Experimental and thermomechanical SPH and FEA model simulation using inconel 825 with tungsten carbide tool | Scientific Reports

Mar 24, 2025

Scientific Reports volume 15, Article number: 10053 (2025) Cite this article

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This study introduces an integrated experimental and finite element analysis (FEA) simulation methodology for improving the turning process of Inconel 825 using tungsten carbide (WC) cutting tools. The research presents an innovative framework that integrates infrared thermal imaging with numerical simulations to examine transient temperature profiles and cutting forces across different machining settings. This study systematically examines the effects of feed rate, cutting speed, and depth of cut on thermal and mechanical responses, employing an L9 orthogonal array for experimental design, in contrast to usual investigations. This research’s primary innovation is the exact monitoring of interface temperatures with an infrared thermal camera, yielding precise thermal data despite the difficulties posed by expensive materials and real-time heat dissipation assessment. The FEA simulations performed in Abaqus FEA utilize an elastoplastic material model exhibiting nonlinear behavior, effectively capturing yielding in both tension and compression. The results demonstrate a robust connection between experimental and numerical findings, with cutting force predictions differing by less than 5%. The research indicates that raising the cutting speed lowers cutting forces while influencing temperature patterns in a non-linear manner. The research underscores the significance of WC inserts in augmenting heat dissipation and promoting machining stability. The proven FEA framework provides a dependable prediction instrument for optimizing machining settings, hence enhancing process control and precision manufacture of high-strength alloys.

The machining of superalloys, such as Inconel poses challenges owing to their high strength, thermal stability, and corrosion resistance at elevated temperatures. Although these qualities make Inconel alloys perfect for demanding uses in aerospace, the chemical industry, and oil and gas sectors, they also cause increased tool wear, too much heat generation, and poor surface smoothness during cutting operations1,2. To mitigate these effects and enhance overall machining performance, optimizing key parameters including feed rate, cutting speed, and depth of cut becomes essential3,4. Given the hardness, wear resistance, and thermal conductivity of materials like Inconel 825, selecting appropriate cutting tools plays a crucial role in improving machinability. Pandey et al.5 investigated the feasibility of utilizing a Physical Vapor Deposition-coated Titanium Nitride/Titanium Carbonitride/Titanium Nitride cermet tool and a Chemical Vapor Deposition-coated Aluminum Oxide/Titanium Carbonitride Silicon Aluminum Oxynitride tool for dry machining of the Inconel 825 superalloy. The machining efficiency was assessed based on the amount of cutting force, tool-tip temperature, and predominant tool wear mechanisms. The results were compared with those of a Chemical Vapor Deposition multi-layer Titanium Nitride/Titanium Carbonitride/Aluminum Oxide/Titanium Nitride-coated Tungsten Carbide-Cobalt tool. The results demonstrated that the Silicon Aluminum Oxynitride tool produced reduced cutting forces while exhibiting higher tool-tip temperatures in comparison to the other two tools. Although abrasion and adhesion were prevalent wear processes, the carbide tool also demonstrated coating delamination and plowing. Conversely, the Silicon Aluminum Oxynitride tool exhibited considerable chipping and notching susceptibility. The wear severity noted on the cermet tool was significantly less than that of the carbide and Silicon Aluminum Oxynitride tools. The bottom surface morphology of the chip exhibited superior quality when cut with the cermet tool.

Modeling and simulating the machining process provide valuable insights into cutting mechanics, yet orthogonal cutting simulations remain complex due to the interplay of multiple materials and the computational challenges involved. These simulations require solving differential equations that describe the physics of the cutting process in a dynamic and evolving domain, often involving extreme strains and deformations6,7. Furthermore, an accurate constitutive model is needed to represent the workpiece material’s behavior across a wide range of temperatures. Additionally, traditional friction models, such as Coulomb’s law, often oversimplify interactions at the chip-tool interface, limiting the accuracy of numerical predictions8,9.

