Analysis of Thermal Aspect in Hard Turning of AISI 52100 Alloy Steel Under Minimal Cutting Fluid Environment Using FEM

Abstract

This paper describes a simulation study on the hard turning of AISI 52100 alloy steel with coated carbide tools under minimal cutting fluid conditions using the commercial software AdvantEdge. A finite element analysis coupled with adaptive meshing was carried out to accurately capture temperature gradients. To minimise the number of experiments while optimising the cutting parameters along with fluid application parameters, a cutting speed (v) of 80 m/min, feed rate (f) of 0.05 mm/rev, depth of cut (d) of 0.15 mm, nozzle stand-off distance (NSD) of 20 mm, jet angle (JA) of 30°, and jet velocity (JV) of 50 m/s were observed to be the optimal process parameters based on the combined response’s signal-to-noise ratios. The effects of each parameter on machined surface temperature, cutting force, cutting temperature, and tool–chip contact length were determined using ANOVA. The depth of cut affected cutting force, while cutting speed and jet velocity affected cutting temperature and tool–chip contact length. Cutting speed influenced machined surface temperature significantly, whereas other parameters showed minimal effect. Nozzle stand-off distance exhibited less significant effect. Taguchi optimisation determined the optimal combination of process parameters for minimising thermal effects during hard turning. Cutting temperature and cutting force simulation results were found to be highly consistent with experimental results. On the other hand, the simulated results for the tool–chip contact length and machined surface temperature were very close to the values found in the literature. The result validated the finite element model’s ability to accurately simulate thermal behaviour during hard-turning operations.

1. Introduction

Hard turning evolved into a crucial machining method in the manufacturing sector for machining hardened steel to replace the conventional grinding process. The better accuracy in terms of tolerances, excellent surface finish, superior product quality, reduced machining time, reduced operation cost, and eco-friendly features are some of the advantages this process offers. Samantaraya & Lakade, 2020 [1] reported the increasing demand for hard-turned components exhibiting superior surface integrity, hence necessitating the optimisation of the hard-turning process to enhance the surface quality. Anand et al., 2019 [2] stated that hard turning normally uses flood coolant to overcome issues such as heat generation, tool wear, and deterioration in surface integrity. The use of flood coolant has significant economic and environmental impact, such as excessive coolant usage, disposal expenses, and health risks, resulted in a growing interest in sustainable machining practices, especially techniques that minimise the use of cutting fluid. Mane et al., 2025 [3] reported that the minimal cutting fluid application (MCFA) was an efficient high-velocity pulsed-jet method that emerged as an eco-friendly strategy to mitigate environmental impacts and enhance surface integrity in machining processes. The finite element method (FEM) is an efficient substitute for experimental methodologies that provides exact and accurate predictions of physical processes. The FEM allows the in-depth analysis of the metal cutting process, addressing the issues such as fracture mechanics, heat transfer, material behaviour in plastic and elastic state, metallurgy, and the implications of coolant application. Yan et al., 2005 [4] used ABAQUS/explicit to predict the cutting force and temperature distribution in the milling of AISI H13 steel with a hardness of 58 HRC. Nasr et al., 2007 [5] developed an Arbitrary Lagrangian–Eulerian (ALE) finite element approach to assess the effects of the cutting edge radius on the residual stresses during the orthogonal dry cutting of AISI 316L austenitic stainless steel. Mamalis et al., 2008 [6] used AdvantEdge software (version 7.8) for orthogonal and oblique cutting in high-speed hard turning. The result indicated that the cutting force and cutting temperature were significantly affected by cutting conditions. Kalhori et al., 2010 [7] presented a physical material-based model to simulate the metal cutting process, which predicted chip formation and cutting forces. The result showed that FEM simulations could optimise coolant flow parameters to enhance machining performance. Maňková et al., 2011 [8] conducted FEM simulation using AdvantEdge software to examine the influence of cutting parameters on the hard turning of AISI 1045 alloy steel using a mixed oxide ceramic insert. The model investigated the effects of cutting parameters on chip formation, cutting forces, workpiece deformation, temperature distribution, and heat fluxes within the workpiece and cutting tool inserts. A comparison between model validation and experimental data had a discrepancy of less than 10%. Attanasio et al., 2012 [9] proposed an FEM model to study the effects of the cutting parameters on tool wear and white layer formation in the orthogonal machining of hardened AISI 52100 steel with PCBN inserts. FE models were compared with the experimental results for tool wear and microstructural changes.

Hu et al., 2013 [10] used DEFORM-3D software to examine the cutting forces and temperatures in AISI 1013 steel with nano-crystalline Al₂O₃ ceramic cutting tool inserts under high-pressure cooling conditions. The results emphasised the significance of tool material and coolant delivery in enhancing machining performance. Ezilarasan et al., 2014 [11] conducted FEM simulations to investigate the effect of cutting and fluid application parameters on the cutting temperature, stress, and strain involved during machining of Nimonic C-263 alloy. The experimental results confirmed the accuracy of the FEM predictions of coupled thermomechanical effects. Banerjee & Sharma, 2014 [12] developed a friction model using AdvantEdge software in a milling process with a minimum-quantity-lubrication environment. The model analysed the effects of machining parameters on the cutting temperature and tool–chip contact length. Ma et al., 2015 [13] applied AdvantEdge FEM software to simulate the 3D turning process of titanium-alloy (Ti-6Al-4V) with carbide inserts to investigate the cutting forces and chip formation.

Yadav et al., 2015 [14] simulated the turning process using DEFORM-3D software and experimentally validated the simulation results. The results indicated that the cutting speed and feed rate were the main factors influencing the tool wear and material removal rate. That method proved that the FEM simulations were useful in improving the tool life and the efficiency of the process. Qasim et al., 2015 [15] implemented the machining simulation on AISI 1045 steel using Abaqus software. FEM simulations demonstrated the influence of different cutting speeds, feed rates, and coolant pressures on cutting forces and cutting temperature. The results derived useful insights into the optimisation of machining parameters to reduce the cutting force and cutting temperature. Bapat et al., 2015 [16] simulated the temperature distribution during the turning of AISI 52100 steel with a 2D numerical model. The Johnson–Cook constitutive model was employed to represent workpiece material, incorporating temperature dependence into Young’s modulus and the Poisson ratio. The distributions of temperature along with chip morphology were studied, and simulated results were compared with experimental data, showing an average variation of 13%. Orthogonal cutting processes were modelled by Malakizadi et al., 2016 [17] with FEM simulation in DEFORM-2D, Abaqus, and AdvantEdge software to investigate the influence of different friction models on the outcomes of simulations. The data showed a very crucial role of friction modelling to make precise cutting force, chip morphology, and temperature rise predictions. The result highlighted that the coolant modelling needed to be performed properly for a significant influence in the field of coolant-assisted machining. Zhang et al., 2016 [18] discussed the changes in cutting forces during the high-speed machining (HSM) of Inconel 718 and highlighted the significant role of coolant assistance during machining to achieve the stability in cutting forces. It showed that high-pressure cooling (HPC) reduced temperature-related variations in the cutting forces and led to improved dimensional accuracy.

Kundrák et al., 2017 [19] developed a thermal model using the finite volume method to study various aspects of thermal phenomena in hard turning, including the formation of a white layer. Sadeghifar et al., 2018b [20] conducted a finite element simulation to examine the cutting forces, cutting temperature, and residual stresses in the orthogonal turning of 300Me steel and validated the results through the experimental run. Sadeghifar et al., 2018a [21] conducted a comprehensive comparison of various metal-cutting simulation software, such as DEFORM, AdvantEdge, and Abaqus, focusing on the chip formation process and its effects on surface integrity. Grzesik et al., 2018 [22] analysed tool wear during the turning of Inconel 718, demonstrating the influence of coolant pressure on enhancing the process performance and tool life. The study found that high coolant pressure decreased tool wear, thereby increasing the overall machining performance. Kaynak et al., 2018 [23] conducted a comparison of flood cooling, minimum quantity lubrication (MQL), and HPC in the machining of titanium alloys. HPC was found to have a better performance compared to other alternative cooling technologies in the reduction in tool wear and improvement of surface integrity. Jaafar & Al-Ethari, 2018 [24] used numerical simulations coupled with experimental validation to improve the machining of Ti6Al4V alloy. It was reported that the proper control of coolant flow and pressure led to a decrease in tool wear and enhanced surface quality. These research studies reflect the significance of the FEM in simulating the complex interactions among coolant parameters. Mishra et al., 2019 [25] performed a finite element study to analyse the impact of cutting speed and depth on residual stresses when turning Ti-6Al-4V. The results obtained from the model were compared with experimental values for validation. Rosli et al., 2019 [26] developed an FEM approach to model the MQL characteristics in the orthogonal cutting of medium steel JIS S45C with a TiCN-coated cermet tool using DEFORM-3D software. The result showed that FEM could effectively evaluate the characteristics of MQL by modelling the input friction and chip morphology, facilitating the differentiation between contact and environmental conditions.

