The material model is a crucial aspect of 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. It comprises a strain hardening function, denoted as g ε p , and a thermal softening function, denoted as θ(T). The strain rate sensitivity function is represented by τ ε ˙ . Figure 2 illustrates the material constitutive model based on the Power Law and Coulomb friction model. Material constitutive model describes the relationship between stress and strain in the plastic deformation region. This model accounts for strain hardening behavior, where the flow stress is a function of strain, strain rate, and temperature, making it essential for accurately simulating material behavior in machining processes.

The material constitutive model governed by the Power Law is illustrated in Figure 1. During metal cutting processes, 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 hardening function g ε p , the strain rate sensitivity model used for computing flow stress and thermal softening function Θ T and the thermal softening function are all presented in the flowchart in Figure 2. 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 (μ), 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 frictional behavior at the tool–chip interface, governed by Coulomb’s model, is illustrated in Figure 2.

Thermo-mechanical properties substantially affect the parameters of constitutive models in AdvantEdge machining simulation. The basic option offers a predefined material library, whereas the custom option enables users to select specific properties. AdvantEdge automatically integrates these properties into its constitutive models, which govern strain hardening, strain rate sensitivity, and thermal softening. Table 2 delineates the thermo-mechanical properties of tool workpiece materials.

The WC-Co sintered carbide tool coated with three layers (TiN/TiCN/Al₂O₃) coating was modeled. Tool coating within AdvantEdge was done 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 to leverage the distinct benefits of each coating material based on previous studies, simulation trials, and experimental measurements of cutting temperature. The 2 µm TiN layer, positioned as the innermost coating, provides robust adhesion to the carbide substrate while ensuring excellent wear resistance. The 3 µm TiCN intermediate layer enhances the tool’s hardness and fracture toughness, serving as a transitional phase to absorb mechanical stresses between the ductile inner TiN and the outer Al₂O₃ layer. The 5 µm Al₂O₃ layer, as the outermost coating, acts as a strong thermal insulator, preventing oxidation and maintaining tool integrity under high temperature cutting conditions. This combination of coatings ensures optimal performance and durability for hard turning applications. The tetrahedral-type mesh was used for the meshing process. A total of 24,000 nodes were applied for mesh generation, with a mesh grade of 0.4 and element sizes of 0.05 mm and 0.01 mm respectively.

For accurate predictions, temperature-dependent thermal conductivity values were used. The thermal conductivity of the multi-layer coated carbide tool exhibits considerable variation as a result of its layered structure. The coating materials, including TiN, TiCN, and Al₂O₃, demonstrate low thermal conductivity (7–33 W/m·K), functioning as thermal barriers that diminish heat dissipation into the tool. The tungsten carbide substrate exhibits notably higher thermal conductivity (30–47.50 W/m·K), facilitating effective heat dissipation from the cutting zone. Interactions between the tool and workpiece, as well as the tool and chip, were modelled with friction coefficients ranging from μ = 0.5 to 0.7 for hard turning. The heat partitioning ratios were established as follows: 80%–85% of heat was directed into the chip, 10%–15% into the workpiece, and 3%–7% into the tool. The thermal properties were incorporated according to established literature to ensure precise FEM-based thermal modelling and realistic temperature distribution in the simulation. Table 3 presents mechanical and thermal properties of coated and uncoated materials.

Figure 3 illustrates the turning simulation interface, allowing users to define workpiece dimensions and simulate machining process. Initial stress conditions can be adjusted through external file input for customized simulation configurations. A stress-free initial state assumption was adopted to ensure accurate thermal and mechanical behavior representation, with the interface offering a clear visual of the process for detailed analysis.

