Flank Wear Model Research Articles

Study of the influence of cutting parameters on tool wear and the state of the machined surface

This work presents a study of wear on the clearance face of turning tools. This study will contribute to the development of improved machining precision and efficiency while extending tool life and also ensuring the surface quality produced, despite wear and edge deposition phenomena. A tool flank wear model is developed as a function of the cutting conditions, including the cutting speed, depth of cut, feed rate, and machining time. Factors influencing tool clearance face wear are determined through experimental testing. We also investigate the edge phenomenon reported on the tool cutting face by plotting a curve of the edge variation versus the machining time. Tool wear and built-up edge phenomena both affect the quality of the surface finish produced. Additionally, the edge phenomenon degrades the geometric quality of the tool and generates wear on the tool clearance face. Therefore, models are developed to aid in selection of appropriate cutting conditions that ensure the expected surface finish is realized while also providing control over tool wear. Overall, this method combines experimental and theoretical approaches to study and control turning tool wear with the aim of improving both machining quality and efficiency.

Prediction of the cutting tool wear during dry hard turning of AISI D2 steel by using models based on Learning process and GA polyfit

In this paper, we are interested in the prediction of flank wear through dry hard turning of AISI D2 steel with a mixed alumina insert. In the machining process, the cutting tool is principally affected by two kinds of wear: flank and crater wear. The latter are criteria for cessation of the tool function. In the absence of a real-time wear sensor, it is necessary to know or track wear with the view to prevent tool damage. For this purpose, the current research focuses on the development of predictive models of flank wear based on Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Machine (SVM), and Polynomial Fit using Genetic Algorithm (GAPOLYFITN). The simulation process involves considering input variables including feed (f), cutting speed (Vc), and cutting time (tc); the output is the flank wear (VB). To assess the statistical efficacy of the predictive models, some performance indicators were employed, including the R-squared statistic-R2, Mean Square Error-MSE, Mean Absolute Error-MAE, and Mean Absolute Percentage Error-MAPE. The results, for the present case study, show that the R-squared statistic ranges from 0.85 to 0.99, the MSE is between 0.000046 and 0.000177, the MAE ranges from 0.002958 to 0.009336, and the value of MAPE varies from 3.50 to 9.60%. The predictive capability of GPR and GAPOLYFITN in determining flank wear are the best, as they exhibit high (R2), and lower values of MSE, MAE, and MAPE. The powerful predictive model of flank wear is the GPR because it provides R2 = 0.96, MSE = 4.6e-5, MAE = 0.002958, and MAPE = 3.50%.

Tool Wear Prediction When Machining with Self-Propelled Rotary Tools.

The performance of a self-propelled rotary carbide tool when cutting hardened steel is evaluated in this study. Although various models for evaluating tool wear in traditional (fixed) tools have been introduced and deployed, there have been no efforts in the existing literature to predict the progression of tool wear while employing self-propelled rotary tools. The work-tool geometric relationship and the empirical function are used to build a flank wear model for self-propelled rotary cutting tools. Cutting experiments are conducted on AISI 4340 steel, which has a hardness of 54–56 HRC, at various cutting speeds and feeds. The rate of tool wear is measured at various intervals of time. The constant in the proposed model is obtained using genetic programming. When experimental and predicted flank wear are examined, the established model is found to be competent in estimating the rate of rotary tool flank wear progression.

Wear behavior of tool flank in the side milling of Ti6Al4V: An analytical model and experimental validation

Due to the poor machinability of Ti6Al4V material, the cutting tool can easily suffer flank wear during the process of high-speed side milling, which reduces the tool life as well as the surface integrity of workpiece. Further, an effective method for predicting the flank wear of end mill during side milling of Ti6Al4V is lacking in the existing literature, which makes it difficult to improve the productivity of the overall process. To this end, in this study, a flank wear prediction model is constructed based on three main mechanisms: abrasive wear, adhesive wear, and diffusive wear. Subsequently, a normal stress model and temperature field model of wear land on the flank of end mill are established. Finally, these two models are incorporated in the flank wear model to obtain the variation rate of wear land width, which is regarded as a criterion to evaluate the reliability of the proposed flank wear prediction model of side mill. The prediction results are found to be in excellent agreement with the experimental results, which verifies the high prediction accuracy of the proposed model. Overall, this model can serve as a useful theoretical basis for the rational selection of tool geometry and cutting parameters.