Luo et al., 2020 [27] performed advanced simulations on turning of 7075-T651 aluminium alloy using AdvantEdge software. In that work, various cutting parameters were investigated to analyse their influence on cutting force, cutting temperature, and residual stress. The simulation results showed a good agreement with experimental data, proving the accuracy of the model. Wu et al., 2020 [28] created a thermomechanically coupled finite element model using Deform-3D to examine the high-pressure cooling machining of GH4169. The model analysis focused on cutting temperatures and surface residual stresses, revealing that elevated cooling pressure led to a decrease in residual tensile stress on the machined surface. Liu et al., 2020 [29] developed a two-dimensional finite element model with AdvantEdge FEM software. The influence of cutting parameters and coolant pressure was examined through simulation and experimental testing. The cutting force was primarily affected by the cutting depth, followed by the feed rate and coolant pressure, while the cutting speed had the least impact. The feed rate, coolant pressure, cutting speed, and cutting depth emerged as the most critical parameters affecting the machined surface roughness. The relative error range of 5.1% to 15.3% between the simulation and experimental data indicated a strong concordance between the two approaches. Eltaggaz et al., 2020 [30] applied an integrated numerical model to simulate the cutting heat distribution on cutting tools during the machining of austempered ductile iron (ADI) in both dry and MQL conditions. The simulated temperature values exhibited a high degree of consistency with the experimental data, with a 5.74% error for dry cutting and a 10.74% margin for MQL. Javidikia et al., 2021 [31] studied the influence of machining parameters on temperature, cutting forces, and axial surface residual stresses (ASRSs) developed during the turning of AA6061-T6 under a dry, wet, and MQL environment with the generation of a 3D FEM model. The results indicated that an increased feed rate resulted in higher ASRSs, whereas a tool nose radius of 0.4 mm resulted in lower ASRSs. Further, the study validated the relative dominance of machining forces and temperature on the residual stresses. Nouzil et al., 2021 [32] conducted a numerical investigation of the machining of Inconel 718 and studied the effect of the jet radius, location, and cutting speed on the cutting forces and power. The shear angle and chip compression ratio were evaluated for a better understanding of the metal cutting process. It was established during the investigation that a change in jet location resulted in a change in the shear angle, which in itself increased the shear angle, thereby reducing the shear area and consequently cutting forces and power.

Akeel et al., 2022 [33] discussed the selection of appropriate cutting tool, cutting fluid, cutting fluid application strategies, and simulation techniques in the machining of difficult-to-cut alloys that overcome the challenges such as tool wear, poor surface finish, and higher temperatures. Ullah et al., 2022 [34] developed a numerical and experimental approach to predict residual stresses in milled Ti-6Al-4V alloy. The model showed excellent correlation with experimental results, and the effect of the white layer on the distribution of stress was investigated. This method offers a comprehensive understanding of residual stress in milled components. Weng et al., 2023 [35] developed a three-dimensional Coupled Eulerian–Lagrangian (CEL)-based numerical model to predict residual stress evolution during multiple consecutive cuts in turning. The validation showed strong agreement between simulation results and experimental measurements. Hao et al., 2024 [36] examined the impact of tool coatings on the distribution of cutting temperature while performing orthogonal cutting on H13-hardened steel. Soori et al., [37] explored coolant effects on cutting temperature, surface roughness, and tool wear in the turning of Ti-6Al-4V titanium alloy, using virtual machining systems, a modified Johnson–Cook methodology, a coupled Eulerian–Lagrangian approach, and the Takeyama–Murata analytical model. The result revealed that the cutting temperature, surface quality, and tool wear could be accurately predicted to enhance the accuracy with and without coolant using virtual machining systems. Feng et al., 2024 [38] proposed an FEM-based simulation model to investigate the residual stresses during the turning of GH4169 under spray cooling conditions. The findings indicated that the residual tensile stresses decreased and stabilised with higher spray pressure, while it initially decreased and then increased with higher flow rates. Wang et al., 2024 [39] suggested a hybrid analytical–FEM model to predict the machining response under varying MQL parameters. High-speed milling experiments validated the model’s correctness, as the simulated cutting force and residual stress values nearly matched the experimental outcomes. That study facilitated the optimisation of MQL parameters for desirable results. Khetre et al., 2024 [40] examined the application of a coconut-oil-based SiC–MWCNT nano-cutting fluid in MQL turning with a CBN cutting tool insert, leading to reduced tool temperature and enhanced tool life. A comparison of the projected values using the FEM with the experimental data, demonstrated a significant consistency with a marginal error ranging from 1.27% to 3.44%.

Hard turning is an essential machining technique in the manufacturing industry, providing exact tolerances, enhanced surface finish, reduced machining duration, and environmentally sustainable features. Nevertheless, the utilisation of flood coolant may result in economic and environmental challenges. The study of machining utilising diverse cooling technologies, such as flood cooling, minimal quantity lubrication (MQL), and HPC, has demonstrated enhanced efficacy in reducing tool wear and enhancing surface integrity. The finite element method (FEM) serves as an efficient alternative to experimental techniques, offering precise predictions of physical phenomena. The literature on thermal considerations in hard turning emphasises its precision and efficiency for machining hard-to-cut material with FEM simulations providing insights into cutting forces, temperature distribution, and residual stresses. Cutting parameters such as cutting speed, feed rate, and depth of cut, along with cooling techniques such as flood cooling, HPC, MQL, and MCFA, significantly influence thermal performance. Advanced FEM tools like AdvantEdge and DEFORM-3D enable accurate modelling, validated by experiments. Recent studies have explored multi-pass machining and tool coatings for improved thermal control, but gaps remain in integrating cutting and cutting fluid application parameters for optimised and sustainable machining solutions.

The literature highlights the importance of using the FEM in investigating the valuable insights into the thermal aspect of the machining process, but there is a gap in achieving highly accurate models that consider complex heat transfer mechanisms, such as the tool–chip interface, tool–workpiece interface, and the material’s thermal conductivity during hard turning. Current models often lack the precision required for real-world applications, especially for the high-precision machining of hardened steels. Also, numerous studies focus on either cutting parameters or cutting fluid application individually, and there is limited research that integrates both factors to understand their combined effect on thermal performance. More comprehensive models are needed that simultaneously consider these parameters. Future research should focus on selecting appropriate cutting fluid, application strategies, cutting tools, and simulation methods for better results.

This research gap emphasises the need to develop precise FEM models that integrate intricate heat transfer mechanisms and examine the combined effect of cutting parameters and cutting fluid application parameters on thermal performance in hard turning process. The objectives of this research are as follows:

• To develop an integrated FEM model that incorporates both cutting parameters and cutting fluid application parameters to study thermal behaviour in hard turning process.
• To analyse the effect of cutting parameters and cutting fluid application parameters on cutting force, cutting temperature, machined-surface temperature and too–chip contact length during the hard turning of AISI 52100 alloy steel using the FEM.
• To validate FEM predictions with experimental results to ensure the reliability of thermal modelling for industrial applications.
• Currently, metal-cutting simulations are performed with software like ABAQUS, DEFORM, and AdvantEdge. An effort has been undertaken to study the thermal aspect with respect to cutting temperature, machined-surface temperature, tool–chip contact length, and cutting forces in hard turning at different cutting and cutting fluid application parameters using the FEM.

2. Materials and Methods

A finite element analysis is an essential and integral tool for the simulation of machining processes. This method facilitates the analysis of the metal cutting process with all its difficulties in terms of fracture mechanics, heat transfer, performance in the plastic and elastic state of a material, metallurgy, and the effect of using coolants.

The FEM-based commercial software “AdvantEdge” was used to predict the thermal effects occurring during hard turning with a coated carbide tool insert under a minimal cutting fluid environment. AdvantEdge is an explicit dynamic Lagrangian code that can perform a conjugate thermomechanical transient analysis. The program uses an adaptive meshing and a continuous meshing for chips and workpieces, which ensure accurate results. It has an extensive database of workpieces and tool materials that are typically used in cutting operations and provides all the data necessary for efficient material modelling. The workpiece material, cutting tools, and process settings were modelled from the software menu and data library. Figure 1 depicts the range of inputs and outputs related to the AdvantEdge machining software. Inputs include comprehensive specifications for the cutting tool, workpiece, and process parameters. The advanced options for mesh grading and element sizing facilitate customisation. Outputs include the essential aspects of machining such as cutting forces, cutting temperature, plastic strain, maximum shear stress, and residual stress. This extensive framework allows the accurate simulation and analysis of machining operations to enhance performance and material behaviour.

2.1. Material Constitutive Model

This study utilised AISI 52100 hardened alloy steel with a hardness rating of 58 HRC. This material is extensively used in the automotive sector for high-wear applications, such as bearings, spindles, and forming rolls, due to its superior wear resistance and capacity for high-quality surface finishes.