To address these challenges, different numerical techniques have been explored. The use of adaptive re-meshing in finite element models and alternative discretization methods, such as mesh-free and particle-based approaches, have been proposed to improve simulation accuracy10,11,12. While progress has been made in developing advanced material models, there is still room for further refinement, particularly in capturing frictional behavior during metal cutting13. Experimental validation plays a key role in refining numerical models. Parametric tests in CNC machining help assess tool performance and optimize machining conditions14. Simulations are conducted under varying conditions, such as different inflation pressures, vertical loads, and speeds, to replicate real-world machining scenarios. The Smoothed Particle Hydrodynamics (SPH) method, implemented in software packages like Pam-Crash, has been widely used to model complex interactions between tools and materials, including soil behavior in pressure-sinkage and shear-strength studies15.

The numerical simulation of cutting, despite numerous researchers’ repeated conclusions that improved models are needed to describe the friction behavior in metal cutting. Therefore, the third problem in orthogonal cutting modeling is the primary focus of this paper’s contribution16,17. The “water cushioning effect” occurs when the distance between the water and the liquid is too small, limiting the impact effect on rock fragmentation. A discontinuous water jet with a shorter water-liquid distance leads to a deeper crushing zone and increased rock volume fragmentation18. The cutting force magnitude and surface roughness were found to be lower in cryogenically treated and oil-quenched WC-Co tools compared to untreated WC-Co tools. Cryogenically treated WC-Co tools demonstrated superior performance compared to their untreated counterparts19. The higher tool-tip temperature of the coated insert resulted in greater chip curling. Additionally, the shear band microhardness of chips produced using the coated tool appears higher than that of the uncoated insert.

Amol et al.20 investigated the temperature distribution in coated cemented carbide cutting tools through both experimental and FEA. They measured cutting forces and edge temperature distributions during two-dimensional orthogonal turning, highlighting that edge deformation and flank land development significantly affect the observed temperatures of cutting tools. In a study by Ucak et al.21 the FEA was carried out for Inconel 718 superalloy. It was reported that a 3D model can be employed for optimization of tools and processes. In a separate study, Klockea et al.22 developed a system for inversely identifying flow stress data in orthogonal cutting, utilizing methodologies from the University of Kentucky, USA, and RWTH Aachen University, Germany. This method enabled the identification of data in the case of flow stress for Inconel 718 as well as AISI 1045 steel, leading to 2D FE models under various conditions. The approach was validated across different cutting speeds and feed rates.

Vishnu et al.23 created a two-dimensional FE model to simulate orthogonal machining in dry conditions using commercial FEA software Ls-Dyna. Their simulations demonstrated that cutting forces increased with uncut chip thickness at a constant rake tool angle. Chong et al.24 employed a combination of the FEA method and SPH to study material behavior during metal cutting, confirming that their simulation results were valid for comparing different metal-cutting techniques. Zebala et al.25 focused on simulation studies concerning the processing of aircraft components, analyzing how energy values in machining could influence cutting parameters, productivity, tool life, and cutting time. Shahverdi et al.26 addressed simulation difficulties by conducting comprehensive simulations using Ls-Dyna, employing SPH and Arbitrary Lagrangian Eulerian (ALE) methods. They noted that the ALE method yielded results that aligned closely with the sinusoidal behavior observed in the SPH method. Sohail et al.27 examined residual stresses after machining A1-6061 aluminum alloy, discovering that FE modeling could effectively predict these stresses. Raczy et al.28 utilized Euler’s original model to analyze the cutting process of commercially pure copper, incorporating materials like plastic rubber and the Johnson-Cook (JC) model to predict stress distribution, strain distribution, and shear forces during cutting. Marin Baker29 applied a two orthogonal FE model in metal cutting to explore the effects of cutting speed on cutting forces and chip formation processes.