ThirdWave AdvantEdge employed a Lagrangian framework enhanced with adaptive mesh strategies to accurately track material deformation. Mesh behavior was controlled using two critical parameters: the coarsening factor, which regulated mesh expansion following large deformations, and the refinement factor, which ensured localized mesh precision. The meshing parameters for the workpiece were optimized to balance resolution and computational efficiency. The mesh featured a maximum element size of 0.1 mm and a minimum of 0.02 mm, with a mesh grading of 0.4 to ensure smooth size transitions. A curvature-safety factor of 1.5 was applied to capture complex geometries with fidelity, and edge discretization was refined using a setting of 0.5 segments per edge to enhance boundary accuracy. Figure 4 illustrates the meshing configuration adopted for the workpiece and cutting tool.

The meshing configuration adopted for the cutting tool was designed with optimized element resolution to balance accuracy and computational efficiency. The mesh featured a finest element size of 0.01 mm and a coarsest size of 0.05 mm. A mesh grading value of 0.4 was used to manage the transition between element sizes, while a curvature safety factor of 1.5 ensured the geometry’s curved features were accurately captured. The edge discretization parameter was set to 6 segments per edge, and the minimum edge length was defined as 0.24 mm. Coarser mesh zones were applied to geometrically simpler regions, whereas finer meshes were allocated near critical zones to improve simulation fidelity without excessive computational load.

The AdvantEdge simulation framework employs a classical Coulomb-based sliding friction model to characterize tool–chip interface behavior. This approach assumes a constant coefficient of friction (μ), where the frictional force scales linearly with the normal contact force. The total tool–chip contact length integrates both adhesive and sliding zones, enabling accurate prediction of interface stresses and heat generation during machining. This interaction model is represented in Figure 2. This study adopted a coefficient of friction (COF) value of 0.6, consistent with established tribological behavior and thermomechanical conditions. This choice aligns with validated numerical modeling techniques and ensures realistic predictions of cutting forces, cutting temperatures, and temperature distributions.

The experimental trials were conducted using an HMT NH-18 high-precision lathe, a machine well-regarded for its operational stability and consistent performance in precision turning applications.

Residual stresses generated during hard turning are caused due to mechanical and thermal influences, resulting in either compressive or tensile stress states on the surface, influenced by cutting parameters and the machining environment. Thermal effects have a completely different effect than mechanical effects. During exposure to intense heat fluxes, a significant temperature gradient causes localized plasticization of the workpiece material. As the surface returns to room temperature, residual tensile stresses remain on the surface accompanied by compressive stresses in the sub-surface.

The residual stress volume averaging feature within AdvantEdge software provides a residual stress prediction that mimics X-ray diffraction test data. Stress over an area is averaged as a function of depth into the workpiece surface. The stress distribution throughout an area is calculated by taking the average stress values at different depths into the surface of the workpiece. The measurement of residual stress was conducted in the circumferential direction (σ_hoop). The residual stress was graphed in units of stress (MPa) as a function of the distance from the surface of a machined workpiece to the center (mm). The extraction of residual stress can be performed by a single line positioned at the midpoint of the machined surface cut. The extraction was performed by taking the average of three sections (extraction at 25%, 50%, and 75%) of the machined surface. The process includes workpiece modeling, tool modeling, friction modeling, meshing, and boundary conditions. Figure 8 illustrates the lines of residual stress extraction and Thirdwave AdvantEdge model for residual stress. The model parameters were selected based on the similar work carried out by Kadirgama et al. (2012) (Kadirgama and Bakar, 2005) and according to the guidelines given in the Third Wave AdvantEdge, AdvantEdge FEM 7.1 User’s Manual (ThirdWaveSystems, 2015).

Figure 9 depicts the angles for beam deflection. Within the residual stress volumetric averaging window, a beam length and orientation were specified. Beam orientation was then calculated by considering the angle of the beam vector with respect to the Z-axis, and the angle of projection for the beam vector on the XY plane for the X-axis. Beam extraction vector origin, the diameter of the X-ray beam, the number of slices of X-ray beams, and the number of scan points per slice were specified.

