Tool Tip Temperature Research Articles

Influence of TiAlN coating thickness in dry machining of AISI 1045 steel using Finite Element simulation and experiments

High performance cutting can be accomplished using coated tools with optimal coating thickness. Therefore, Finite Element (FE) simulation and experimental investigation are employed to study the influence of thickness of TiAlN coating in the range of 1 to 4 μm during face milling of AISI 1045 steel under dry condition. FE simulation is successful in effectively investigating the influence of coating thickness in machining performance in terms of cutting forces, Von Mises stress, tool tip temperature and crater wear of uncoated as well as coated milling inserts. Simulation results have predicted cutting forces and temperature with maximum errors of 9.21 % and 12.20 %, respectively. FE simulation further demonstrates 2.100 and 1.602 μm as the depths of crater for 1 and 3 μm thick TiAlN coated inserts, respectively, which are also validated by the experiments. The experimental results demonstrate that 3 μm thick coated tool is superior in mitigating cutting forces, particularly under higher cutting speed (150 m/min), bringing down tool tip temperature (6.67 %) and machined surface roughness (18.18 %) as compared to 1 μm thick TiAlN coated milling insert. The results obtained from the current research work clearly indicate that the effect of coating thickness can be effectively investigated using the FE simulation before finalising the optimal coating thickness for the coating depositions and machining experiments.

Experimental investigation on sustainable machining of monel using vegetable oils as cutting fluids and machine learning-based surface roughness prediction

Abstract The material processing industry is anticipated to mitigate environmental degradation. The protocols established by the International Organisation for Standardisation were adhered to. As a result, it would be prudent to investigate the feasibility of minimizing the use of synthetic cutting fluids from the machining process. This study discusses an environmentally-friendly machining technique for turning nickel-based alloy Monel-500, which evaluates four different cooling conditions: dry machining, flood machining, Co-MQL (coconut oil), and Rb-MQL (Rice Bran Oil). These conditions were tested by experimenting with various machining parameters to investigate four aspects of the turning process: surface finish,cutting temperature, tool wear and chip morphology. Rice bran oil is considered eco-friendly compared to synthetic cutting fluids, and employing it in minimum quantity is economical and helps improve the machined workpiece’s surface finish. The investigation has been further extended by applying machine learning algorithms to predict surface roughness, utilising two logical regressions implemented in Python. Among the two machine learning approaches, the random forest regression technique has demonstrated superior results, achieving a prediction accuracy of 99.8%. Consequently, a decision tree has been developed using this regression model to predict the surface roughness. The structured analysis of the decision tree provides more accurate conclusions, offering flexibility in adjusting parameters and expanding options for operation. As a result, the decision tree approach enables the efficient utilisation of production resources and enhances production capacity by making informed choices about cooling methods during the turning process. Rb-MQL has performed better in all aspects than the other three cooling conditions. When comparing machining under dry conditions, flood cooling, Co-MQL, and Rb-MQL (rice bran oil) reduce the tooltip temperature by 39.5%,25.45 and 24.11%, respectively. Rb-MQL reduced surface roughness by 28.23%,43.59 and 60.49% in contrast with machining under dry, flood, and Co-MQL.

Assessment of the influence of flushing on the wear and morphological properties of the tool during electrical discharge machining

The present study is an integrated approach of experimental and simulation processes to investigate the influences of the flushing on the wear, textural feature, heat dissipation, tool-tip temperature, and elemental contents of the electrical discharge machining-tool. The general heat transfer equation in cylindrical coordinates is used to explain the thermal phenomenon through the tool, where the boundary conditions are influenced by convective interactions and dimensionless numbers. The inter-electrode flushing is considered a micro-channel flow, and the used model is conceptualized from “Hazen–Poiseuille observation.” It is observed that the adopted model has a prediction error of 8.503% and can accurately explain the thermal consequences of the process. The study reveals that tool wear is influenced by flushing velocity and flushing pressure. The tool-tip temperature reduces with the Reynolds number, and effectual expelling of the debris can be ensued with a flushing of a higher Reynolds number (Re). However, the increment of Re beyond 4500 provides rapid heat dissipation, which produces extensive residual stress and creates cracks on the surface.

