amount of elements in weight percentage identified in AISI D2 steel.

Element	Weight %
Iron (Fe)	76.66
Chromium (Cr)	12.96
Carbon (C)	4.8
Manganese (Mn)	2.33
Molybdenum (Mo)	1.62
Vanadium (V)	1.08
Silicon (Si)	0.53

The metallographic testing of AISI D2 steel was performed using an optical microscope, as shown in Figure 5. The AISI D2 steel specimens were first cut into small pieces and mounted in Bakelite using a hand mounting machine to facilitate proper holding during the polishing process. Subsequently, the workpiece surface was polished using SiC abrasive papers of grade 800-grit at different rotational speeds. Etching was performed on the polished surface using a 10% picric acid solution in 100 mL of ethanol. Thereafter, it was examined under an optical metallurgical microscope

Figure 5.

Photographic view of the prepared sample for polishing.

The obtained results are shown in Figure 6. Carbide particles are uniformly distributed in the workpiece. Clear grain boundaries are observed in the microscopic images. These carbides are responsible for the high hardness of the AISI D2 steel. Chemical composition obtained from the EDS test matches the composition as mentioned in the literature. A high amount of chromium has resulted in the formation of chromium-based carbides. A high amount of carbon is responsible for high wear resistance; generally, it is eight times that of plain carbon steel. The presence of molybdenum helps in stabilizing the chromium-rich carbides, such as (Cr Fe)₇ C_3. Manganese helps in improving the strength, toughness, and hardenability of AISI D2 steel.

Figure 6.

Microscopic images of AISI D2 (a) at 100X, (b) at 500X, and (c) at 1,000X.

During the literature review, it was found that most of the research work conducted was limited to the study of process parameter variations and their effects on residual stress. Therefore, in the current research work, an attempt has been made to compare the residual stress under worst and favorable machining conditions. The residual stresses generated after machining have been obtained for favorable condition and worst condition scenarios, in all four cutting conditions (dry, wet, nano-based MQL, and cryogenic). The residual stresses determined under favorable conditions were further compared with those obtained under worst conditions for all four cooling environments. The cutting parameters for worst conditions and favorable conditions are chosen on the basis of output responses, which are observed during the conduction of the experimental runs. Based on the experimental results, the parameter levels corresponding to higher values of Ra and cutting force were considered as worst conditions, whereas lower values of Ra and cutting force were considered as favorable conditions. All the selected cutting conditions under different environments are mentioned in Table 2. The actual setup of cryogenic machining and MQL with Nano is shown in Figures 7 and 8, respectively.

Table 2.

Machining conditions for all cutting environments.

Cutting environments	Cutting conditions
Dry	No use of any coolant
Conventional wet	1:10 (soluble oil: Water) flood type
Minimum quantity lubricant (nano)	Nozzle angle 45°, applied at three bars and a flow rate of 200 ml/hr, with nano material (Al₂O₃) with 0.4% concentration by weight
Cryogenic	LN₂ is used as a cooling medium, applied at a pressure of three bar
Selected levels	Rake angle (β)(°)	Cutting speed, (N) (m/min)	Depth of cut (D)(mm)	Feed (F) (mm/rev)
	−10	65	0.4	0.04
	−6	85	0.6	0.08
	1	105	0.8	0.12
	7	125	1.0	0.16
	14	145	1.2	0.20

Figure 7.

Machining under a cryogenic setup.

Figure 8.

Machining under MQL setup.

Practo, iXRD equipped with Cr radiation has been used to determine the stress (residual) in the turned sample, as shown in Figure 9. The figure illustrates the complete setup for measuring residual stress, which consists of the sample, detectors, the source of the X-ray tube, and others. Table 3 summarizes all the experimental parameters used for the XRD method.

Table 3.

Experimental parameters of residual stress measurement.

S. No.	Description of parameter	Values
1.	Material	AISI D2 steel, with BCC structure, hkl-211
2.	X-ray tube used	Cr-K-alpha
3.	Wavelength	2.291 Å
4.	Exposure time	5 s
5.	Direction of stress measurement	Along the feed direction
6.	Scan mode	Continuous
7.	Scan range	−30° to +30°
8.	Numbers of detectors	2 Nos
9.	Voltage	20
10.	Current intensity	mA

Figure 9.

Experimental setup for residual stress measurement.

Residual stresses were calculated using the XRD sin²ψ method. Diffraction peaks from the (211) plane of AISI D2 steel were recorded at various ψ tilts using Cr–Kα radiation. The variation of lattice spacing with sin²ψ was analyzed, and residual stresses were calculated from the slope of the linear fit. The negative slope indicates the occurrence of σCRS, which is helpful for improving fatigue performance of the machined components. XRD intensity peak profiles recorded by detector 1 and detector 2 of the machine setup. The consistency in peak fitting confirms the reliability of residual stress measurement under all the selected machining conditions.