Tool condition monitoring: unscented Kalman filter for tool flank wear estimation in turning of Inconel 718

This article presents flank wear estimation during the turning process of Inconel 718 in dry cutting condition using unscented Kalman filter (UKF). A discrete flank wear model is developed where two components of flank wear due to abrasion and diffusion are considered as state variables and the cutting force is taken as the output variable. The proposed model can be implemented for online tool wear monitoring and the UKF predicts the actual states of tool flank wear in real-time by measuring the cutting force variation. The simulation result of flank wear estimation using UKF is compared with the extended Kalman filter (EKF) under a similar cutting environment. The error of estimation for UKF is obtained less than that of EKF indicating better accuracy of UKF than EKF for tool condition monitoring of the proposed model. Hardware experiments are performed to validate simulated results of both UKF and EKF on the proposed model correlating the cutting force and flank wear components.

Prediction of cutting tool wear during a turning process using artificial intelligence techniques

In the manufacturing industry, cutting tool failure is a serious event which causes damage to the cutting tool and reduces the quality of the product, which increases the cost of production. A reliable, intelligent, tool wear monitoring system is required in the metal cutting manufacturing process to mitigate these negative effects. This study presents a model-based approach for tool wear monitoring based on an adaptive neuro-fuzzy inference system (ANFIS) for a cold-finished steel bar 1215 turning process. A three-input cutting force (Fx, Fy and Fz) and single-output (tool flank wear) model was designed and implemented using the ANFIS approach. The forces were measured using a piezoelectric dynamometer and data acquisition system. Flank wear was also monitored using a tool maker’s microscope. The model prediction results show that it is accurate enough to perform online monitoring of the turning process and can detect wear while operating.

Modeling and optimization of flank wear and surface roughness of Monel-400 during hot turning using artificial intelligence techniques

This work aims to model and investigate the effect of cutting speed, feed rate, depth of cut and the workpiece temperature on surface roughness and flank wear (responses) of Monel-400 during turning operation. It also aims to optimize the machining parameters of the above operation. A power-law model is developed for this purpose and is corroborated by comparing the results with the artificial neural network (ANN) model. Based on the coefficient of determination (R2), mean square error (MSE), and mean absolute percentage error (MAPE) the results of the power-law model are found to be in close agreement with that of ANN. Also, the proposed power law and ANN models for surface roughness and flank wear are in close agreement with the experiment results. For the power-law model R2, MSE, and MAPE were found to be 99.83%, 9.9×10-4, and 3.32×10-2, and that of ANN were found to be 99.91%, 5.4×10-4, and 5.96×10-2, respectively for surface roughness and flank wear. An error of 0.0642% (minimum) and 8.7346% (maximum) for surface roughness and 0.0261% (minimum) and 4.6073% (maximum) for flank wear were recorded between the observed and experimental results, respectively. In order to optimize the objective functions obtained from power-law models of the surface roughness and flank wear, GA (genetic algorithm) was used to determine the optimal values of the operating parameters and objective functions thereof. The optimal value of 2.1973 µm and 0.256 mm were found for surface roughness and flank wear, respectively.

Cutting Conditions Modeling and Optimization in Hard Turning Using RSM, ANN and Desirability Function

Mechanical manufacturing companies are required to produce parts with high quality, greater accuracy and high productivity to be competitive. For this purpose, the present work develops predictive models for arithmetic surface finish (Ra), flank wear (VB) and tangential force (Fz). The optimization was based on the desirability function (DF). The machining tests were carried out by hard turning the X210Cr12 hardened steel (56 HRC) using a coated ceramic tool (CC6050), according to the Taguchi L27 experimental plan. ANOVA was employed to determine the influence of cutting parameters (cutting speed—Vc, feed rate—f and machining time—t) on the output parameters (VB, Ra and Fz). Moreover, the RSM and the ANN methods were used to model the technological parameters. The DF approach was used to determine the optimal machining conditions minimizing simultaneously (VB, Ra and Fz). The results show that VB is mainly influenced by Vc (Cont.% = 39.96) followed by f (Cont.% = 35.36). In addition, it was indicated that f and t have been found as dominant factors affecting Ra with contributions of 31.71 and 23.78%, respectively. However, t and f are the main factors affecting Fz with contributions of 75.74 and 22.66%, respectively. On the other hand, ANN and RSM models correlate very well with experimental data. However, ANN approach shows better accuracy and the capability of predicting cutting process parameters than RSM. The optimum machining setting for multi-objective optimization corresponds to Vc= 80 m/min, f = 0.08 mm/rev and t = 4 min.