The material model is a crucial aspect of a finite element simulation, significantly influencing the accuracy of the results. To ensure precise simulations, the power law material model was selected to conduct the simulation [41]. The power law comprises a strain-hardening function, denoted as g$\left({\mathsf{\epsilon }}^{\mathrm{p}}\right)$, and a thermal-softening function, denoted as $\mathsf{\theta }\left(\mathrm{T}\right)$. The strain rate sensitivity function is represented as $\mathsf{\Gamma }\left(\dot{\mathsf{\epsilon }}\right)$. The material’s constitutive model governed by the power law is illustrated in Equation (1) [42]:

$$\mathsf{\sigma }\left({\mathsf{\epsilon }}^{\mathrm{p}},\dot{\mathsf{\epsilon }},\mathrm{T}\right)=\mathrm{g}\left({\mathsf{\epsilon }}^{\mathrm{p}}\right)·\mathsf{\Gamma }\left(\dot{\mathsf{\epsilon }}\right)·\mathsf{\theta }\left(\mathrm{T}\right)$$

(1)

The strain-hardening function for the power law is defined as in Equations (2) and (3):

$$\mathrm{g}\left({\mathsf{\epsilon }}^{\mathrm{p}}\right)={\mathsf{\sigma }}_0{\left(1+{\displaystyle \frac{{\mathsf{\epsilon }}^{\mathrm{p}}}{{\mathsf{\epsilon }}_0^{\mathrm{p}}}}\right)}^{1/\mathrm{n}}, \mathrm{i}\mathrm{f} {\mathsf{\epsilon }}^{\mathrm{p}}<{\mathsf{\epsilon }}_{\mathrm{c}\mathrm{u}\mathrm{t}}^{\mathrm{p}}$$

(2)

$$\mathrm{g}\left({\mathsf{\epsilon }}^{\mathrm{p}}\right)={\mathsf{\sigma }}_0{\left(1+{\displaystyle \frac{{\mathsf{\sigma }}_{\mathrm{c}\mathrm{u}\mathrm{t}}^{\mathrm{p}}}{{\mathsf{\epsilon }}_0^{\mathrm{p}}}}\right)}^{1/\mathrm{n}}, \mathrm{i}\mathrm{f} {\mathsf{\epsilon }}^{\mathrm{p}}\ge {\mathsf{\epsilon }}_{\mathrm{c}\mathrm{u}\mathrm{t}}^{\mathrm{p}}$$

(3)

where ${\mathsf{\sigma }}_0$ is the initial yield stress, ${\mathsf{\epsilon }}^{\mathrm{p}}$ is the plastic strain, ${\mathsf{\epsilon }}_0^{\mathrm{p}}$ is the reference plastic strain, ${\mathsf{\epsilon }}_{\mathrm{c}\mathrm{u}\mathrm{t}}^{\mathrm{p}}$ is the cut-off strain, and $\mathrm{n}$ is the strain-hardening exponent. During the metal cutting process, the material undergoes deformation in both the primary and secondary cutting zones. This deformation takes place at high temperatures and involves extremely high stresses and strain rates (ranging from 10⁵ to 10⁷ s⁻¹). Conversely, the rest of the workpiece undergoes deformation at moderate or even modest rates of strain. The strain-rate sensitivity function is provided by Equations (4) and (5):

$$\mathsf{\Gamma }\left(\dot{\mathsf{\epsilon }}\right)={\left(1+{\displaystyle \frac{\dot{\mathsf{\epsilon }}}{{\dot{\mathsf{\epsilon }}}_0}}\right)}^{{\displaystyle \frac{1}{{\mathrm{m}}_1}}}, \mathrm{i}\mathrm{f} \dot{\mathsf{\epsilon }}\le {\dot{\mathsf{\epsilon }}}_1$$

(4)

$$\mathsf{\Gamma }\left(\dot{\mathsf{\epsilon }}\right)={\left(1+{\displaystyle \frac{\dot{\mathsf{\epsilon }}}{{\dot{\mathsf{\epsilon }}}_0}}\right)}^{{\displaystyle \frac{1}{{\mathrm{m}}_2}}}{\left(1+{\displaystyle \frac{{\dot{\mathsf{\epsilon }}}_1}{{\dot{\mathsf{\epsilon }}}_0}}\right)}^{\left({\displaystyle \frac{1}{{\mathrm{m}}_1}}{\displaystyle \frac{1}{{\mathrm{m}}_2}}\right)}, \mathrm{i}\mathrm{f} \dot{\mathsf{\epsilon }}>{\dot{\mathsf{\epsilon }}}_1$$

(5)

where $\dot{\mathsf{\epsilon }}$ is the strain rate, ${\dot{\mathsf{\epsilon }}}_0$ is the reference plastic strain rate, ${\dot{\mathsf{\epsilon }}}_1$ is the strain rate, where the transition between low- and high-strain sensitivity occurs, ${\mathrm{m}}_1$ is the low-strain-rate sensitivity coefficient, and ${\mathrm{m}}_2$ is the high-strain-rate sensitivity coefficient.

The thermal-softening function $\mathsf{\theta }\left(\mathrm{T}\right)$ is defined by Equations (6) and (7):

$$\mathsf{\theta }\left(\mathrm{T}\right)={\mathrm{c}}_0+{\mathrm{c}}_1\mathrm{T}+{\mathrm{c}}_2{\mathrm{T}}^2+{\mathrm{c}}_3{\mathrm{T}}^3+{\mathrm{c}}_4{\mathrm{T}}^4+{\mathrm{c}}_5{\mathrm{T}}^5, \mathrm{i}\mathrm{f} \mathrm{T}<{\mathrm{T}}_{\mathrm{c}\mathrm{u}\mathrm{t}}$$

(6)

$$\mathsf{\theta }\left(\mathrm{T}\right)=\mathsf{\theta }\left({\mathrm{T}}_{\mathrm{c}\mathrm{u}\mathrm{t}}\right)-\left({\displaystyle \frac{\mathrm{T}-{\mathrm{T}}_{\mathrm{c}\mathrm{u}\mathrm{t}}}{{\mathrm{T}}_{\mathrm{m}\mathrm{e}\mathrm{l}\mathrm{t}}-{\mathrm{T}}_{\mathrm{c}\mathrm{u}\mathrm{t}}}}\right), \mathrm{i}\mathrm{f} \mathrm{T}\ge {\mathrm{T}}_{\mathrm{c}\mathrm{u}\mathrm{t}}$$

(7)

where ${\mathrm{c}}_0$ through ${\mathrm{c}}_5$ are coefficients for the polynomial fit, T is the temperature, ${\mathrm{T}}_{\mathrm{c}\mathrm{u}\mathrm{t}}$ is the cut-off temperature, and ${\mathrm{T}}_{\mathrm{m}\mathrm{e}\mathrm{l}\mathrm{t}}$ is the melting temperature.

The thermomechanical properties play a crucial role in defining the constitutive model parameters in an AdvantEdge machining simulation, influencing strain hardening, strain-rate sensitivity, and thermal-softening behaviour. In an AdvantEdge machining simulation, the selection of workpiece material offers two options: standard and custom. The standard option provides a comprehensive material library with predefined mechanical and thermal properties, ensuring consistency and validated accuracy. The custom option allows users to define specific material properties, tailoring parameters such as the elastic modulus, yield strength, strain-hardening exponent, and thermal characteristics to better match experimental conditions. AdvantEdge inherently incorporates these material characteristics into its constitutive models automatically, defining key constants governing strain hardening, strain-rate sensitivity, and thermal softening.

Table 1. Thermomechanical properties AISI 52100 alloy steel (58 HRC).

Sr. No.	Material Properties		Magnitudes
01	Elastic modulus	:	200–210 GPa
02	Yield strength	:	2000 MPa
03	Ultimate tensile strength	:	2500 MPa
04	Hardness	:	58 HRC
05	Density	:	7.81 g/cm³
06	Poisson’s ratio	:	0.27–0.39
07	Thermal conductivity	:	21–46.6 W/m°C, (f(t)-temperature-dependent)
08	Specific heat capacity	:	460 J/kg·K f(t)
09	Thermal expansion coefficient	:	11.3–15.3 × 10⁻⁶ K⁻¹ f(t)
10	Strain-hardening exponent	:	0.08–0.15
11	Strain-rate sensitivity exponent	:	0.01–0.03
12	Strain rate (ε˙)	:	105 to 107 s⁻¹
13	Emissivity	:	0.6–0.7
14	Cut-off temperature	:	1200–1400 °C

presents the thermomechanical properties of tool workpiece materials.