Results and discussion

The central composite design technique has been applied to conduct the experiments. It is one of the well-known classes of second-order design, which was suggested by Khuri.³⁴ It reduces the number of experiment runs from 625 to 31 only. The experiments are executed, and values of output responses are recorded with the help of a piezoelectric-based dynamometer and Ra tester. The experimental results under all the selected cutting environments are presented in Tables 4 and 5 for cutting force and Ra, respectively.

Table 4.

DOE results of main cutting forces at different cutting environments.

S. No.	N (m/min)	F (mm/rev)	D (mm)	Β (Degree)	Cutting force F_z (N) dry	Cutting force F_z (N) wet	Cutting force F_z (N) nano MQL	Cutting force F_z (N) cryo
1	105	0.12	0.8	1	317	236	210	234
2	85	0.16	1	7	441	295	280	301
3	125	0.08	1	7	247	189	180	194
4	125	0.16	0.6	−6	285	193	225	201
5	105	0.12	0.8	1	315	234	220	236
6	125	0.16	0.6	7	276	215	207	224
7	85	0.16	0.6	−6	279	271	258	281
8	85	0.16	1	−6	450	332	314	340
9	105	0.12	0.8	1	324	240	219	245
10	125	0.16	1	7	403	304	287	308
11	85	0.08	1	7	260	190	182	197
12	125	0.08	1	−6	279	198	187	205
13	125	0.16	1	−6	456	336	319	342
14	105	0.12	1.2	1	438	360	318	347
15	105	0.12	0.8	1	325	221	210	220
16	105	0.12	0.8	1	318	235	231	241
17	145	0.12	0.8	1	435	332	254	337
18	65	0.12	0.8	1	356	233	222	234
19	85	0.16	0.6	7	273	196	187	198
20	105	0.12	0.8	1	325	241	224	246
21	105	0.2	0.8	1	430	331	326	340
22	125	0.08	0.6	−6	190	181	172	184
23	105	0.04	0.8	1	170	117	112	119
24	85	0.08	0.6	−6	180	169	162	168
25	105	0.12	0.8	14	286	207	197	211
26	105	0.12	0.8	−10	320	242	234	245
27	85	0.08	0.6	7	168	126	120	129
28	105	0.12	0.8	1	312	243	230	250
29	105	0.12	0.4	1	185	165	157	167
30	125	0.08	0.6	7	165	124	118	130
31	85	0.08	1	−6	292	218	207	217

Table 5.

DOE results of surface roughness at different cutting environments.

S. No.	N (m/min)	F (mm/rev)	D (mm)	β (degree)	R_a (µm), dry	R_a (µm), wet	R_a (µm) nano MQL	R_a (µm) cryo
1	105	0.12	0.8	1	0.43	0.32	0.26	0.31
2	85	0.16	1	7	1.2	0.71	0.58	0.75
3	125	0.08	1	7	0.94	0.69	0.57	0.73
4	125	0.16	0.6	−6	0.41	0.31	0.25	0.37
5	105	0.12	0.8	1	0.45	0.31	0.25	0.34
6	125	0.16	0.6	7	0.34	0.27	0.22	0.29
7	85	0.16	0.6	−6	0.68	0.49	0.4	0.47
8	85	0.16	1	−6	1.27	0.81	0.66	0.84
9	105	0.12	0.8	1	0.47	0.34	0.28	0.35
10	125	0.16	1	7	0.9	0.74	0.61	0.74
11	85	0.08	1	7	0.65	0.51	0.42	0.5
12	125	0.08	1	−6	0.5	0.34	0.28	0.37
13	125	0.16	1	−6	1.1	0.79	0.65	0.85
14	105	0.12	1.2	1	1.5	0.89	0.73	0.91
15	105	0.12	0.8	1	0.4	0.31	0.25	0.32
16	105	0.12	0.8	1	0.49	0.33	0.27	0.33
17	145	0.12	0.8	1	0.6	0.41	0.34	0.43
18	65	0.12	0.8	1	0.5	0.4	0.43	0.39
19	85	0.16	0.6	7	0.69	0.54	0.33	0.57
20	105	0.12	0.8	1	0.46	0.34	0.28	0.34
21	105	0.2	0.8	1	0.61	0.42	0.32	0.41
22	125	0.08	0.6	−6	0.34	0.21	0.17	0.22
23	105	0.04	0.8	1	0.34	0.21	0.18	0.24
24	85	0.08	0.6	−6	0.83	0.51	0.42	0.54
25	105	0.12	0.8	14	0.83	0.61	0.6	0.67
26	105	0.12	0.8	−10	0.46	0.34	0.28	0.37
27	85	0.08	0.6	7	0.75	0.61	0.5	0.67
28	105	0.12	0.8	1	0.43	0.31	0.25	0.32
29	105	0.12	0.4	1	0.65	0.43	0.34	0.35
30	125	0.08	0.6	7	0.61	0.49	0.5	0.51
31	85	0.08	1	−6	0.69	0.5	0.35	0.56