Modeling of flank wear progression for coated cubic boron nitride tool during hard turning of AISI H11steel

The hard turning process is carried out on a harder workpiece (hardness greater than 45 HRC) to achieve surface finish that is comparable with the grinding process. Surface finish of a workpiece is considerably influenced by the tool flank wear during hard turning process. Therefore, to achieve better surface finish, it is important to study flank wear progression and wear mechanisms that are associated with a particular tool and workpiece material. The aim of existing work is to develop a model for flank-wear rate by combining two wear mechanisms, namely, abrasive and adhesive. The developed model incorporates properties tool and workpiece material, cutting conditions, tool geometry and other process parameters in hard turning of AISI H11 steel using coated CBN tool and predicts the flank wear progression with cutting time. The proposed flank wear model was calibrated and experimentally validated in hard turning of AISI H11 steel (48-52 HRC).The predicted results of flank wear rate are found in reasonable agreement with the results of experiments.

Investigation on flank wear mechanism of CVD and PVD hard coatings in high speed dry turning of low and high carbon steel

High speed dry machining is the key manufacturing technology for higher productivity and for preserving green environment. To successfully implement this technology it is essential to understand the wear mechanism of the cutting tool engaged in high speed dry machining. Understanding of flank wear mechanism helps to design new coating material that improves the tool life. Among various types of wear taking place on cutting tool, flank wear is the most crucial one as it limits the service life of the tool. Therefore, to accomplish this goal two work materials, C20 and C80 steel were turned at 300m/min and 600m/min with CVD Al2O3/TiC, TiCN/TiC bilayer coated and PVD TiAlN single layer coated inserts and flank wear was studied. As the intention was to study only the wear mechanism of the top Al2O3, TiCN and TiAlN coatings, the machining was done for 15s only to avoid any exposure of the underlayer coating or substrate. It was found that, the flank wear mechanism substantially differed for different coatings. Plastic deformation induced necking and ridge formation, grain spallation induced ridge and groove formation, dissolution-diffusion and abrasion was found to be the responsible flank wear mechanism depending on the machining environments (cutting speed and work material) and coatings. A new flank wear model was presented to explain the groove and ridge formation on Al2O3 coating.

Modeling of Principal Flank Wear: An Empirical Approach Combining the Effect of Tool, Environment and Workpiece Hardness

Hard turning is increasingly employed in machining, lately, to replace time-consuming conventional turning followed by grinding process. An excessive amount of tool wear in hard turning is one of the main hurdles to be overcome. Many researchers have developed tool wear model, but most of them developed it for a particular work-tool-environment combination. No aggregate model is developed that can be used to predict the amount of principal flank wear for specific machining time. An empirical model of principal flank wear (VB) has been developed for the different hardness of workpiece (HRC40, HRC48 and HRC56) while turning by coated carbide insert with different configurations (SNMM and SNMG) under both dry and high pressure coolant conditions. Unlike other developed model, this model includes the use of dummy variables along with the base empirical equation to entail the effect of any changes in the input conditions on the response. The base empirical equation for principal flank wear is formulated adopting the Exponential Associate Function using the experimental results. The coefficient of dummy variable reflects the shifting of the response from one set of machining condition to another set of machining condition which is determined by simple linear regression. The independent cutting parameters (speed, rate, depth of cut) are kept constant while formulating and analyzing this model. The developed model is validated with different sets of machining responses in turning hardened medium carbon steel by coated carbide inserts. For any particular set, the model can be used to predict the amount of principal flank wear for specific machining time. Since the predicted results exhibit good resemblance with experimental data and the average percentage error is <10 %, this model can be used to predict the principal flank wear for stated conditions.