2.2. Friction Modelling

The simulation results can be significantly influenced by the friction coefficient between the tool and the workpiece. AdvantEdge simulation software adopts the classical sliding friction approach to simulate the friction that occurs at the contact point between the tool and the chip. The concept is founded on the principle that the force of frictional sliding is directly proportional to the normal load. The relationship between these forces is defined by the coefficient of friction ($\mathsf{\mu }$), which is considered a constant value during contact between the tool and the chip. This model considers the combined length of the adhesive and sliding areas as the tool–chip contact length. The Coulomb friction model is represented by Equation (8) [42].

$${\mathrm{F}}_{\mathrm{f}}=\mathsf{\mu }{\mathrm{F}}_{\mathrm{n}}$$

(8)

where ${\mathrm{F}}_{\mathrm{n}}$ is the normal force acting between the surfaces, ${\mathrm{F}}_{\mathrm{f}}$ is the resulting force due to friction, and $\mathsf{\pi }$ is the coefficient of friction. This study selected a coefficient of friction (COF) of 0.6, aligned with established tribological behaviour, thermomechanical conditions, and validated numerical modelling approaches. The selected value for the COF provided a realistic cutting force, cutting temperature, and temperature distribution, ensuring the accuracy and reliability of simulation results.

Figure 2 depicts a turning simulation interface that enables the user to input workpiece dimensions and simulate the machining process. It also enables the modification of initial stress settings through file inputs. The study examined the thermal and mechanical effects of cutting processes, assuming a stress-free initial state for accurate material behaviour representation. The setup provides a visual representation of the turning process for further evaluation.

2.3. Tool Modelling

The WC-Co sintered carbide tool coated with three layers (TiN/TiCN/Al₂O₃) of coating was modelled as a rigid body, constrained to move only in the cutting direction at the prescribed cutting parameters. Tool coating within AdvantEdge was applied using a thin layer of an explicitly meshed coating. Three layers of coating (Al₂O₃: 5 µm, TiCN: 3 µm, and TiN: 2 µm) were selected. The 2 µm TiN layer was considered the inner-most layer, and each additional layer fell outside the previous. A tetrahedral-type mesh was used for the meshing process. A total of 24,000 nodes were applied for the mesh generation, with a mesh grade of 0.4 and element sizes of 0.05 mm and 0.01 mm, respectively. Figure 3 depicts the tool coating parameters.

Temperature-dependent thermal conductivity values were used to ensure realistic predictions. For the multilayer coated carbide tool, the thermal conductivity varied significantly due to the layered structure. The coating materials, such as TiN, TiCN, and Al₂O₃, exhibited low thermal conductivity (7–33 W/m·K), acting as thermal barriers that reduced heat dissipation into the tool. In contrast, the tungsten carbide substrate had a significantly higher thermal conductivity (30–47.5 W/m·K), enabling heat conduction away from the cutting zone. Tool–workpiece and tool–chip interactions were modelled using friction coefficients μ = 0.4–0.7 for hard turning and heat partitioning ratios of 80–85% heat into the chip, 10–15% into the workpiece, and 3–7% into the tool. The thermal properties were incorporated based on the established literature to ensure accurate FEM-based thermal modelling and a realistic temperature distribution in the simulation.

Table 2. Thermomechanical properties of coating materials.

Material	Al2O3	TiCN	TiN	WC: Uncoated
Coating thickness (μm)	3–5	4–8	1.5–3	-
Hardness (HV)	2000	3000	2300	-
Thermal expansion coefficient (×10−6; K−1)	8.4	8	9.4	5
Modulus of elasticity (GPa)	415	448	600	650
Poisson ratio	0.22	0.23	0.25	0.25
Density (kg/m3)	3780	4180	4650	11,900
Heat capacity (N/mm2 °C)	3.42	2.5	3	15
Thermal conductivity (W/m°C)	33 (50 °C)	26 (25 °C)	20 (40 °C)	30 (30 °C)
	28 (90 °C)	27 (100 °C)	21 (100 °C)	32 (100 °C)
	19 (300 °C)	28 (300 °C)	22 (300 °C)	34 (300 °C)
	13 (500 °C)	30.5 (500 °C)	23.5 (500 °C)	37 (500 °C)
	7 (1000 °C)	33.5(1000 °C)	26 (1000 °C)	44 (1000 °C)
	7 (1300 °C)	35 (1300 °C)	27 (1300 °C)	47.5 (1300 °C)

shows the thermomechanical properties of tool coating materials.

2.4. Thermal Boundary Conditions

The simulation model in AdvantEdge is designed to incorporate thermomechanical coupling through a staggered computational approach, ensuring an accurate representation of the heat transfer and mechanical behaviour. The framework employs geometrically identical meshes for both thermal and mechanical domains, facilitating synchronized calculations. In this staggered process, the mechanical step is performed first, assuming a constant temperature distribution, while the thermal step follows, considering continuous heat generation. During the mechanical phase, stress–strain responses are evaluated based on the prevailing temperature conditions. The heat generated due to plastic deformation and friction is then computed and applied to the thermal mesh. The forward-Euler algorithm is used to update the temperature field, ensuring precise thermal predictions. These updated temperature values are then transferred back to the mechanical mesh, where they influence material properties through thermal softening. This iterative process continues throughout the simulation, enhancing the accuracy and reliability of machining analysis. Thermal boundary conditions for AdvantEdge, as described by Kadirgama et al., 2014 [43], are given as follows:

The heat is generated due to heavy plastic work performed on the workpiece. It is computed using Equation (9):

$$\mathrm{R}={\displaystyle \frac{\mathrm{J}·\mathrm{f}·{\mathrm{W}}^{\mathrm{p}}}{\mathsf{\rho }}}$$

(9)

where ${\mathrm{W}}^{\mathrm{p}}$ is the rate of plastic work, f is the fraction of plastic work converted into heat, $\mathrm{J}$ is the mechanical equivalent of heat, and $\mathsf{\rho }$ is the density of the workpiece material. The heat is generated due to friction between the chip and the rake face of the tool, which is expressed as Equation (10):

$$\mathrm{q}={\mathrm{F}}_{\mathrm{f}\mathrm{r}}·{\mathrm{V}}_{\mathrm{r}}·\mathrm{J}$$

(10)

where ${\mathrm{F}}_{\mathrm{f}\mathrm{r}}$ is the friction force, ${\mathrm{V}}_{\mathrm{r}}$ is the relative sliding velocity between the tool and the chip, and $\mathrm{J}$ is the mechanical equivalent of heat. The generated frictional heat is distributed to the chip and tool according to Equation (11):

$${\displaystyle \frac{{\mathrm{Q}}_{\mathrm{c}\mathrm{h}\mathrm{i}\mathrm{p}}}{{\mathrm{Q}}_{\mathrm{t}\mathrm{o}\mathrm{o}\mathrm{l}}}}={\displaystyle \frac{\sqrt{{\mathrm{k}}_{\mathrm{c}\mathrm{h}\mathrm{i}\mathrm{p}}\times {\mathsf{\rho }}_{\mathrm{c}\mathrm{h}\mathrm{i}\mathrm{p}}\times {\mathrm{c}}_{\mathrm{c}\mathrm{h}\mathrm{i}\mathrm{p}}}}{\sqrt{{\mathrm{k}}_{\mathrm{t}\mathrm{o}\mathrm{o}\mathrm{l}}\times {\mathsf{\rho }}_{\mathrm{t}\mathrm{o}\mathrm{o}\mathrm{l}}\times {\mathrm{c}}_{\mathrm{t}\mathrm{o}\mathrm{o}\mathrm{l}}}}}$$

(11)

where ${\mathrm{Q}}_{\mathrm{c}\mathrm{h}\mathrm{i}\mathrm{p}}$ is the heat given to the chip, ${\mathrm{Q}}_{\mathrm{t}\mathrm{o}\mathrm{o}\mathrm{l}}$ is the heat given to the tool, k is the conductivity, $\mathsf{\rho }$ is the density, and c is the heat capacity. Third Wave AdvantEdge incorporates a stepwise variation in the rate sensitivity exponent m while maintaining the continuity of stress. This leads to the following relations for low and high strain rates.