Figures 10 –17 show all the experimental results obtained from the measurement of residual stresses that are generated on AISI D2 steel using a TiAlN PVD-coated insert under different environmental conditions. Tables 6 and 7 show the experimental values recorded during the experiment conducted for determining the residual stresses for dry (favorable) and dry (worst) conditions, respectively. These figures contain the graphs between the sin²ψ and d-spacing, the intensity peaks curve.

Table 6.

Experimental data for various parameters under dry (favorable conditions).

Detector: 1
β Angles	Ψ	sin²ψ	d-spacing	2Θ	Strain×E3	FWHM	Breadth	Intensity
30.00	18.20	0.0976	1.170039	156.49	−0.145	4.034	5.26	795.16
25.68	13.88	0.0576	1.170169	156.43	−0.034	4.093	5.19	805.50
21.17	9.37	0.0265	1.170164	156.43	−0.038	4.051	5.07	833.74
16.32	4.52	0.0062	1.170225	156.40	0.014	4.104	4.94	799.63
11.80	0.00	0.0000	1.170201	156.41	−0.007	3.831	4.72	821.76
3.99	−7.81	0.0184	1.170263	156.38	0.047	3.971	5.00	807.71
0.00	−11.80	0.0418	1.170267	156.38	0.050	4.110	5.16	799.84
−3.99	−15.79	0.0740	1.170272	156.38	0.054	3.889	5.06	811.18
−11.80	−23.60	0.1602	1.170340	156.35	0.112	4.065	5.14	804.48
−16.32	−28.12	0.2221	1.170311	156.36	0.088	4.111	4.95	754.57
−21.17	−32.97	0.2961	1.170341	156.35	0.113	4.215	5.15	718.80
−25.68	−37.48	0.3702	1.170235	156.40	0.023	4.019	5.00	739.14
−30.00	−41.80	0.4442	1.170278	156.38	0.060	4.127	5.01	637.56
Detector: 2
βAngles	Ψ	sin²ψ	d-spacing	2Θ	Strain×E3	FWHM	Breadth	Intensity
30.00	41.80	0.4442	1.169832	156.59	−0.322	3.883	4.60	511.02
25.68	37.48	0.3702	1.169863	156.57	−0.295	3.912	4.61	517.66
21.17	32.97	0.2961	1.169979	156.52	−0.196	3.804	4.42	560.80
16.32	28.12	0.2221	1.170017	156.50	−0.164	3.830	4.36	575.31
11.80	23.60	0.1602	1.170028	156.50	−0.155	3.781	4.55	583.33
3.99	15.79	0.0740	1.170008	156.50	−0.171	3.646	4.15	611.89
0.00	11.80	0.0418	1.170018	156.50	−0.163	3.677	4.34	591.81
−3.99	7.81	0.0184	1.170177	156.42	−0.027	3.732	4.35	618.50
−11.80	−0.00	0.0000	1.170147	156.44	−0.053	3.770	4.53	616.20
−16.32	−4.52	0.0062	1.170198	156.41	−0.009	3.741	4.34	622.83
−21.17	−9.37	0.0265	1.170277	156.38	0.059	3.873	4.74	626.36
−25.68	−13.88	0.0576	1.170285	156.37	0.066	3.911	4.58	607.66
−30.00	−18.20	0.0976	1.170347	156.35	0.118	3.918	4.53	591.58

Table 7.

Experimental data for various parameters under dry (worst conditions).