Villumsen et al.30 pioneered the metal-cutting method using Lagrangian analysis in Ls-Dyna software. Their findings indicated that increased effective plastic failure strain correlated with heightened shear and thrust forces. Further, they analyzed force output by adjusting parameters such as particle resolution and friction between the workpiece and the tool. Dorogoy et al.31 validated Johnson-Cook parameters using shear stress model values in Abaqus light software, establishing a logarithmic relationship between the JC model and the normal strain rate. Limido et al.32 evaluated the SPH method within a 2D SPH model framework for fast shear modeling in Ls-Dyna, confirming its efficacy in predicting cutting forces without considering friction parameters. Komanduri et al.33 assessed the heat source on the shear plane to estimate the tool-wafer interface temperature, assuming a rectangular heat source influenced by sliding surface heating. Zhang et al.34 developed a one-dimensional model of transient temperature distribution for single-layered devices using the Laplace transform, finding uniform interface temperatures across layers, although the alumina layer exhibited a lower substrate temperature over time.

Chou and Song35 estimated heat generation, shear plane geometry, and rake surface heat sources by treating the heat source as a collection of smaller heat sources. They determined that high temperatures negatively impacted feed and cutting speed while increasing cutting depth, particularly at higher feed rates. Abu Heshim et al.36 measured the tool tip temperature with a pyrometer on the rake face, observing that heat distribution diminished as cutting speed increased until a certain point, after which it rose again. Grzesik et al.37 provided an in-depth simulation of orthogonal metal shear plane strain, examining how different layers affected tool tip temperature rise. Their integrated thermal simulation models with the process of orthogonal cutting models for both uncoated and coated tools successfully determined temperature distributions in the cutting zone. Sivaramakrishnaiah et al.38 verified that FE simulation results closely matched physical test data, which led to further ANSYS simulations demonstrating effective cooling compared to prior heating methods. They designed experiments to evaluate how machining parameters affect workpiece heat distribution, cutting strength, and surface roughness of uncoated tools. The study found that tools made of coated carbide performed superior in comparison to uncoated tools in reducing interface temperatures, cutting forces, and surface roughness. The research established an FEA simulation framework to predict chip behavior and cutting forces during the machining of Al-6061 employing WC tools, employing the JC model to reflect viscoelastic behaviors. Results were compared with experimental data, demonstrating a close match between measured and forecasted cutting forces based on parameters like feed, cutting speed, and depth of cut39,40.

Johnson and Cook41 developed a material model based on torsion and dynamic Hopkinson bar test over a wide range of strain rates and temperatures. This constitutive equation was established and is as follows41:

The very first parenthesis denotes the elastic-plastic word, signifying strain hardening. The following phrase pertains to viscosity, indicating that the flow stress of a material escalates when subjected to elevated strain rates. The last phrase pertains to temperature softening. The \(\:\sigma\:\) denotes flow stress while \(\epsilon\) represents the true plastic strain in Eq. (1). A, B, C, n, and m are material constants determined by material testing. T represents instantaneous temperature, Tr denotes room temperature, and Tm signifies the melting point of a certain substance. The JC material model posits that flow stress is influenced independently by strain, strain rate, along temperature. Advanced alloys such as Inconel 825 under extreme conditions provide great difficulties for machining; current research leaves some important issues inadequately investigated42,43. Particularly in terms of heat production produced by friction and plastic deformation and their consequent thermal gradients, the thermomechanical behavior of Inconel 825—especially under high temperatures and force rates—has not been well investigated. These elements greatly influence material characteristics yet are not well investigated. Although extensively used for thermomechanical modeling, simulation approaches such as SPH and FEA have limited predictive capability, computational efficiency, and fit with experimental data. Furthermore, the absence of strong experimental validation often compromises the dependability of these models as simulated results such as cutting forces and temperatures do not regularly coincide with actual data. Furthermore, typically handled without combining insights from both experimental and simulation-based investigations is the optimization of machining parameters like cutting speed, feed rate, and depth of cut for Inconel 825.