2.5. Coolant Modelling

The focused coolant model pressure function of coolant modelling within AdvantEdge was used to simulate the effects of coolant and its jet application parameters in machining processes. Minimal jet cooling was represented by adopting the appropriate cutting fluid’s heat transfer coefficient as AdvantEdge software permits only the user to change the cutting fluid’s jet velocity to modify the cooling conditions. Banerjee et al., 2014 [12] developed a friction model using AdvantEdge software, when machining AISI 1045 steel under minimum quantity lubrication with a heat transfer coefficient value of 250 W/m²·K and wet cooling with a heat transfer coefficient value of 5230 W/m²·K. Liu et al., 2020 [29] developed a simulation model of AdvantEdge FEM to predict the cutting force under a high-pressure cooling environment with a convective heat transfer coefficient value of 10,000 W/m²·K. Feng et al., 2024 [38] conducted a simulation of spray cooling in the turning of GH4169 with a heat transfer coefficient value of 2644.8 W/m²·K, and a jet velocity of 6.14 m/s The convective heat transfer coefficient typically ranges from 2500 to 5500  W/m²·K for the coolant jet velocities in the range of 50–70 m/s. In the present study, a convective heat transfer coefficient of 5320 W/m²·K was selected and the initial coolant temperature was considered to correspond to the room temperature. With the focused coolant model pressure function of coolant modelling, the effects of coolant application parameters were studied. Coolant flow was assumed to be uniform and steady after leaving the nozzle. For a specific nozzle stand-off distance, the nozzle location was determined based on the reference position and angle of impingement. The cutting fluid’s flow velocity at various pressures was determined using Equation (12) [44].

$$\mathrm{V}={\displaystyle \frac{\mathrm{Q}}{{\mathrm{S}}_{\mathrm{A}}}}={\displaystyle \frac{{\mathrm{C}}_{\mathrm{d}}·\mathrm{n}·\frac{\mathsf{\pi }{\mathrm{d}}^2}{4}·\sqrt{\frac{2\mathrm{P}}{\mathsf{\rho }}}}{\frac{\mathsf{\pi }{\mathrm{d}}^2}{4}}}$$

(12)

where V represents the fluid flow velocity, while ${\mathrm{C}}_{\mathrm{d}}$ denotes the coefficient of discharge of the nozzle, assumed to be 1. The variable n indicates the number of nozzles, d refers to the diameter of the nozzle, $\mathsf{\rho }$ signifies the density of the cutting fluid, P stands for the pressure, $\mathrm{Q}$ is the flow rate at the nozzle, and ${\mathrm{S}}_{\mathrm{A}}$ represents the area of the aperture at the nozzle.

Figure 4 shows a simulation interface designed for a thermal-fluid analysis in machining, focusing on the heat transfer and optimisation of coolant parameters. The heat transfer module supported modelling with constant or temperature-dependent properties, employing a fixed heat transfer coefficient of 5320 W/m²·K, and a coolant temperature of 20 °C to simulate ambient cooling. The “Focused Location” configuration enabled precise coolant targeting, integrating parameters like jet coordinates, nozzle positioning, nozzle angle, and jet velocity. The activation of the “Model Pressure” feature incorporated coolant pressure effects, ensuring a comprehensive representation of localized thermal interactions crucial for tool integrity and efficiency in high-speed machining. Modelling simulations were performed under identical experimental cutting conditions and accordingly, the results obtained were analysed.

2.6. Mesh Refinement

The AdvantEdge software utilises a Lagrangian method with adaptive mesh partitioning, where the coarsening factor controls the mesh expansion post-deformation, and the refinement factor governs the precision of mesh refinement. Figure 5 shows a meshing parameter configuration for a workpiece in computational simulations, focusing on the refinement and quality of the generated mesh The maximum and minimum element sizes were set to 0.1 mm and 0.02 mm, respectively, with a mesh grading of 0.4 to control elements’ size transitions. A curvature-safety factor of 1.5 ensured the mesh captured geometric curvature accurately, while segments per edge were set to 0.5 for edge discretization.

Figure 6 presents a mesh parameter for the cutting tool, with the maximum and minimum element sizes defined as 0.05 mm and 0.01 mm, respectively. Mesh grading, curvature safety, and segments per edge were set to 0.4, 1.5, and 6, respectively. The minimum edge length was specified as 0.24 mm. Larger elements were assigned to simple areas, while finer elements were applied to detailed regions, ensuring accuracy and efficiency.

2.7. Experimental Method

2.7.1. Test Specimen and Its Chemical Composition

AISI 52100, a high carbon, chrome-based low-alloy steel, is widely used in engineering applications including the manufacture of aircraft bearings, ball and roller bearings, spinning tools, CV joints, ball screws, gauges, punches, dies, etc. AISI 52100 steel was chosen as the work material due to its broad applicability in automotive and allied industries. The hardness of workpieces in the received condition was 20 HRC. The chemical composition of the AISI 52100 workpiece was tested using BRUKER’S optical emission spectrometer (Model—Q4 TASMAN) and compared with the composition available in the AdvantEdge material library. The chemical composition and hardness in both cases were nearly identical. Given this similarity, the standard material from the AdvantEdge library was selected with its default material properties, ensuring accurate and validated material behaviour for the simulation. The standard library material yielded comparatively better results in terms of accuracy and correlation with experimental and published data. The chemical composition for the workpiece through OES, is presented in

Table 3. Chemical composition of the AISI 52100 steel specimen in percentage by weight (OES).

C %	Si %	Mn %	P %	S %	Cr %	Ni %	Cu %	Fe %
0.98	0.277	0.391	0.026	0.022	1.410	0.060	0.058	Balance

, while

Table 4. Composition of the AISI 52100 steel specimen (AdvantEdge’s material library).

C %	Si %	Mn %	P %	S %	Cr %	Ni %	Cu %	Fe %
0.98	0.230	0.350	0.025	0.025	1.450	-	-	Balance

provides the composition from AdvantEdge’s material library.

2.7.2. Heat Treatment of Workpiece Material

For the heat treatment of the workpiece, the bars were heated to the proper austenizing temperature of 920 °C, held at that temperature for 30 min, and then quenched in oil. After quenching, tempering was carried out, i.e., the material was reheated to a predetermined temperature at about 400 °C below the lower critical temperature range for 2 h, followed by air cooling to remove residual stresses and obtain a homogeneous structure. This enabled the FEM analysis to justify the workpiece’s stress-free initial state assumption. The hardness of the work material was found to be 58 ± 1 HRC after heat treatment.

2.7.3. Cutting Insert and Tool Holder

Coated carbide tools offer an economical alternative to CBN/PCBN and ceramic tools. Hard turning tests were conducted using HK 150 grade multilayer coated carbide inserts mounted on a PCLNR2020 K12 type right-hand tool holder. Figure 7 shows the geometry of the cutting inserts and the coating layer with a carbide substrate.

These geometrical parameters and angles of the tool were incorporated into the FEM tool modelling to ensure consistency and accuracy in the simulations.

2.7.4. Measurement of Cutting Force

In this experimental study, the cutting forces in the axial, cutting, and radial directions were measured using a strain gauge-based three-component lathe tool dynamometer (IEICOS Model 620C) specifically designed for 25 mm tool bits. The dynamometer featured a calibration range of 500 kg-force across the Fx, Fy, and Fz directions, utilising a 4-arm bonded strain gauge sensor for each force component. The system provided a linearity and accuracy of 1% of the full scale, ensuring precise force measurements during the machining process.

2.7.5. Cutting Fluid and Cutting Fluid Application Techniques

An eco-friendly semisynthetic cutting fluid, SUN Cut ECO-33, was used with the MCFA technique, delivering a small amount of cutting fluid in pulses at very high velocity. The cutting fluid was free from hazardous chemicals; it ensured safety while providing excellent cooling and lubricity, improving heat dissipation, and reducing friction. With a pH of 9–9.2, a viscosity of 48 mm²/s at 30 °C, and a concentration range of 3–10%, it offered effective cooling and lubrication for sustainable machining.

2.7.6. Measurement of Cutting Temperature

Measuring cutting temperature presents significant challenges in metal-cutting operations. The temperature at the tool–chip interface during metal cutting is complex, making it challenging to establish a measurement setup. Therefore, the accurate measurement of temperature remains a significant challenge. In this study, an infrared thermometer and embedded thermocouple technique was used to measure the cutting temperature (cutting edge temperature) as explained by Hoyne et al., 2015 [44] in their research work.

Figure 8 shows the infrared thermometer. Figure 9 presents the cutting tool with embedded thermocouple. The HTC IRX-68 infrared thermometer measures temperature from −50 °C to 1850 °C (IR) and −50 °C to 1370 °C (Type K), with accuracies of ±1.0% (IR) and ±1.5% (Type K). The primary specifications included a response time of less than 150 ms, an optical resolution of 50:1, and an adjustable emissivity ranging from 0.10 to 1.0. The emissivity values were determined through a literature review and experiments. The tool exhibited an emissivity range of 0.2–0.4, the chip 0.5–0.8 due to oxidation, and the workpiece 0.4–0.7, varying with temperature and surface conditions. Infrared and thermocouple measurements confirmed 0.4 as the accurate emissivity value for the tool and 0.6 for the workpiece. The K Type thermocouple probe connected to the device ensured precise and accurate temperature monitoring for the research. The thermocouples were positioned in holes at less than 0.3 mm from the cutting edge of the insert. The holes were made by electric discharge machining (EDM) normal to the tool flank face, and the thermocouple could be positioned as close to 0.15 mm from the cutting edge. To prevent damage, thermocouples were inserted into blind holes machined by EDM. To control the position of the thermocouple with respect to the cutting edge of the insert, the blind holes’ dimensions were selected to maintain the insert’s strength after EDM, as shown in Figure 10.