Numerical investigation was carried out using FEM to examine the effect of cutting parameters on surface and subsurface residual stresses (σ_hoop) induced after dry-hard turning. The workpiece modeling, tool modeling, friction modeling, and mesh refinement were carried out as described in previous sections. The residual stress volume averaging feature within AdvantEdge software was used to predict the residual stress which mimics X-ray diffraction test data. The cutting tool and its geometry used in the simulation and experimental work are discussed in detail in a subsequent section. The tests were conducted at various cutting conditions to understand their individual and interaction effect on residual stresses. Table 6 shows residual stress values at various cutting conditions in dry hard turning.

In the applied range of cutting conditions, the residual stresses were tensile in nature at surface and compressive in nature at subsurface, however, their magnitude varied, depending on the cutting conditions and cutting environment. Figures 10a–p ) presents the graphs of residual stresses vs. distance beneath the surface under different cutting conditions in dry cutting environment. The influence of cutting parameters on residual stresses in dry turning of AISI 52100 hardened alloy steel of hardness 58 HRC were studied. Figures 10a–d shows the tensile residual stresses of (100, 125, 130, and 140 MPa) at the surface and compressive residual stresses of (−145, −180, −200, and −260 MPa) at sub-surface at v = 80 m/min, f= (0.05, 0.10, 0.15, and 0.20 mm/rev) and d= (0,1, 0.1, 0.2, and 0.2 mm) respectively. The tensile residual stresses of 125 MPa and 130 MPa were observed at a cutting speed 80 m/min and depth of cut 0.1 and 0.2 mm, respectively. The increase of feed rate in the range of 0.05–0.20 mm/rev at applied cutting speed generated higher compressive residual stresses at subsurface in the range of −145 MPa to −260 MPa, however, the less significant change was observed in tensile residual stress at the surface. Figures 10e–h shows the tensile residual stresses of (170, 150, 160, and 180 MPa) at the surface and compressive residual stresses of (−210, −375, −390, and −445 MPa) at subsurface at v = 100 m/min, f= (0.05, 0.10, 0.15, and 0.20 mm/rev) and d= (0.3, 0.2, 0.2, and 0.3 mm) respectively. It was shown that an increase in the depth of cut from 0.2 mm to 0.3 mm caused an insignificant increase in tensile residual stresses at surface, however, a minor increase of these stresses was observed. The significant increase in value of compressive residual stress of - 210 MPa to - 445 MPa at sub-surface was noticed at a feed rate of 0.05 mm–0.20 mm/rev. An increase of cutting speed from 80 to 100 m/min caused increased tensile residual stresses in the range of 125 MPa–170 MPa.

Figures 10i–l shows the tensile residual stresses of (220, 270, 300, and 320 MPa) at the surface and compressive residual stresses of (−360, −380, −500, and −650 MPa) at sub surface at v = 120 m/min, f= (0.05, 0.10, 0.15, and 0.20 mm/rev), and d= (0,3, 0.4, 0.4, and 0.4 mm) respectively. At cutting speed 120 m/min and depth of cut 0.3 and 0.4 mm, the tensile residual stresses of 220 MPa and 270 MPa were observed respectively. At the applied cutting speed of 120 m/min, the increase in feed rate range from 0.05 to 0.020 mm/rev caused higher compressive residual stresses in the range of −360 MPa to −650 MPa at subsurface. No significant influence was observed in tensile residual stress at the surface at applied feed range. Figures 10m–p shows the tensile residual stresses residual stresses of (380, 400, 450, and 375, MPa) at the surface and compressive residual stresses of (+340, −480, −465, and −570 MPa) at sub surface at v = 140 m/min, f= (0.05, 0.10, 0.15 & 0.20 mm/rev) and d= (0,4, 0.5, 0.5, and 0.4 mm) respectively. The tensile residual stresses at surface increased drastically from 170 MPa to 450 MPa over the cutting speed range of 100 m/min to 140 m/min it was also noticed that the compressive residual stress at sub-surface significantly increases at cutting speed 140 m/min and feed rate range 0.05–0.20 mm/rev combination, on the other hand depth of cut of 0.3 mm and 0.4 mm at applied cutting speed did not show significant effect on tensile residual stress. The results closely align with the experimental findings reported in the literature, validating the accuracy and reliability of the proposed model. (Leppert and Peng, 2012) (Chavan and Sargade, 2020).