Improving the novel white coconut oil-based metalworking fluid using nano particles for minimum surface roughness and tool tip temperature

At present, nearly 85 % all of the requirement for MWFs are satisfied by the use of mixtures of petroleum by-products and synthetic substances with supplementary additives to enhance their properties. The demand for easily biodegradable, environmental friendly MWFs is a current requirement. COCOTP, a novel biodegradable MWF based on white (refined) coconut oil, developed by authors, had previously shown promising tribological properties for machining mild steel (MS) and stainless steel (SS) when used with the flooding method, but had fallen short of the performance of commercially available, non-biodegradable alternatives with MQL. Therefore in the current investigation, nano-particles were added to improve the performance of novel COCOTP MWF to use it with MQL conditions. Two nanomaterials nano-graphite and nano-Al2O3 were separately added to the base fluid in different concentrations as a monodispersed suspension. These nano enhanced fluids (NEFs) were subsequently used in straight turning experiments of two work materials AISI304 and SS400. Both the nano-enhanced fluids show convincing improvements over both COCOTP and mineral-oil based fluids in terms of surface roughness of the specimens regardless of the material being turned. However when machining SS 400, NEFs perform better only in lower speeds in terms of temperature. SS400 has a much higher thermal conductivity than AISI304 means that the quantity of residual heat remaining at the point of material removal which can be absorbed by the cutting fluid is lower in SS400. During machining SS400 under MQL lubrication 9.8 %, 26.8 % and 24 % reduction of surface roughness values (with respect to soluble oil) and during machining AISI304 55.3 %, 73.7 % and 70.4 % reduction of surface roughness values were obtained at 1175 rpm with COCOTP, NEF A and NEF G respectively. Based on the experimental results, the best-performing nano-enhanced fluids under MQL are 0.3 % (w/w) Al2O3 and 0.3 % (w/w) graphite.

Development of a numerical cutting model for understanding the chip formation in cryogenic machining

The aim of this study is to develop a finite element model of metal cutting that includes fluid structure interaction. The proposed model is used to improve the physical comprehension of the chip formation during cryogenic assisted machining. This is performed based on the coupled-Eulerian-Lagrangian formulation. An algorithm is also developed to ensure heat exchange between the fluid and the structure. It also allows decoupling the mechanical and thermal effects induced by liquid nitrogen. The titanium alloy Ti17 is used as a work material, and uncoated tungsten carbide is used as a tool under dry and cryogenic conditions with cutting speeds of 50 m/min and 70 m/min and feeds of 0.1 mm/rev and 0.2 mm/rev. Simulation is validated by comparison with the experimental results. Standard deviations of both forces and chip thickness are low; they stay under 15% and 35% of the average value, respectively. The chip formation, the cutting force and the temperature are studied. It is shown that cryogenic machining has a significant effect on the chip formation and the temperature distribution in the cutting zone. A remarkable temperature reduction by up to 500 °C is observed at the tool rake face. However, the tool tip temperature slightly decreases with the application of liquid nitrogen when compared to dry machining.

Experimental investigation and optimization of cutting parameters during dry turning process of copper alloy

Increasing the quality and productivity of machined components are the main issues of machining operations in metalworking industries. The copper alloys CuZr and CuCrZr generally find applications for current-carrying structural components, seam welder wheels, shafts, and bearings flash. The manufacturing of these components is still facing challenges in the form of machining process characteristics. One of the most common machining operations for removing material is turning that produces reasonably good surface finish quality, which is influenced by different factors (speed of cut, rate of feed, tool geometry, cutting fluid, cutting tool, etc.). This research has focused on experimental study and optimization of the cutting parameters viz. cutting speed, depth of cut, and feed, for best surface finish, material removal rate, tool tip temperature as well as surface morphology during dry turning of C15000 and C18150 copper alloy using High-Speed Steel (HSS) tool The plan and design of experiment has been performed through orthogonal Taguchi L9. array. The optimum cutting settings were discovered by using the Taguchi technique and using the performance index by applying a Grey Relational Grade (GRG). The best cutting parameters for both materials were a cutting speed 1200 rpm, feed rate 0.06 mm/rev, and depth of cut 1.25 mm. The optimum factors obtained from GRA for all responses (surface roughness, MRR, and tool temperature) at the best level of cutting parameters are the same for both materials. These cutting parameters values yielded the experimental result for each response like surface roughness, MRR, and tool tip temperature (2.5 µm,12,475 mm3/min, and 74 °C) for grade C15000 whereas (2.39 µm, 2590mm3/min and 68 °C) for grade C18150. The optimization of cutting parameters plays a vital role in the improvement of surface finish which minimizes mechanical failures caused by wear, corrosion, and thereby increasing the productivity of the products. This investigation is expected to help all researchers working in this area of applications.