Detector: 1
β Angles	Ψ	sin²ψ	d-spacing	2Θ	Strain×E3	FWHM	Breadth	Intensity
30.00	18.20	0.0976	1.169270	156.85	−0.802	5.856	7.10	875.74
25.68	13.88	0.0576	1.169470	156.76	−0.631	4.814	7.83	846.01
21.17	9.38	0.0266	1.170001	156.51	−0.177	6.055	7.53	791.43
16.32	4.53	0.0062	1.170229	156.40	0.017	5.190	7.03	799.86
11.80	−0.00	0.0000	1.170468	156.29	0.222	5.240	6.68	828.23
3.99	−7.80	0.0184	1.170705	156.18	0.424	5.197	6.78	773.60
0.00	−11.80	0.0418	1.170425	156.31	0.185	5.192	7.05	750.05
−3.99	−15.79	0.0740	1.170082	156.47	−0.109	4.953	7.02	752.49
−11.80	−23.59	0.1602	1.169774	156.61	−0.371	3.593	6.31	750.09
−16.32	−28.12	0.2221	1.169535	156.73	−0.575	5.269	6.73	729.73
−21.17	−32.97	0.2961	1.169825	156.59	−0.328	5.177	6.82	732.42
−25.68	−37.47	0.3701	1.168882	157.04	−1.134	5.345	6.76	725.41
−30.00	−41.80	0.4442	1.169315	156.83	−0.764	4.952	6.85	700.74
Detector: 2
β Angles	Ψ	sin²ψ	d-spacing	2Θ	Strain×E3	FWHM	Breadth	Intensity
30.00	41.80	0.4442	1.168530	157.21	−1.434	−0.302	5.70	566.99
25.68	37.47	0.3701	1.168084	157.43	−1.816	4.014	5.03	535.66
21.17	32.97	0.2961	1.167936	157.50	−1.942	5.130	5.41	539.74
16.32	28.12	0.2221	1.168307	157.32	−1.625	4.097	5.24	592.56
11.80	23.59	0.1602	1.169072	156.95	−0.971	5.271	5.21	602.00
3.99	15.79	0.0740	1.169382	156.80	−0.706	3.980	4.74	664.56
0.00	11.80	0.0418	1.170273	156.38	0.055	4.256	5.37	661.80
−3.99	7.80	0.0184	1.170005	156.51	−0.174	4.123	4.77	608.84
−11.80	0.00	0.0000	1.170345	156.35	0.116	4.205	4.94	659.13
−16.32	−4.53	0.0062	1.170340	156.35	0.112	3.909	4.52	633.51
−21.17	−9.38	0.0266	1.170295	156.37	0.074	4.287	4.93	627.68
−25.68	−13.88	0.0576	1.169745	156.63	−0.396	3.968	4.44	596.89
−30.00	−18.20	0.0976	1.170284	156.37	0.065	3.792	4.65	643.41

Figure 10.

Graph and intensity peak curve under dry (favorable condition).

Figure 11.

Graph and intensity peak curve under wet (favorable condition).

Figure 12.

Graph and intensity peak curve under nano MQL (favorable condition).

Figure 13.

Graph and intensity peak curve under cryogenic (favorable condition).

Figure 14.

Graph and intensity peak curve under dry (worst condition).

Figure 15.

Graph and intensity peak curve under wet (worst condition).

Figure 16.

Graph and intensity peak curve under nano MQL (worst condition).

Figure 17.

Graph and intensity peak curve under cryogenic (worst condition).

According to Figures 10 –17, the shift in diffraction peaks observed in the intensity peak curves is directly correlated with changes in interplanar lattice spacing (d-spacing), which occurs due to the presence of residual stresses in the material. As per the results, diffraction peaks are obtained at different tilt angles. These shifts indicate changes in lattice strain caused by residual stresses developed on the machined surface. Similarly, the slope variation in d-spacing with respect to sin² ψ also confirmed the magnitude of residual stress generation. A shift of the diffraction peak toward higher diffraction angles indicates σCRS caused by a reduction in lattice spacing. The negative stress value obtained from the XRD analysis indicates that the generated residual stresses are mainly compressive in nature. The good linearity of the plot also validates the reliability of the XRD residual stress measurement using the d–sin² ψ method. Figure 18 shows the results of residual stresses that are generated under different conditions at the favorable levels and worst levels of cutting parameters. It has been found that the generated stresses(residual) are compressive in nature in all cutting conditions. The σCRS are beneficial for the fatigue life of the product. Higher feed, depth of cut produces higher cutting forces resulting in large σCRS. Generally, during a hard turning operation, σCRS are generated. According to published research, mechanical effects favor σCRS while heat effects encourage the production of σTRS. High hardness of the material is also responsible for generating the σCRS.^35–37 With the use of effective cooling techniques such as nano-based MQL and cryogenic cooling, the dominance of thermal effects over mechanical effects was restricted, resulting in the generation of σCRS. Low σCRS are generated under dry conditions, and high σCRS are generated under cryogenic conditions. Substantial differences in the generation of residual stress were found under the same environmental conditions, only by varying the process parameters, which proves that the process parameters strongly influence the residual stresses.