This study aims to enhance the understanding of the thermomechanical behavior of Inconel 825 under demanding machining conditions using SPH and FEA approaches. By exploring how temperature gradients influence material properties, the research seeks to closely replicate the heat generated through friction and plastic deformation. Additionally, it focuses on assessing the predictive capabilities of SPH and FEA models, emphasizing their computational efficiency and alignment with experimental observations. To ensure meaningful insights, experimental validation will be carried out, allowing a direct comparison between simulated and real-world outcomes, such as cutting forces and temperature distribution. Moreover, the study aims to refine machining parameters to improve precision, efficiency, and overall feasibility. By providing a more comprehensive perspective on the machining behavior of Inconel 825, this research hopes to offer valuable guidance for its industrial applications, contributing to more effective manufacturing practices.

The present study explores optimal turning conditions for machining Inconel 825 using a tungsten carbide tool through a combination of experimental and numerical approaches. To provide precise thermal measurements, transient temperature changes during the cutting operation were recorded using infrared thermal imaging. Given the costly nature of both Inconel materials and thermal imaging equipment, getting reliable temperature readings can be challenging, hence this method is very helpful. Following a L9 orthogonal array experimental design, the study methodically investigates, using three main machining parameters, the effect on temperature distribution and cutting forces. The experimental setup includes Inconel 825 as the workpiece material, a tungsten carbide cutting tool, and instruments such as a central lathe with variable speed and feed drive, an infrared pyrometer, and a dynamometer.

Inconel belongs to the group of austenitic nickel-chromium-based super-alloys. Alloy 825 is a nickel-chromium-iron alloy with the inclusion of titanium, copper, and molybdenum. The tool and workpiece’s mechanical and thermal properties are listed in Table 1. The Kistler dynamometer and infrared camera were used to measure cutting forces, and temperatures and to generate during the turning operations with the tool holder with inserts, based on holder specifications (25 × 25 M12), and the experimental setups are illustrated in Fig. 1.

Lathe machine with Infrared camera experimental Setups for different experiments.

The center lathe is widely employed for producing tubular components from various materials, such as steel and polymers. Numerous essential engine assembly components are manufactured using lathes, which may be handled manually or by automated control systems, such as CNC machines, particularly configured for accurate machining operations. This investigation involved turning operations conducted on a typical lathe, with a feed rate between 0.04 and 0.12 mm per revolution. The workpiece, fabricated in a cylindrical configuration of 100 mm in length and 30 mm in diameter, was manufactured with a tungsten carbide cutting tool on a 2 kW HMT lathe. The machining trials were performed at varied speeds reaching 1500 revolutions per minute. The surfaces were cleaned up with a light machining pass, removing 0.1 mm from the uppermost surface with a tungsten carbide cutting tool on a normal lathe. This procedure ensured the removal of surface imperfections or wobbling, hence enhancing the accuracy of following machining processes. The machining cutting forces were measured using a Kistler dynamometer (Type 9272 model), linked through a high-insulation cable. The dynamometer transformed the produced electrical charge into corresponding voltage signals via an amplifier circuit, facilitating precise force measurement. The research concentrated on three principal force components: cutting force (Fz), radial force (Fy), and feed force (Fx).

The temperature distribution surrounding the shear zone was gauged using an infrared thermal imaging camera. Thermal imagers were employed to record temperature differences in the shear domain by translating infrared radiation into visual images. This non-contact measuring method produced thermographs as output. An InfraTec VarioCAM hr head 400 infrared camera (temperature range: − 40–2000 °C, thermal sensitivity: 30 mK at 30 °C) was utilized for temperature measurement during machining. The workpiece emissivity was set at 0.4, and the IRBIS 3 plus software was employed for data acquisition and thermal analysis. An infrared thermal images camera (3.6–5 μm) assembly consisting of a scanning unit, an infrared detector, and a VGA monitor had been employed for measuring high precision cutting temperature (− 40–2000 °C range). The thermal imager converts the infrared radiation captured by the camera into images that illustrate accurate temperature distribution across the tool and workpiece surface. The temperature measure results by this noncontact method were obtained in reports in the form of thermographs (The thermal setup consists of vision research-phantom V5.2’s, high-speed camera, various points, and scattered highly system and infrared thermal imaging camera assembly). Apart from an infrared thermal camera, a standard infrared thermocouple to estimate the cutting temperature on the tool’s rake faces.