The copper guard was fixed over the thermocouple with metal adhesive to protect the thermocouple from forming chips during machining. The dimensions a, b, c, and d indicate the thermocouple’s hole positioning within the insert, ensuring precise temperature monitoring while maintaining tool integrity. “a” denotes the horizontal distance from the cutting edge along the rake face, optimising temperature sensitivity and thermocouple safety. “b” represents a fixed hole depth oriented perpendicular to the rake surface, influencing thermocouple stability and response time. “c” represents the distance from the hole centre to the principal flank face, facilitating positioning modifications without compromising the tool’s integrity. “d” is the minimal residual material thickness between the hole and the principal flank face, maintained at 0.15 mm to ensure structural integrity and measurement precision. Overall, these characteristics enhance thermocouple positioning for reliable thermal monitoring.

Table 5. Blind hole dimensions on the inserts (mm).

a	b	c	d
0.15	1.25	0.47	0.15
0.25	1.25	0.49
0.35	1.25	0.51
0.45	1.25	0.53

shows the blind hole dimensions on the inserts.

2.8. Machining Conditions

The machining conditions used in the experimental tests were the same as the machining inputs entered before the simulation. The ranges and levels of fluid application parameters were selected based on the experimental work performed and previous studies [45].

Table 6. Machining conditions for modelling and simulating the process.

Conditions	Descriptions
Workpiece	: AISI 52100 (58 HRC) and AISI 4140 (58HRC)
Cutting speed (v) and their levels	: 80, 110, and 140 m/min
Feed (f) and their levels	: 0.05, 0.10, and 0.15 mm/rev
Depth of cut (d) and their levels	: 0.1, 0.3, and 0.5 mm
Cutting fluid application parameters	: Jet velocity—50 m/s, 60 m/s and 70 m/s : Jet angle—20°, 30°, and 40° : Nozzle stand-off distance—20, 30, and 40 mm
Design of experiment	: Taguchi L27 orthogonal array
Cutting environment	: Minimal cutting fluid application
Cutting tool	: Multilayer coated carbide insert (TiN/TiCN/Al2O3)
Coating layer thickness	: Al2O3: 5 µm, TiCN: 3 µm, TiN: 2 µm
Tool geometry	: Back rake angle −6°, negative edge inclination angle −6°, clearance angle 5°, approach angle 95°, and nose radius 0.8 mm.
Convective heat transfer coefficient	: 5320 W/m2·K [38].

illustrates the machining conditions employed for modelling and simulating the process.

Figure 11 illustrates the process parameters for the hard-turning process. It highlights key variables such as feed rate, depth of cut, length of cut, cutting speed, and initial temperature (20 °C). Numerical simulations were conducted utilising Taguchi’s L27 orthogonal array, which served as the experimental design for a 2D finite element simulation of the hard turning process in a minimal cutting fluid environment. The study emphasised the influence of cutting and cutting fluid application parameters on machining responses.

Table 7. Design of experiments for the 2D finite element simulation.

Run No.	v (m/min)	f (mm/rev)	d (mm)	Jet Angle (JA) (°)	Jet Velocity (JV) (m/s)	Nozzle Stand-off Distance (NSD) (mm)
1	80	0.05	0.15	20	50	20
2	80	0.05	0.15	20	60	30
3	80	0.05	0.15	20	70	40
4	80	0.10	0.30	30	50	20
5	80	0.10	0.30	30	60	30
6	80	0.10	0.30	30	70	40
7	80	0.15	0.45	40	50	20
8	80	0.15	0.45	40	60	30
9	80	0.15	0.45	40	70	40
10	110	0.05	0.30	40	50	40
11	110	0.05	0.30	40	60	20
12	110	0.05	0.30	40	70	30
13	110	0.10	0.45	20	50	40
14	110	0.10	0.45	20	60	20
15	110	0.10	0.45	20	70	30
16	110	0.15	0.15	30	50	40
17	110	0.15	0.15	30	60	20
18	110	0.15	0.15	30	70	30
19	140	0.05	0.45	30	50	30
20	140	0.05	0.45	30	60	40
21	140	0.05	0.45	30	70	20
22	140	0.10	0.15	40	50	30
23	140	0.10	0.15	40	60	40
24	140	0.10	0.15	40	70	20
25	140	0.15	0.30	20	50	30
26	140	0.15	0.30	20	60	40
27	140	0.15	0.30	20	70	20

outlined the design of experiments for the 2D finite element simulation, with 27 experimental runs conducted. Cutting speeds of 80, 110, and 140 m/min, feed rates of 0.05, 0.10, and 0.15 mm/rev, and depths of cut of 0.15, 0.30, and 0.45 mm were selected. Jet angles ranging from 20° to 40°, jet velocities between 50 and 70 m/s, and nozzle stand-off distances from 20 to 40 mm were used. Each parameter combination tested aimed to analyse the effects systematically.

Table 8. Results of the hard turning of AISI 52100 steel from the 2D FEA model.

Run. No	Fa (N)	Fc (N)	Cutting-Edge Temp. (°C)	Machined-Surface Temp. (°C)	Tool–Chip Contact Length (mm)	S/N Ratio (dB)
1	58.913	101.192	521	281	0.15411	−50.85
2	71.525	119.162	536	293	0.17124	−51.17
3	97.983	146.312	565	304	0.19110	−51.73
4	62.499	134.033	525	307	0.16992	−51.33
5	55.395	158.745	553	319	0.19160	−51.83
6	75.231	208.569	578	333	0.21440	−52.29
7	87.439	203.531	551	362	0.18582	−52.28
8	71.474	228.531	581	371	0.22780	−52.74
9	91.346	253.279	598	379	0.25268	−53.14
10	57.992	158.523	548	300	0.11258	−51.58
11	65.412	176.226	581	311	0.13420	−52.07
12	112.192	207.609	612	318	0.15524	−52.57
13	71.761	184.270	584	339	0.13280	−52.39
14	83.545	206.804	606	346	0.16760	−52.81
15	108.891	224.507	638	353	0.19524	−53.21
16	62.571	142.238	521	382	0.14989	−51.35
17	97.679	178.64	543	394	0.16897	−51.95
18	104.675	218.07	591	405	0.20897	−52.70
19	69.117	157.718	601	331	0.08190	−52.32
20	117.143	195.539	626	338	0.10156	−52.80
21	105.564	216.46	641	343	0.12156	−53.11
22	65.487	107.828	621	354	0.09192	−52.48
23	76.982	134.382	643	363	0.11584	−52.89
24	100.133	163.351	664	372	0.13998	−53.31
25	87.426	131.968	634	412	0.10321	−52.99
26	101.264	169.789	654	427	0.12016	−53.36
27	113.045	188.296	682	439	0.15747	−53.76

presents simulation results from a 2D FEA model of hard-turning AISI 52100 steel. It shows how cutting forces, temperatures, tool–chip contact length, and S/N ratios varied under different conditions. The axial force (Fa) ranged from 55.395 N to 117.143 N while the cutting force (Fc) varied between 101.192 N and 253.279 N. The cutting-edge temperature rose from 521 °C to 682 °C, and the machined-surface temperature increased from 281 °C to 439 °C. The tool–chip contact length expanded from 0.0819 mm to 0.25268 mm, indicating a greater interaction and load on the tool. The S/N ratio, which measures process stability, ranges from −50.85 to −53.76 dB, with more negative values indicating higher variability. These results highlight the significant impact of machining parameters on mechanical and thermal loads, essential for optimising tool life, surface integrity, and machining efficiency across the various runs.

3. Result and Discussion

Figure 12a presents the temperature contour plot near the cutting edge for the following machining conditions: cutting speed (v) = 110 m/min, feed rate (f) = 0.15 mm/rev, depth of cut (d) = 0.15 mm, jet angle (JA) = 30°, jet velocity (JV) = 70 m/s, and nozzle stand-off distance (NSD) = 30 mm. The result indicated that the highest temperature, approximately 591 °C, occurred at the tool–chip interface and near the cutting edge. This is attributed to the higher velocity causing greater turbulence and reducing the dwell time of the coolant in the cutting zone, thereby limiting its ability to dissipate heat.

Additionally, the larger NSD caused the jet to disperse before effectively interacting with the cutting region, further reducing its cooling potential. It was observed that the cooling efficiency of a jet did not consistently improve with an increase in jet velocity; rather, achieving an optimum jet velocity was essential for effectively reducing cutting temperatures. Figure 12b presents the temperature contour plot near the cutting edge for the following machining conditions: cutting speed (v) = 110 m/min, feed rate (f) = 0.15 mm/rev, depth of cut (d) = 0.15 mm, jet angle (JA) = 30°, jet velocity (JV) = 60 m/s, and nozzle stand-off distance (NSD) = 20 mm. A jet velocity of 60 m/s and a shorter NSD of 20 mm reduced the cutting temperature to 543 °C. The moderate velocity allowed the coolant to impinge more effectively and remain in the cutting zone longer, facilitating improved heat transfer. Moreover, the reduced NSD enabled a more direct jet impact on the cutting zone, hence minimising heat dispersion.