Analytical and Empirical Modeling of Wear and Forces of CBN Tool in Hard Turning - A Review

Machining of steel material having hardness above 45 HRC (Hardness–Rockwell C) is referred as a hard turning. There are numerous models which should be scrutinized and implemented to gain optimum performance of hard turning. Various models in hard turning by cubic boron nitride tool have been reviewed, in attempt to utilize appropriate empirical and analytical models. Validation of steady state flank and crater wear model, Usui’s wear model, forces due to oblique cutting theory, extended Lee and Shaffer’s force model, chip formation and progressive flank wear have been depicted in this review paper. Effort has been made to understand the relationship between tool wear and tool force based on the different cutting conditions and tool geometries so that appropriate model can be used according to user requirement in hard turning.

Mechanistic modeling of cutting forces and tool flank wear in the thermally enhanced turning of hardened steel

Thermally enhanced machining (TEM) is a method to improve the machinability of hard materials. In the literature, few investigations focus on the analytical modeling of machinability characteristics in the TEM. For this reason, this research tries to present theoretical models to better understanding of cutting forces and flank wear in the TEM process. The goal of this study is to present a methodology for predicting cutting forces and tool flank wear during thermally enhanced turning of hardened steel AISI 4140. A mechanistic model is developed to estimate the cutting forces under different TEM conditions by considering shear stresses in the primary and secondary shear zones as well as applied forces on the tool edge. A flank wear model is developed to estimate abrasion, adhesion, and diffusion wears by attention to some considerations such as the effect of TEM on the reduction of material hardness, thrust force, and the effect of multi-layer coated carbide tools on flank wear. Comprehensive experimental results are classified into two groups for calibration as well as verification of analytical models. By precise application of external concentrated heat source, the temperature in the shear plane and frictional area increases which finally leads to reduction of cutting forces. In other words, rising of uncut chip about 300 °C results in decreasing of shear stress in the frictional area about 200 MPa. Abrasive and adhesive were the dominant wear type in high levels of cutting speed and uncut chip temperature, respectively. The effect of cutting speed decreased in high value of uncut chip thickness due to the reduction of particle hardness in comparing with the hardness of tool coating.

Development of flank wear model of cutting tool by using adaptive feedback linear control system on machining AISI D2 steel and AISI 4340 steel

The purpose of this paper is to build an adaptive feedback linear control system to check the variation of cutting force signal to improve the tool life. The paper discusses the use of transfer function approach in improving the mathematical modelling and adaptively controlling the process dynamics of the turning operation. The experimental results shows to be in agreement with the simulation model and error obtained is less than 3%. The state space approach model used in this paper successfully check the adequacy of the control system through controllability and observability test matrix and can be transferred from one state to another by appropriate input control in a finite time. The proposed system can be implemented to other machining process under varying range of cutting conditions to improve the efficiency and observability of the system.

Modeling Flank Wear Progression Based on Cutting Force and Energy Prediction in Turning Process

Flank wear of cutting tools is often used as the tool life criterion because it has high impact on the diametric accuracy of turning. Accurate prediction of tool wear is important for optimizing tool change and other cutting parameters. In this work, a tool wear model is proposed based on the prediction of the cutting force and the energy consumption in turning process. Based on the cutting force prediction using a validated mechanistic force model, the energy consumption in turning can be estimated. A tool flank wear model is developed using the prediction of the cutting energy consumption and considering the impact of the cutting speed. By using the distribution of the cutting force on tool edge and the energy consumption for each revolution of the workpiece, the wear volume distribution on the tool edge for each revolution is predicted. The flank wear (VB) is further calculated using the tool geometry information. The model prediction of flank wear is validated by using tool wear data published in literature. The comparison shows that the model prediction agrees well with the experimental results.