Cutting was first done under user-defined criteria covering cutting speed, feed rate, and depth of cut. Using an Orthogonal L9 array, the experimental design followed the Taguchi approach whereby the cutting parameters were arranged in a matrix to enable nine different experimental trials44. The lathe machine set the parameters of speed, feed, and depth of cut; the cutting operation was carried out 13 mm in each trial, so ensuring that the machining time did not run more than 10 s. An infrared pyrometer was used to track the cutting temperature during the operation. This process was done regularly over all nine trials since the design matrix is shown in Table 2 while the parameter levels are in Table 3.

Geometric transformation of tool-workpiece interaction: 3D to 2D projection.

The geometrical representation of the workpiece and tool was simplified from 3D to 2D by taking a cross-section of the cylindrical workpiece, as depicted in Fig. 2. Numerical simulations were carried out using FEA with the 2D ALE modeling software, Abaqus setup of an orthogonal cutting model with the thermal boundary and mechanical conditions in Fig. 3. The software provides advanced features for simulating machining processes, such as adaptive re-meshing capabilities to resolve varying length scales, multi-body deformable contact to model tool-workpiece interactions, and transient thermal analysis45,46. For large deformations, the material properties in the model combine thermal softening, deformation hardening, and rate sensitivity connected with a transient heat conduction analysis47.

In the simulations, the coefficient of friction was set at 0.5 constantly. The orthogonal cutting conditions applied for the two-dimensional FE mesh are shown in Fig. 3 schematically. While the process parameters consist of feed rate (f), cutting speed (V), and depth of cut (doc), the cutting tool is characterized by its rake angle, clearance angle, & cutting-edge radius. In the FE modeling, the JC plasticity model was implemented in conjunction with the defined cutting parameters. This material model is widely used in simulating orthogonal cutting processes using FEM due to its ability to capture the strain-rate-dependent behavior of materials, which significantly influences the stress-strain relationship during machining48,49. The JC parameters for the Inconel 825 workpiece, as detailed in Table 4, were derived from the Abaqus-2D user manuals. This model is particularly suited for scenarios involving a broad range of strain rates (102−106 s−1) and can accurately predict plastic deformation, as well as material damage and failure, under varying cutting conditions.

These simulations examined the influence rate of feed, speed of cutting, as well as depth of cut over important performance variables including cutting forces and temperature. Under high feed rate as well as cutting speed, the results revealed notable variations across cutting forces and temperatures. The CPE4RT element has been selected for meshing the model. This element is very dependable for thermocoupled mechanical applications in metal cutting systems. The JC plasticity model, which correlates stress, strain rate, as well as temperature, is essential for an effective modeling process50,51. The fundamental equation of the above model is demonstrated below. The simulation technique employs the JC material constitutive model, which illustrates stress as an indicator of strain, strain rate, and temperature. This model is often integrated into finite element method (FEM) modeling software, such as Abaqus. Herein, σ represents equivalent stress, ε denotes comparable plastic strain, T signifies the material’s temperature52, εo refers to the strain rate, Tr is the source temperature, Tm indicates the melting temperature. Also m, n, A, and B are coefficients.

Equation (2) contains several material-specific constants for Inconel 825, which were determined through standardized experimental procedures. These constants were derived from empirical testing to accurately characterize the material’s mechanical behavior under varying strain rates, temperatures, and loading conditions, ensuring precise representation within the constitutive model. During the simulation procedure, it is understood that the work item authorized will only respond in plastic. Figure 4 depicts a meshed design with contacts.