This study explored how various coating materials affect heat and temperature distribution during machining using FEM. The coatings were chosen for their thermal conductivity and heat resistance. Temperature data collected from different points in cutting zone and on the tool surface, provided insights into the thermal behavior influenced by the coatings. Table 7 illustrates the machining conditions employed for modeling and simulating the process.

The simulations were performed at cutting speed v = 140 m/min, fed rate f = 0.20 mm/rev and depth of cut d = 0.4 mm. From Figures 11a–d, the cutting temperature of 605°C with three layers of coating (Al₂O₃/TiCN/TiN-WC), 654°C with (TiCN/TiN-WC) coating, 682°C with (TiN-WC) coating and 718°C with uncoated WC cutting tool insert were observed. The significant reduction in cutting edge temperature was observed with Al₂O₃/TiCN/TiN-WC coating material as compared to other coating material; this is due to the decrease in thermal conductivity of Al₂O₃ material with increase in temperature. Al₂O₃ acts as a thermal barrier and does not allow heat to penetrate and distribute along the rake face of tool, as a result of which the carbide substrate remains partly thermally insulated by the coating. TiN coatings demonstrated a higher cutting edge temperature of 682°C, as it gets oxidize into TiO₂ above 500°C. The significant reduction in cutting temperature of 16% was observed with Al₂O₃/TiCN/TiN-WC coating, 9% with TiCN/TiN-WC coating and 5% with TiN-WC coating as compared to uncoated carbide tool. It is thus revealed from the result that cutting tool coatings influences the heat transfer and temperature distribution in the cutting zone when turning of AISI 52100 hardened steel. Table 8 depicts the cutting temperature obtained at different coating material.

Figures 12a–d shows distribution of heat source at Vc = 140 m/min, f = 0.20 mm/rev and d = 0.4 mm for coated carbide and uncoated tools, it can be concluded based on cutting simulations that the heat rate increases in the cutting zone and changes for the cutting tool coating materials used. The minimum value of e_c (volumetric specific cutting energy) equal to 11,111 W/mm³ was computed for an Al₂O₃/TiCN/TiN-WC coated carbide, whereas the maximum value of 19,444 W/mm³ was obtained for a WC uncoated carbide tool. The predicted results are in good agreement with those in the literature regarding the effect of cutting tool coating materials on the cutting edge temperature and volumetric heat generation (volume specific cutting energy) e_c in hard turning under various cutting conditions (Grzesik et al., 2005).

Finite element predictions of residual stresses closely align with experimental data reported in the literature, underscoring the reliability and accuracy of predictive model. The residual stress prediction through AdvantEdge’s residual stress volume averaging feature closely matched with the X-ray diffraction test data. Hence the approach, that was used to develop the finite element model and modeling procedure, is considered reliable for pre-process residual stress prediction using FEM. Hard turning tests were conducted using HK 150 grade MTCVD multilayer coated carbide inserts mounted on a PCLNR2020 K12 type right-hand tool holder. Table 9 presents a comparison between simulated and experimental values of cutting temperatures at different cutting conditions and tool coatings. It highlights the effectiveness of coatings in reducing heat generation and evaluates the accuracy of FEM simulations in predicting cutting temperatures. The error between simulated and experimental results ranges from 3.58% to 8.90%, indicating a reasonable agreement. Despite incorporating appropriate boundary conditions, isotropic material modeling, and thermo-physical properties, the observed discrepancy can be attributed to factors such as microstructural inhomogeneity, dynamic contact variations at the tool-chip-workpiece interface, and localized emissivity fluctuations phenomena that are inherently difficult to replicate within the constraints of current simulation techniques. Moreover, Al₂O₃/TiCN/TiN-WC coating was found to be more effective compared to other coatings, offering better thermal control.