Towards Industry 4.0 and Sustainable Manufacturing Applying Environmentally Friendly Machining of a Precipitation Hardened Stainless Steel Using Hot Turning Process

This study aims to address the aforementioned challenges, solutions and implementation perspectives with regard to sustainable manufacturing. In this research, the conventional and hot turning of AISI630 hardened stainless steel have been investigated using PVD-(Ti,Al)N/(Al,Cr)2O3 coated carbide cutting tools at various feed rates and cutting speeds. The high hardness of AISI630, along with the low thermal conductivity, has made it one of the most difficult-to-cut materials, and consequently, its machining is associated with high tool wear and poor workpiece surface quality. AISI630 stainless steel is used in the manufacture of pressure vessels and components exposed to high-stress and corrosive environments in the oil and gas industries. In the present research work, tool flank wear and crater wear mechanisms have been studied in different cutting conditions as well as different preheating temperatures using SEM microscopy. Experimental results showed that hot turning operation at temperatures up to 300 °C reduces flank wear by 33% and improves machined surface roughness by 23%. In addition, FEM simulation has been developed to predict tool tip temperature and cutting forces during turning processes. Experimental and FEM analysis shows that cutting force reduction at a preheating temperature of 300 °C is one of the reasons that reduces tool wear compared to conventional turning. Moreover, it has been shown that by increasing preheating temperature in hot turning, the hardness of the carbides in the workpiece decreases more than the hardness of the tool substrate and reduces coating materials, consequently reducing cutting tool abrasion wear phenomenon.

Influence of surface roughness and thermal characteristics in turning operation using nanofluid lubrication

The turning operation is commonly used as a major material removal process in the manufacturing industry. The rate of heat generation during machining operation causes deprivation in the surface finish of the workpiece, reduction in tool life, localized melting of burr, which results in built-up edge (BUE), and therefore causes high cutting force, etc. Any manufacturing technique requires adequate lubrication to attain the optimum tool tip temperature. The nanofluid can be used as a lubrication fluid for controlling the temperature. In the present study, the experimental data for temperature and surface roughness is obtained at different workpiece rotation speeds at the same depth of cut and the same feed of the turning operation. It is observed that at temperature reduces by 25 % to 33 % compared with the dry run at the tool tip and workpiece at spindle speeds equal to 250 RPM, 420, and 710 RPM using the mixing of cutting oil + water + different proportions of Al2O3 nanoparticles. It is also observed that the surface finish is also improved by 11.26 % to 17.26 % at different spindle speeds with the use of nanofluid as a lubricating fluid.

Effectiveness of coolant on sustainable drilling and hole quality of titanium alloy

The present research investigated the effects of input parameters such as feed rate, cutting speed and coolant type on sustainability and hole quality during the drilling of Ti-6Al-4V alloy. Several output parameters such as surface roughness, burr height, chip thickness ratio, thrust force, torque and peak power consumption were measured and analysed. Higher cutting speeds (2000 r/min) and lower feed rates (0.04 mm/rev) are recommended to be used to reduce drilling torque (0.77 Nm), thrust force (695 N) and power consumption (0.07 kW). However, it should be noted that increasing cutting speed (2000 r/min) can increase tool tip temperature which is detrimental, particularly for low thermal conductivity materials such as titanium alloys. In addition to that, the effect of tool wear also cannot be negated, as increased tool wear generates more heat in the cutting zone which was not effectively removed due to the poor thermal conductivity of titanium. Liquid nitrogen and chilled air had the greatest influence on temperature reduction at the cutting zone, however, its poor penetration and lubrication properties diminished hole quality. Minimum quantity lubrication conditions proved to strike a balance between good hole quality and enhancing sustainable machining by lowering torque, force and power consumption.

A simulation-based analysis on the cooling effect and the tool temperature distribution in modulated turning (MT)

Modulation assistance is widely used for breaking chips of highly ductile materials in turning. This process is also known as the “modulated turning” (MT) or the “modulation assisted machining (MAM)”. The process transforms continuous single point turning into an intermittent cutting process where the tool tip briefly disengages from the workpiece generating discrete short chips. Resembling the milling process mechanics, periodic tool disengagements in modulated turning introduces cyclic cool-down periods and thus it can potentially attenuate tool-tip temperatures. It has been hypothesized that this effect can greatly lower maximum and the average tool-tip temperatures improving wear resistance of the process. This paper presents a thermal model to predict the tool-tip temperature distribution in modulated turning. A generalized chip formation formulation is first developed to predict the uncut chip thickness variation, cutting forces and the tool engagement/disengagement phase durations. Thermomechanical behavior of tool-chip contact is then modeled to predict the rake face temperature distribution. Simulation studies are presented to analyze the temperature distribution and understand peak temperature (hot) spots on the rake face. It has shown that the phase angle parameter – the frequency ratio between workpiece rotation and tool modulation– of MT plays a key role in controlling the hot spots on the rake and can be used as a tool to influence the wear regime.