Figure 3 represents a finite element model boundary condition for a metal cutting simulation. The reference temperature is set at 293 K, indicating ambient conditions applied to the boundaries of the workpiece. Represented as ql, the heat flux arises at the tool-chip and tool-workpiece interfaces from plastic deformation and friction; arrows show the direction of heat dissipation. The cutting depth indicates the degree of material removal, so affecting cutting forces and heat generation. Tool velocity describes the cutting tool’s movement, so influencing thermal distribution and strain rate. Tool wear and cutting temperatures can be affected by the rounded edge of the cutting tool indicated by its tool radius. Playing a part in chip formation and heat dissipation, the parameter θf represents the rake angle or cutting-edge inclination. Using finite element modeling, this graphic shows the heat flow and boundary conditions taken into account in the thermal analysis of metal cutting.

2D Structure of workpiece and tool.

The SPH & FEA model, as illustrated in Fig. 4, comprises a rigid tool (modeled using FEM elements) and an SPH-based workpiece. The tool was constrained in the y and z directions, with motion restrictions applied in all six degrees of freedom. The workpiece had dimensions of 4.8 × 0.95 × 0.3 mm and was represented by 2,000 SPH particles, which determined the particle spacing within the model. All translational degrees of freedom had constraints at both the left and bottom SPH nodes to restrain the workpiece. In the FEA (Abaqus) mesh model CPE4RT element was employed. This element has four nodes, is hybrid, has constant pressure, and is bilinear in temperature and displacement. The thermo-coupled mechanical use of metal cutting systems is where it is most dependable.

SPH and FEA meshing model scheme.

To replicate the thermomechanical behavior of the machining process, the work also combines numerical simulations employing Abaqus FEA and the SPH method. A strong association between the two in terms of temperature distribution and cutting forces is shown by employing validation against experimental data. This validation helps to justify the use of FEA and SPH models for important parameter prediction in metal cutting, including material deformation and temperature increase, hence lowering the demand for large-scale physical testing. The main conclusions of the work include the notable decrease in interface temperatures, cutting forces, and surface roughness when coated WC tools are used rather than uncoated ones. This shows how well-improved tool coatings could help to maximize Inconel 825 machining. Finally, a thorough framework for comprehending and optimizing the machining of Inconel 825 is given by the combination of physical testing, sophisticated temperature monitoring methods, and strong numerical simulations. Particularly for high-performance uses involving challenging-to-machine materials like Inconel alloys, the results of this research show great potential for improving precision turning processes in the manufacturing sector.

Interface temperature rises correspondingly as the cutting speed rises; cutting pressures tend to be smaller at slower cutting speeds. Greater cutting pressures follow from higher feed rates. Higher chip temperatures and tool-chip interface temperatures as well as greater cutting pressures resulting from more wear follow from a deeper depth of cut. On the other hand, lowering the included angle of the cutting insert reduces both chip temperature and tool-chip interface temperature, lowering only cutting forces (Fz) as illustrated in Fig. 5a–h. The main goal of the L9 orthogonal experiment was to match machining-produced temperatures. Cutting speed did not affect the resultant cutting temperature in the case while feed rate rose from 0.04 to 0.12 mm/rev. However, as Table 5 shows, higher temperatures were seen with higher feed rates.

Cutting forces (FZ), Feed force(Fx), and Thrust force(Fy) for (a) First (b) Second (c) Third (d) Fourth (e) Fifth (f) Sixth (g) Seventh (h) Eighth sample.

The temperature during machining for the WC cutting tool is conducted using data from L9 orthogonal experiments. The machining experiments are performed at varying feed rates, cutting speeds, and depths of cut. When compared to coated cutting tools, uncoated tools exhibit a greater increase in temperature as the feed rate rises from 0.04 to 0.12 mm/rev. Cutting speed has little effect on the resultant cutting temperature; however, higher temperatures are observed at larger feed rates. For each machining experiment, recorded video data is analyzed offline, and the cutting temperature data shown in Fig. 6 is collected at regular intervals of 10 to 15 s. Temperature signals are converted into digital form every second. For varying times, eight data points are generated, with a measurement distance of 0.5 mm from the measurement surface. Table 5 presents the temperature distribution as shown in Fig. 6. The temperature evaluation process in the simulations. This includes the specific regions of the workpiece or tool where temperature data was extracted and the rationale for these locations.