Figure 12c shows the temperature contour plot near the cutting edge at v = 110 m/min, f = 0.15 mm/rev, d = 0.15 mm, JA = 30°, JV = 50 m/s, and NSD = 40 mm. The peak temperature attained was around 521.67 °C. This configuration demonstrated moderate temperature management as compared to situations with elevated jet velocities and decreased stand-off distances. The cutting parameters influenced heat generation due to friction and material deformation. The optimal jet velocity (JV) of 50 m/s significantly decreased the cutting temperature, even at an increased stand-off distance (NSD) of 40 mm. This underscores the need for choosing the optimal jet velocity to enhance cooling efficiency while mitigating thermal impacts. The results indicated that an extremely high jet velocity diminished cooling efficiency due to turbulence and dispersion, but an excessively low velocity was inadequate for effective cooling. The findings emphasised the necessity of adopting suitable machining and cooling settings to reduce the thermal impacts. Reducing the temperature at the cutting edge is essential for minimising tool wear, ensuring dimensional accuracy, and enhancing surface finish. The findings further confirmed the significance of elevated jet velocity and nozzle positioning in enhancing cooling efficiency, consequently making the process more thermally stable.

Figure 13a illustrates the cutting-edge temperature when v = 140 m/min, f = 0.10 mm/rev, d = 0.15 mm, JA = 40°, JV = 50 m/s, and NSD = 30 mm. Even with a moderate stand-off distance (NSD) of 30 mm, the cutting temperature was significantly reduced, reaching a maximum value of 621.53 °C. The nozzle angle (JA) of 40° exhibited negligible impact on temperature reduction, highlighting the significant relevance of jet velocity and stand-off distance in regulating thermal effects during machining. Figure 13b shows a reduced cutting temperature, peaking at 643.63 °C, attributed to the moderate cutting fluid’s jet velocity (JV = 60 m/s) despite the higher stand-off distance (NSD = 40 mm). The nozzle angle (JA = 40°) appeared to have a very minor influence on the cutting temperature, highlighting the critical role of jet velocity in thermal management. Figure 13c shows the cutting-edge temperature at v = 140 m/min, f = 0.10 mm/rev, d = 0.15 mm, JA = 40°, JV = 70 m/s, and NSD = 20 mm. The maximum temperature reached 664 °C near the cutting edge due to high cutting speed and large depth of cut, which increased heat generation. The elevated heat generation during the machining process was significantly influenced by the higher cutting speed (v = 140 m/min) and increased depth of cut (d = 0.15 mm). The smaller stand-off distance (NSD = 20 mm) concentrated the cooling jet, limiting heat dissipation and causing localized heat build-up near the cutting zone. Despite the cooling jet’s velocity of 70 m/s, its effectiveness in reducing the peak temperature was found to be limited under these aggressive machining parameters.

Figure 14 illustrates the cutting forces Fx and Fy under machining conditions of v = 140 m/min, f = 0.10 mm/rev, d = 0.15 mm, JA = 40°, JV = 70 m/s, and NSD = 20 mm. The tangential force Fy reached a peak value of approximately 160 N, while the axial force Fx stabilized around 100 N. The increased cutting speed and feed rate led to higher cutting forces compared to lower cutting speeds. However, the higher jet velocity (JV = 70 m/s) and reduced stand-off distance (NSD = 20 mm) caused turbulence, reducing fluid penetration and decreasing its cooling and lubrication efficiency, leading to higher cutting forces.

Figure 15 depicts the cutting forces Fx and Fy under the cutting conditions of v = 140 m/min, f = 0.15 mm/rev, d = 0.30 mm, JA = 20°, JV = 50 m/s, and NSD = 30 mm. The force Fy, representing the tangential cutting force, was observed to reach a peak of approximately 131 N, while the axial force Fx stabilized at around 87 N. Despite the increased feed rate and depth of cut, which typically elevate cutting forces due to higher material removal resistance, a decrease in force was observed. The cooling jet velocity (JV) = 50 m/s and small stand-off distance (NSD) = 20 mm contributed to efficient cooling, thereby maintaining cutting forces within optimal ranges.

Figure 16 shows cutting forces Fx = 101 N and Fy = 169 N at v = 140 m/min, f = 0.15 mm/rev, d = 0.30 mm, JA = 20°, JV = 60 m/s, and NSD = 40 mm. Compared to the previous case, under the same cutting conditions and (JV = 50 m/s and NSD = 30 mm) where Fx = 87 N and Fy = 131 N, the cutting forces increased. It was observed that higher jet velocities did not always result in lower cutting forces. Excessively high jet velocities and a larger stand-off distance caused turbulence, which reduced the ability of the cutting fluid to penetrate the cutting zone and dissipate heat efficiently. This decrease in cooling and lubrication effectiveness led to an increase in friction and cutting forces.

The impact of the cutting and cutting fluid application parameters on cutting force, cutting temperature, machined-surface temperature, and tool–chip contact length was examined using an ANOVA.

Table 9. ANOVA for the cutting force (Fc).

Source	DF	Seq. SS	Contr.	Adj. MS	F-Value	p-Value
v	2	3035.9	7.43%	1517.93	27.33	0.000
f	2	3490.0	8.54%	1744.99	31.42	0.000
d	2	17,630.8	43.13%	8815.41	158.71	0.000
JA	2	1682.0	4.11%	840.980	15.14	0.000
JV	2	14,179.3	34.68%	7089.65	127.64	0.000
NSD	2	85.3	0.21%	42.6600	0.770	0.483
Error	14	777.6	1.90%	55.5500
Total	26	40,880.9	100.00%

shows the ANOVA for the cutting force (Fc). The ANOVA for the cutting force (Fc) indicated that the depth of cut served as the most influential factor, contributing 43.13% to Fc, with a significant F-value of 158.71 and a p-value of 0.000. Jet velocity followed with a contribution of 34.68% (F-value: 127.64, p-value: 0.000), while the feed and cutting speed contributed 8.54% and 7.43%, respectively, both being statistically significant (p-values: 0.000). The jet angle had a smaller impact at 4.11%, and the nozzle stand-off distance contributed only 0.21%, deemed statistically insignificant (p-value: 0.483). The error accounted for 1.90% of the variation, ensuring the model’s reliability. Overall, the depth of cut and jet velocity emerged as the primary factors influencing cutting force, providing key insights for process optimisation.

An ANOVA for the cutting temperature is presented in

Table 10. ANOVA for the cutting temperature.

Source	DF	Seq. SS	Contr.	Adj. MS	F-Value	p-Value
v	2	33,864.2	61.36%	16,932.1	555.87	0.000
f	2	1908.2	3.46%	954.1	31.32	0.000
d	2	2881.6	5.22%	1440.8	47.30	0.000
JA	2	3926.0	7.11%	1963.0	64.44	0.000
JV	2	11,977.6	21.70%	5988.8	196.61	0.000
NSD	2	204.7	0.37%	102.3	3.36	0.064
Error	14	426.4	0.77%	30.5
Total	26	55,188.7	100.00%

. The ANOVA for the cutting temperature indicated that cutting speed exerted the highest influence, contributing 61.36% with a significant F-value of 555.87 and a p-value of 0.000, followed by jet velocity contributing 21.70% (F-value: 196.61, p-value: 0.000). Minor contributions were made by feed rate (3.46%), jet angle (7.11%), and depth of cut (5.22%), all found to be statistically significant with p-values of 0.000. The nozzle stand-off distance was found to have a negligible impact, contributing only 0.17% with a p-value of 0.254, indicating insignificance. The error accounted for just 0.77%, reflecting the model’s reliability. Overall, cutting speed and jet velocity were identified as the dominant factors affecting cutting temperature, critical for optimising thermal effects during machining.

An ANOVA for the machined-surface temperature is presented in

Table 11. ANOVA for the machined-surface temperature.

Source	DF	Seq. SS	Contr.	Adj. MS	F-Value	p-Value
v	2	16,947.2	39.48%	8473.59	359.16	0.000
f	2	18,149.9	42.29%	9074.93	384.65	0.000
d	2	2015.6	4.70%	1007.81	42.72	0.000
JA	2	2178.7	5.08%	1089.37	46.17	0.000
JV	2	3229.0	7.52%	1614.48	68.43	0.000
NSD	2	71.4	0.17%	35.70	1.51	0.254
Error	14	330.3	0.77%	23.59
Total	26	42,922.1	100.00%

. The ANOVA for the machined-surface temperature indicated that feed exerted the highest influence, contributing 42.29% with a significant F-value of 384.65 and a p-value of 0.000, followed by cutting speed at 39.48% (F-value: 359.16, p-value: 0.000). Jet velocity contributed 7.52%, jet angle 5.08%, and depth of cut 4.70%, all of which were statistically significant with p-values of 0.000. The nozzle stand-off distance was found to have a negligible impact, contributing only 0.17% with a p-value of 0.254, indicating insignificance. The error accounted for just 0.77%, reflecting the model’s reliability. Overall, feed and cutting speed were identified as the dominant factors affecting surface temperature, critical for optimising thermal effects during machining.