Machinability study of silicon carbide and cenosphere reinforced Al7075 composite using three different tool materials

AbstractThis research conducted a machinability study on Al7075 composite reinforced with nano‐sized (100 nm) silicon carbide and cenosphere (industrial waste) particulates with 1.8 % weight. These composites were fabricated utilizing an ultrasonically assisted stir‐casting setup and scanning electron microscope investigation was conducted to evaluate the dispersion properties of the reinforcement in the matrix phase. During the study, the effect of variation of feed rate, cutting speed and depth of cut on cutting forces and tool tip temperature has been studied. A total of 253 experiments were conducted using three different tool inserts polycrystalline diamond, cermet, and coated carbide under dry cutting conditions. Among the two components of the cutting force, it was noted that the primary cutting force was the largest. A full factorial response surface regression model has been developed and it is found that the regression model can predict cutting force and temperature with fair accuracy.

Investigating the machinability behaviour of Al7075/SiC5CS5 hybrid composite with the variation of tool geometry

This research investigates the machinability behavior of an Al7075 hybrid composite fabricated with micro-sized cenosphere (CS) and silicon carbide (SiC) particles. The composite was fabricated using stir-casting with ultrasonic assistance. The experimental study investigated the effect of variation of machining regimes (i.e., feed rate, cutting speed, and depth of cut) and tool geometry (tool nose radii) on main cutting force and tool tip temperature. For the study, the Cermet tool was considered as tool material for the dry turning of aluminum alloy and composite. In addition, a regression model was created using the response surface method (RSM) technique. The results indicate that the developed model can accurately predict output regimes with an accuracy ranging from 80.44 to 99.98%. Further, by using sequential approximation optimization, the optimal combination of input regimes for maximal metal removal rate was identified.

Comparison of Tool Wear, Surface Roughness, Cutting Forces, Tool Tip Temperature, and Chip Shape during Sustainable Turning of Bearing Steel.

In this study, a comparison of measured cutting parameters is discussed while machining AISI 52100 low-alloy hardened steel under two different sustainable cutting environments, those in which a dry and minimum quantity lubrication (MQL) medium are used. A two-level full factorial design method has been utilized to specify the effect of different experimental inputs on the turning trials. Experiments were carried out to investigate the effects of three basic defining parameters of turning operation which are namely cutting speed, cutting depth, feed rate effects and also the effects of the cutting environment. The trials were repeated for the combination of different cutting input parameters. The scanning electron microscopy imaging method was used to characterize the tool wear phenomenon. The macro-morphology of chips was analyzed to define the influence of cutting conditions. The optimum cutting condition for high-strength AISI 52100 bearing steel was obtained using the MQL medium. The results were evaluated with graphical representations and they indicated the superiority of the pulverized oil particles on tribological performance of the cutting process with application of the MQL system.

Different Aspects of Machinability in Turning of AISI 304 Stainless Steel: A Sustainable Approach with MQL Technology

Machining of AISI 304 austenitic stainless steel is considered to be difficult due to its structural aspects and low thermal conductivity, which leads to increased temperatures during machining. To overcome the challenges of machining AISI 304 stainless steel, several cooling and lubricating techniques have been developed. The main objective of this experimental study is to evaluate the different turning conditions of AISI304 stainless steel under dry and minimum quantity lubrication (MQL) environment conditions. The machining experiments were conducted using a two-level full factorial design method and utilized a TiC-coated cutting tool. The tool-tip temperature, cutting force and surface roughness were analyzed regarding three cutting parameters namely, cutting speed, feed rate and cutting depth. Also, chip macro-morphology was investigated to define the interaction at the tool-chip-workpiece region. The cutting medium affects the surface roughness significantly (more than 100%) for all cutting parameter values. In some environmental cutting conditions, high cutting speed provides 10% lesser surface roughness than low cutting speed parameters. Also, the cutting force decreases by 20% in low feed rate machining conditions. However, the effect of this parameter disappeared for cutting forces in high feed rates and low cutting depth conditions in both MQL and dry environments. Cutting speed was observed as the most influential factor on surface roughness, followed by feed rate. The depth of cut was the main parameter that caused the temperature to increase in the dry machining environment.