In the simulation model, temperatures were evaluated at the chip-tool interface, cutting edge, and workpiece surface, corresponding to the regions of maximum heat generation and transfer. These locations were chosen to align with the experimental thermal measurements captured by the infrared camera. The temperature distribution was analyzed along predefined nodes in the FE model to match the spatial resolution of the experimental setup.

Thermal imaging temperature formation using Infrared camera.

The FE analysis of the turning process was conducted using Abaqus 6.13 in SPH and FEA software in a 2D plane format. In this simulation, the cutting tool as well as the test workpiece are modeled as deformable objects, allowing for deformation to occur during the turning operation.

The cutting forces (Fz) and temperatures were determined through both simulation and experimental setups, with the results summarized. Figures 7 and 8 illustrate comparative graphics between the simulation and testing results for turning with a WC tool. The FEM simulation results were compared with experimental values, as presented in Figs. 7 and 8. The graphs of cutting forces plotted for FEM and SPH values alongside the actual test results, are depicted in Fig. 6. Figure 7 indicates that the FEM and SPH values closely align with the actual test values and exhibit similar trends. The FEM model proves to be highly effective. Ultimately, it is evident that the SPH simulation values for forces and temperatures are higher compared to both the FEM and experimental values.

Three independent approaches FEA, SPH, and experimental measurements are used to generate a bar chart (Fig. 5) showing a comparison of cutting forces expressed in Newtons. The data is displayed in nine separate cases on the chart, each corresponding to a different machining condition. Generally speaking, the experimental findings show more cutting forces than what FEA and SPH would indicate. For example, in scenario 3 the experimental force achieves a value much above both SPH and FEA values. Several situations show a similar tendency whereby the experimental pressures often surpass the simulations-based forecasts. This disparity could result from the intrinsic limits of numerical techniques, which frequently reduce difficult real-world variables such as heat gradients, material behavior, and tool wear. There are, nevertheless, occasionally cases when the simulation findings quite nearly match the experimental facts. In case 4, for instance, the improved correlation is shown by the experimental force being somewhat close to the FEA projections. This suggests that even if numerical models offer useful prediction insights, more modeling technique improvement is required to raise the agreement with the experimental result’s overall circumstances. The graphic shows the general trend and emphasizes the possibilities of combining computational approaches with physical tests to maximize cutting operations.

The provided bar chart (Fig. 8) contrasts the degrees Celsius (°C) temperatures measured in nine different instances. These cases show differences in machining parameters including feed rate, cutting speed, as well as depth of cut that influence the temperature distribution at the workpiece-tool interface during the turning process of Inconel 825 using a WC tool. Generally speaking, the FEA results show more temperature forecasts than either SPH or experimental approaches. For instance, the FEA approach forecasts higher temperatures in examples 1, 5, and 7 while the SPH and experimental results are usually more closely aligned. This disparity implies that maybe from simpler assumptions about heat transmission and material behavior, the FEA simulations may overestimate the heat generated during machining. On some occasions, such as case 4 and case 6, the SPH approach reveals temperatures more in line with the experimental values while FEA often overestimates the thermal impacts. Reflecting more realistic thermal conditions found during actual machining operations, the experimental findings usually lie between the FEA and SPH predictions. This comparison emphasizes how well employing several strategies to forecast the distribution of temperature works. Although FEA often overpredicts in some situations, especially in cases involving complicated temperature dynamics, SPH results seem to offer a closer fit to the practical trials. The temperature was monitored at several spatial points along the tool’s top rake face. The two measurements at distinct spatial positions were tallied for nine scenarios based on the different input values.

Comparison of cutting force (Fz) obtained.