Figure 18.

Comparison of residual stresses under favorable conditions (β = 1°, D = 0.4 mm, F = 0.04 mm/rev, and N = 105 m/min) and worst conditions (β = −6°, D = 1 mm, F = 0.16 mm/rev, and N = 85 m/min).

The present study was conducted under hard turning conditions. The high hardness and high yield strength of the selected material, AISI D2 steel, restrict bulk plastic deformation. Due to which deformation becomes at the localized manner under the subsurface of the AISI D2 steel. Even the generation of high cutting forces results in high plastic deformation and a significant strain hardening effect in a localized manner. After the cutting tool passes, the layer under the subsurface attempts for elastic recovery, but the recovery under such effect is found to be limited. As a result, σCRS are accumulated into the machined surface.

Analysis of 3D surface topography

The 3D surface topography under worst and favorable conditions under various cutting environments is shown in Figure 19. According to the figure, there are well-defined peaks and valleys visible on the machined surfaces. The geometry of the single-point cutting tool and process parameters are responsible for defining the topographical behavior of the machined surfaces.

Figure 19.

3D surface topography under favorable conditions (a) dry, (b) wet, (c) nano MQL, (d) cryogenic, and worst conditions (e) dry, (f) wet, (g) nano MQL, (h) cryogenic.

While comparing the surface generation under favorable and worst conditions, it has been concluded that favorable conditions under all the environments produce better surface quality than the worst conditions. A higher degree of peaks and valleys has been found under the worst conditions. Higher cutting speed, feed, and depth of cut cause higher temperature, highly stressed cutting edge, and more chatter when compared to the favorable turning condition, thereby increasing waviness and Ra values of the machined surface. However, it was found that under nano MQL conditions, the degree of Ra is better compared to other environmental conditions for both the worst and the most favorable conditions. Figure 19 shows the Ra values measured through 3D surface topography analysis under favorable and worst conditions for selected environments.

According to Figure 20, it is depicted that good Ra is achieved for nano-based MQL machining and cryogenic-based machining due to efficient cooling at the chip tool interface.

Figure 20.

Surface roughness value achieved under different cutting environments.

Conclusions

The following are the conclusions that may be taken from the current research work.

Higher cutting parameters such as feed, speed, depth of cut, and negative rake angle lead to greater plastic deformation during the machining process. This increases the cutting forces, which in turn generate larger σCRS, especially when machining under extreme conditions. The findings highlight the need for careful selection of cutting parameters to balance productivity and minimize the detrimental effects of excessive cutting forces.

Consistent with the existing literature, σCRS are typically generated during hard turning operations. In this study, all recorded residual stresses were found to be compressive in nature, confirming the general tendency for hard turning to induce benefits like enhancement of fatigue resistance of the machined components and improvement in product life cycle.

σCRSs are beneficial for enhancing the fatigue life of a component by reducing the likelihood of crack initiation. In contrast, σTRS can lead to premature failure by promoting crack growth and reducing the component’s overall performance. This highlights the importance of inducing compressive stresses through appropriate machining and cooling techniques to improve component longevity.

Thermal factors tend to dominate in the generation of σTRS, whereas mechanical effects are primarily responsible for inducing σCRSs. Under dry cutting conditions, high temperatures are generated, which reduce the compressive stresses, and lower levels of σCRS are produced. This shows that there is a critical role of cooling methods in controlling residual stress development.

Both cryogenic cooling and nano Al₂O₃-based MQL were found to generate higher levels of σCRSs compared to dry cutting conditions. These advanced cooling techniques help mitigate thermal effects, thereby promoting favorable residual stress profiles that enhance surface integrity and fatigue resistance.

Efficient cooling methods, such as nano-based MQL, and cryogenic cooling, effectively limit the influence of thermal effects, which typically generate tensile stresses. By enhancing the cooling efficiency, these techniques maintain the mechanical dominance in the cutting process, leading to the production of high σCRSs that are beneficial for improving the component’s overall performance and durability.

The inherent high hardness of AISI D2 steel plays a significant role in the generation of high σCRSs during machining. The material’s hardness contributes to greater resistance to deformation, which results in increased cutting forces and, consequently, the generation of beneficial σCRSs that improve fatigue resistance.

The 3D surface topography analysis indicates that MQL conditions based on nano Al₂O₃ result in more accurate and smoother machined surfaces. This improved surface finish, in combination with the high σCRSs generated under this cooling strategy, enhances both the functional performance and lifecycle of the machined components. This finding reinforces the effectiveness of nano-based MQL as a cooling and lubrication strategy for high-quality machining.

Footnotes

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Vaibhav Chandra

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