Table 12. ANOVA for the tool–chip contact length.

Source	DF	Seq. SS	Contr.	Adj. MS	F-Value	p-Value
v	2	0.029271	59.73%	0.014635	362.91	0.000
f	2	0.006893	14.07%	0.003447	85.46	0.000
d	2	0.000682	1.39%	0.341	8.46	0.004
JA	2	0.000031	0.06%	0.000016	0.39	0.687
JV	2	0.011484	23.43%	0.005742	142.38	0.000
NSD	2	0.000079	0.16%	0.000040	0.98	0.400
Error	14	0.000565	1.15%	0.000040
Total	26	0.049005	100.00%

shows the ANOVA for the tool–chip contact length, The ANOVA for the tool–chip contact length indicated that cutting speed represented the most significant factor, contributing 59.73% with an F-value of 362.91 and a p-value of 0.000. Jet velocity followed with a contribution of 23.43% (F-value: 142.38, p-value: 0.000), while feed accounted for 14.07% (F-value: 85.46, p-Value: 0.000). Minor contributions were observed from the depth of cut (1.39%, p-value: 0.004), while the jet angle (0.06%) and nozzle stand-off distance (0.16%) were deemed statistically insignificant (p-values of 0.687 and 0.400, respectively). The error accounted for only 1.15%, confirming the model’s reliability. Overall, cutting speed and jet velocity emerged as the dominant factors influencing the tool–chip contact length.

The responses for cutting and cutting fluid application parameters were established based on the observed combined S/N ratios, as indicated in

Table 13. Signal to Noise ratios (Smaller is better).

Level	Cutting Speed (m/min)	Feed (mm/rev)	Depth of Cut (mm)	Jet Angle (°)	Jet Velocity (m/s)	Nozzle Stand-off Distance (mm)
1	−51.94	−52.03	−52.05	−52.48	−96.00	−52.39
2	−52.30	−52.51	−52.43	−52.19	−52.41	−52.45
3	−53.01	−52.70	−52.76	−52.57	−52.87	−52.40
Delta	1.07	0.67	0.71	0.37	0.91	0.06
Rank	1	4	3	5	2	6

. The signal-to-noise (S/N) ratios indicated that smaller values were preferred for optimal performance. For cutting speed, the lowest S/N ratio of −53.01 dB at level 3 made it the most influential factor, followed by jet velocity at −52.87 dB. Feed and depth of cut showed relatively similar S/N ratios at −52.70 dB and −52.76 dB, respectively, with the depth of cut ranked slightly higher. The jet angle had a smaller impact, ranked fifth with an S/N ratio of −52.57 dB, while nozzle stand-off distance showed the least variation with an S/N ratio of −52.40 dB. The ranking indicated that cutting speed and jet velocity were the most significant factors affecting the process’s stability.

The main effects plot of the S/N ratio was generated using the responses, as given in Table 13. The maximum value of each parameter in the S/N plot indicated the optimal level of parameters, which contributed more to the reduction in cutting force, cutting temperature, machined surface temperature, and tool–chip contact length, as shown in Figure 17. The cutting speed of 80 m/min, feed rate of 0.05 mm/rev, depth of cut of d = 0.15 mm, jet angle of 30⁰, jet velocity of 50 m/s, and nozzle stand-off distance of 20 mm were the optimal parameters.

The cutting fluid’s jet parameters coupled with the cutting parameters have a significant effect on machining responses. The interaction effect of cutting and cutting fluid’s jet application parameters was studied through contour plots. Figure 18a shows the interaction effect of the depth of cut and jet velocity on the cutting force. From this graph, a lower cutting force value of 117–136 N was reached between a depth of cut of 0.15–0.25 mm and a jet velocity of 50–55 m/s. The maximum cutting force of 230–250 N was observed at a depth of cut of 0.40–0.45 mm/rev and jet velocity of 60–70 m/s. Figure 18b shows the influence of cutting speed and jet velocity on cutting temperature. The cutting temperature value of 521–561 °C was reached between a cutting speed of 80–115 m/min and a jet velocity of 50–60 m/s. The cutting temperature value of 641–682 °C was reached between a cutting speed of 130–140 m/min and a jet velocity of 60–70 m/s.

Figure 19a presents the effect of the interaction between the cutting speed and feed rate on the machined-surface temperature. It was observed that the machined-surface temperature increased with an increase in feed rate and cutting speed. A minimal machined-surface temperature value of 281–320 °C was found between a cutting speed of 80–120 m/min and a feed rate of 0.05–0.10 mm/rev. A machined-surface temperature of 399–419 °C was observed at a cutting speed of 130–140 m/min and feed rate of 0.13–0.15 mm/rev. Figure 19b reveals the tool–chip contact length against the cutting speed and feed rate. It was found that the tool–chip contact length increased with the feed rate and decreased with cutting speed. A minimum tool–chip contact length of 0.10–0.12 mm was reached between a cutting speed of 130–140 m/min and a feed rate of 0.05–0.10 mm/rev. A higher tool–chip contact length value of 0.20–0.22 mm was observed at a cutting speed of 80–85 m/min and a feed rate of 0.13–0.15 mm/rev.

Figure 20a shows the interaction effect of the jet angle and jet velocity on the cutting force (Fc). The jet angle exhibited a minimal impact on the cutting force, while a reduced cutting force of 117–155 N was recorded at 50–52 m/s and a jet angle of 20°–30°. The higher cutting force value of 231 N was found at a jet angle of 30°–40° and a jet velocity of 65–70 m/s. Figure 20b shows the influence of the jet angle and jet velocity on cutting temperature. The lower cutting temperature value of 541 °C was reached for a jet angle of 28°–32° and jet velocity of 50–55 m/s.

4. Model Validation

The experimental runs were performed with the same set of parameters as those used in the simulations. The deviation between the experimental and simulation values were observed in the range of 7% to 10% for cutting force, cutting temperature and machined surface temperature. The simulated tool–chip contact length values were found to be lower, ranging from 0.08 to 0.25 mm, in comparison to the literature values of 0.15 to 0.45 mm. This discrepancy can be attributed to the combined influences of the optimal values of jet velocity, jet angle, and stand-off distance. The simulated machined-surface temperature values were validated by the experimental results presented by Chen et al., 2017 [46], indicating close agreement and validating the accuracy of the simulation methods.

Table 14. Model validation through a confirmatory test.

Run No.	Experimental				Simulated
	Fa (N)	Fc (N)	Cutting-Edge Temp. (°C)	Machined-Surface Temp. (°C)	Fa (N)	Fc (N)	Cutting-Edge Temp. (°C)	Machined-Surface Temp. (°C)
01	66.277	110.7	484	295	58.91	101.192	521	281
18	116.71	198.4	614	376	104.6	218.070	591	405
21	99.230	233.7	579	312	105.5	216.460	641	343
24	112.48	185.4	637	407	100.1	163.351	664	372

shows the confirmatory test for the model validation.

5. Conclusions

The modelling and simulation of the hard turning of AISI 52100 alloy steel at an elevated hardness of 58 HRC were carried out using the finite element-based commercial software package AdvantEdge. The main effects plot for S/N ratios identified the optimal combination of parameters for the minimisation of the cutting force, cutting temperature, machined-surface temperature, and tool–chip contact length. The optimal parameters were observed to be a 80 m/min cutting speed, a 0.05 mm/rev feed rate, and a 0.15 mm depth of cut, a 30° jet angle, a 50 m/s jet velocity, and a 20 mm nozzle stand-off distance, which resulted in improved performance. The significant influence of various parameters was determined by employing ANOVAs. The depth of cut was identified as the most critical cutting factor impacting the cutting force, with jet velocity being the next most influential cutting parameter. Cutting temperature and tool–chip contact length were found to be mainly influenced by cutting speed and jet velocity, whereas the feed rate, depth of cut, and jet angle had a less considerable effect on cutting temperature. The machined-surface temperature was greatly affected by the cutting speed and then by feed rate, while the depth of cut, jet angle, and jet velocity showed less significant influence. The nozzle stand-off distance did not show any effect on the machined-surface temperature. For the cutting force, cutting temperature, and machined-surface temperature, the observed difference between experimental and simulated values ranged from 7% to 10%. The analysis showed a strong correlation between the experimental and computational results, suggesting that the proposed models can accurately predict various machining responses under similar conditions.

Future research should focus on the experimental validation of FEM results, exploring advanced and hybrid cooling techniques such as high-pressure cooling, vortex tube cooling, cryogenic cooling, MQL with nanofluids, and their combinations to further enhance thermal control and improve machining performance. AI-driven optimisation, coupled with a microstructural analysis, will provide deeper insights into surface integrity by improving the micro-hardness distribution and minimising undesirable residual stresses. These advancements will contribute to sustainable and efficient machining practices.