Comparison of temperature obtained.

Simulation serves as a prognostic tool that leverages an appropriate mathematical model to anticipate system behavior under specified conditions53. In this context, a model was created to estimate the cutting force. Using independent variables including feed, speed, and depth of cut, Minitab was used to generate a multiple regression model for the dependent variable, cutting force. The following expression (Eq. 3) is derived.

Equation (3) was employed to estimate and forecast cutting force values, as presented in Table 6. The needed heat energy was then determined using Eq. (4).

Where Fz and V are Cutting Force (N) and cutting speed (V), respectively.

The simulated temperature data were evaluated in the same regions as those monitored experimentally. A comparison of the peak temperature values and temperature distributions showed close agreement.

This research examined the machining characteristics of Inconel 825 utilizing WC tools, integrating experimental analysis with finite element models to enhance cutting forces and interface temperature. The study revealed a significant connection between experimental and numerical results, offering important insights into the interplay of machining parameters. The principal findings derived from the study are as follows:

The finite element technique (FEM) with Abaqus accurately predicted cutting forces, with simulated outcomes corresponding to experimental results within a 5% error range.

Cutting forces immediately surged abruptly, attained a steady-state phase, and thereafter diminished progressively until stabilizing, therefore validating the precision of the modeling methodology.

Increased cutting speeds (Vc) led to reduced cutting forces, illustrating the inverse correlation between speed and force.

The peak interface temperature was observed at a cutting speed of 95 m/min, after which a decrease in speed increased temperature, suggesting intricate thermal interactions.

WC inserts improve heat dissipation, hence enhancing machining stability and minimizing excessive thermal accumulation.

Infrared thermal imaging facilitates real-time temperature monitoring; nevertheless, more study is required to elucidate the disparities in recorded cutting temperatures.

The research employed an L9 orthogonal array to elucidate the correlations among machining parameters, interface temperature, and cutting forces, facilitating process improvement.

Transient heat transfer and SPH simulations corroborated the experimental findings, affirming their validity.

Future endeavors will concentrate on applying this technique to industrial contexts, partnering with industry stakeholders to enhance machining tactics for diverse material-tool pairings.

The data that support the findings of this study are available from the corresponding author, [AGA], upon reasonable request.

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The authors appreciate the respective institutions for their extended support throughout this research work.

Any funding agency did not support this research.

Department of Mechanical Engineering, SVR Engineering College, Nandyal, Kurnool, Andhra Pradesh, 518502, India

Malayathi Sivaramakrishnaiah

Department of Mechanical Engineering, G.Pullareddy Engineering College, Kurnool, AP, India

R. Meenakshi Reddy

Department of Mechanical Engineering, JNTUA College of Engineering Pulivendula, YSR kadapa(District), AP-516390, India

A. Damodara Reddy

Department of Research and Innovation, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, 602105, India

Prabhu Paramasivam

University Center for Research & Development (UCRD), Chandigarh University, Mohali, Punjab, India

Praveen Kumar Kanti

Department of Mechanical Engineering, Adama Science and Technology University, Adama, 2552, Ethiopia

Abinet Gosaye Ayanie

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MS: conceptualization, methodology, data curation, writing – original draft. RMR: writing – review and editing, SoftwareADR: Validation, Project administration, writing –review and editingPP: writing – review and editing, Supervision, ResourcesPKK: Software, Visualization AGA: writing – original draft, writing –review and editing.

Correspondence to Prabhu Paramasivam or Abinet Gosaye Ayanie.

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Sivaramakrishnaiah, M., Reddy, R.M., Reddy, A.D. et al. Experimental and thermomechanical SPH and FEA model simulation using inconel 825 with tungsten carbide tool. Sci Rep 15, 10053 (2025). https://doi.org/10.1038/s41598-025-95018-6

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Received: 14 January 2025

Accepted: 18 March 2025

Published: 24 March 2025

DOI: https://doi.org/10.1038/s41598-025-95018-6

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