Machinability appraisal of nitronic-50 under dry environment using uncoated carbide inserts

ABSTRACT Nitrogen strengthened austenitic stainless steels are uneasy to machine. 22-13-5 stainless steel better known as Nitronic-50 is also not an exception. The current study deals with such an investigation where Nitronic-50 has been machined under varied cutting speeds, feeds and depths of cut and its machinability has been tried to be adjudged on the basis of cutting forces, apparent coefficient of friction, tool-tip temperature, chip reduction coefficient, flank wear width, chip morphology and surface roughness. It is seen that while using higher cutting speed-lower feed-higher depth of cut combination, tangential cutting forces were 18.83% and 29.57%, apparent coefficient of friction was 9.48% and 12.90%, tool-tip temperature was 11.02% and 22.31%, flank wear was 41.25% and 29.91%, and surface roughness was 22.91% and 24.27% lesser as compared to the higher values obtained with higher cutting speed-higher feed-higher depth of cut and intermediate cutting speed-higher feed-higher depth of cut combinations, respectively.

Turning of Al 7075-T6 aerospace alloy under different sustainable metalworking fluid strategies by coated carbide tools

ABSTRACT We primarily need to reduce the consumption of metalworking fluid to ensure sustainable and eco-friendly machining of aerospace alloys. The present study was planned to determine the efficiency of various eco-friendly metalworking fluid strategies for the sustainable turning of aerospace aluminium alloy (Al7075-T6) coated carbide tools under different eco-friendly metalworking fluid strategies namely dry machining, minimum quantity lubrication (MQL), Ranque-Hilsch vortex tube (RHVT), and compressed air. Machining performance was investigated in terms of micro-hardness, tool tip temperature, tool wear, cutting forces, work surface roughness, chip morphology, and energy consumed. Results manifested that MQL and tool coatings can significantly lower tool tip temperature by up to 16%, tool wear by up to 102–106%, average cutting forces by 17–21%, and surface roughness reduced from 11–21% as compared to dry conditions. Abbreviations: BUE, built-up edge; CVD, chemical vapour deposition; CrN, chromium nitride; DCR, disposed chip ratio; DLC, diamond like carbon; DSPR, disposal scrap part ratio; EDS, energy dispersive X-ray spectroscopy; MF, metalworking fluid; MoS2, molybdenum disulphide; MQL minimum quantity lubrication; PVD, physical vapour deposition; RHVT, Ranque-hilsch vortex tube; RPSR, recycled part scrap ratio; RSPR, remanufacturing scrap part ratio; SEM, scanning electron microscopy; SDSS, super duplex stainless steel; TiAlN, titanium aluminium nitride

Performance of Si-doped TiAlxN supernitride coated carbide tool during dry machining of Inconel 718 superalloy

Conventional high speed dry machining using uncoated carbide tool is challenging for machining of difficult-to-cut aerospace superalloy Inconel 718. On the other hand, coolant-assisted machining may not be a feasible solution always due to issues related to operator's safety and environmental protection. Use of harder tool materials is also not wise always in purview of production economics. Application of coated carbide tools is therefore being given importance since last several decades. In this context, present work examines performance of newly developed Si-doped TiAlxN supernitride nanocomposite (HSN2) coated carbide tool during dry machining of Inconel 718. Tool performance is assessed in perspectives of a few machinability criteria such as cutting force, tool-tip temperature and width of tool flank wear. Different tool wear mechanisms are explained in correlation with properties of tool substrate and coating material. The work also includes study of chip's micro-morphology. It is experienced that HSN2 coated tool outperforms uncoated WC-Co tool. As compared to uncoated tool, lower cutting force, reduced tool-tip temperature and shorter width of tool flank wear are obtained during application of HSN2 coated tool. HSN2 coated tool produces chips with better underside surface morphology and lower microhardness than its uncoated counterpart.HSN2 coated tool experiences ~17 % reduced temperature at the tool-tip when compared to uncoated counterpart, operated at cutting velocity = 47 m/min. As compared to uncoated carbide tool, HSN2 coated tool causes ~56 %, ~ 57 % and ~43 % reduced tangential cutting force, feed force and radial force respectively, at cutting velocity = 134 m/min. Usage of HSN2 coated tool causes lower flank wear (~ 15 % reduced) than that of uncoated counterpart, operated at the highest cutting speed (134 m/min). Chips evolved by using HSN2 coated tool corresponds to ~9 % reduced microhardness (within shear band) than that obtained using uncoated tool at cutting velocity = 103 m/min.

Investigation on the machinability of TiN nano particulate reinforced magnesium matrix composite

In the present work, the machinability of magnesium matrix nanocomposite was investigated. The nanocomposite consists of pure Mg as matrix and TiN Nano particulate as reinforcement. The composite was fabricated through powder metallurgy process for three different compositions such as 1.5, 2.5 and 5% by weight of TiN Nano particulates. Machinability behavior of the nanocomposite was studied through milling process with process parameters, feed rate of 5, 10 and 15 mm/min, depth of cut of 0.15, 0.3 and 0.45 mm and spindle speed of end mill remain constant of about 2000 rpm. The performance measures considered in this study are cutting forces, material removal rate, surface roughness and tool tip temperature. The milling process was carried out at room temperature for normal atmospheric conditions. Poly crystalline diamond (PCD) coated solid carbide flat type end mill was used in the study. L27 orthogonal array was used to conduct the experiments. The results from the study revealed the fact that the performance measures like cutting force, tool tip temperature and surface roughness are significantly influenced by TiN Nano particulate in the composite, whereas the material removal rate is greatly influenced by the machining parameter feed rate. High hardness, heat resistance and excellent wear resistance of TiN Nano particulate resulted in the influence of the performance measures. Main effect plots and interaction plots were generated for the performance measures which was further followed by ANOVA and second order polynomial regression equation to test the level of significance with least error.

Assessment of machining performance parameters of nanofluid-based MQL-turning: An experimental analysis and CFD modelling

Nano-cutting fluids as a coolant are often used in modern days manufacturing industry. These nano-cutting fluids have certain advantages over conventional coolants in terms of heat transferability and lubricity. The present focused study investigates the machining performance in Minimum quantity lubrications (MQL) turning operation using MoO3/water nano-cutting fluid. The machining performance parameters (like friction coefficient, cutting forces, surface roughness and wear) are investigated experimentally using MoO3/water nano-cutting fluid. Also, a computational fluid dynamics (CFD) model was developed to investigate the heat dissipation and temperature profile at rake and flank surface of the carbide cutting tool. The experimental outcomes of the present study illustrate the enhancement in machining performance in terms of reduction in friction coefficient, cutting forces, surface roughness and wear. Heat transfer analysis and a numerical model based on the notion of a CFD tool were used to explore the temperature propagation along with the flank and rake surface of tungsten carbide cutting inserts in order to properly measure tool tip temperature while turning. The simulation study demonstrated the reduction in tool tip temperature at rake face from 1031 K to 824.98 K for the cutting fluid velocity of 25 m/s, while for 2 bar cutting fluid pressure the temperature at rake face reduces to 874.37 K.

Determination of tool tip steady-state temperature in dry turning process based on artificial neural network

Effective and accurate measurement of cutting temperature of the tool tip is of great significance in the machining fields. However, the current measuring methods, including the embedded thermocouple and thermography, have some inevitable shortages. For example, the hole for displacing the embedded thermocouple damages the cutting edge strength, and the chip blocks the view contact between the tool tip and the thermal imager. This paper proposes two feed-forward multi-layered perceptron artificial neural network (ANN) models with Levenberg-Marquardt backpropagation training algorithms to determine the tool steady-state tip temperature for the dry turning process. The first model (called TC_ANN) calculates the steady-state tip temperature based on the temperatures of four specific points on the rake face. The second model (called TP_ANN) predicts the steady-state tip temperature via the cutting parameters. First, a fully thermo-mechanical finite element simulation model of the dry turning process is established. Next, the simulation model is repeated with different combinations of cutting parameters for sufficient times. Then, collection of the steady-state temperatures of the tool tip and these four specific points, and corresponding cutting parameters for each simulation model is conducted as the dataset to train these two ANN models. Based on the evaluation results with experimental data, the simulation model is validated to be reliable, and the mean relative errors of the tip temperature calculation for the actual turning process via TC_ANN and TP_ANN are 3.25% and 4.03%, respectively. Finally, the influences of cutting parameters on the tool tip temperature are investigated based on TP_ANN. It is found that the cutting speed and depth of cut have the most and least significant effects on the tip temperature, respectively, and the feed rate has almost the same influence as the depth of cut.