Optimization of Tool Wear Using Coupled RSM-GA Approach in Turning of Stainless Steel AISI 304 with Magnetic Damping of Tool Shank

Tool wear, especially flank wear, is a major concern in the manufacturing industry. Increased tool wear is caused by chatter and leads to increased surface roughness, reduced productivity and higher operating costs. It is more pronounced in the machining of difficult to cut materials such as stainless steel, tool steel, Inconel and hardened Ti alloys. Additionally, unpredictable tool wear can lead to frequent shutdowns of the machining process making it difficult for full automation. Therefore, to increase productivity and to reduce costs associated with increased and unpredictable tool wear, numerous research studies have been carried out. In this research, two permanent ferrite bar magnets of 1500 Gauss strength were used to dampen the vibration of the tool shank in the turning of stainless steel AISI 304 using titanium nitride (TiN) coated carbide inserts. Mild steel fixtures were used to place the magnets beside and below the tool shank in the carraige of a Harrison M390 engine lathe. The tool overhang was kept constant at 120 mm. A small central composite design (CCD) approach in response surface methodology (RSM) was used to model the tool wear as a response of the three primary cutting parameters: cutting speed, feed, and depth of cut. Design Expert software (version 6) was used to generate the 14 experimental runs needed to develop and verify the empirical mathematical model of tool flank wear. The resultant tool flank wear was measured using both optical and scanning electron microscopes (SEM). Finally, an empirical quadratic mathematical model of tool wear was found. This model was then used as the objective function in the optimization of tool wear using genetic algorithms (GA). The optimization results predicted that the minimum tool wear was 0.0427 mm. This prediction was subsequently validated experimentally.

Modelling and Optimization of Tool Wear in Machining of EN24 Steel Using Taguchi Approach

This paper presents the effects of process parameters like cutting speed, feed rate and different cooling conditions (i.e. dry, wet and liquid nitrogen used as a coolant) on tool wear (crater and flank wear) in machining of EN24 alloy steel using uncoated tungsten carbide insert tool. Experiments were carried out as per Taguchi’s L9 orthogonal array and further statistical analysis of variation (ANOVA) was used to study the effect of process parameters on responses i.e. crater wear and flank wear, based on their P value and F-value at 95 % confidence level. Overall, cryogenic cooling provides improved tool life leading to extended product life and performance. Mathematical models for crater and flank wear are found to be statistically significant.

Wear characteristics and wear control method of PVD-coated carbide tool in turning Inconel 718

As nickel-based alloy Inconel 718 has poor thermal conductivity and serious work hardening, it is classified as a difficult-to-cut material. The tool wear is very serious. In this paper, PVD-coated carbide tools were employed to cut Inconel 718. Firstly, tool wear morphology and tool wear mechanism were studied. The results show that tool wear characteristic changed with cutting speeds. There was an optimum cutting speed at which oxides serving as boundary lubrication layer formed. These oxides can reduce the friction coefficient of the tool-chip contact surface and inhibit tool wear. Secondly, according to the wear mechanism analysis, tool flank wear model was established in the light of wear delamination theory. The optimum cutting temperature was proposed, and it was calculated using the established model. At the optimum cutting temperature, tool wear can reach the minimum value. At last, verification experiments for the optimum cutting temperature were carried out. For a pair of tool-workpiece, the minimal tool wear can be obtained by optimizing cutting conditions so as to make the cutting temperature reach the optimum value. This provides a good method for tool wear control. It also provides a theoretical basis and effective means for a reasonable choice of cutting parameters.

Experimental investigation into machinability of hardened AISI 4140 steel using TiN coated ceramic tool

The concerns with some machinability aspects on surface roughness, flank wear and chip morphology in hard turning of AISI 4140 steel using PVD-TiN coated Al2O3+TiCN mixed ceramic inserts under dry environment. The machined surface characterization, tool wear mechanism and chip morphology are investigated in this study, along with optimization and development of mathematical models for surface roughness and flank wear. By adopting combined techniques such as orthogonal array and analysis of variance, the consequences of cutting parameters (cutting speed, feed and depth of cut) on surface roughness (Ra, Rq and Rz) and flank wear (VB) are explored. The results show that feed is the principal cutting parameter influencing surface roughness, followed by cutting speed. However, flank wear is affected by the cutting speed and interaction of feed-depth of cut, although depth of cut has not been found statistical significant, but the flank wear is an increasing function of depth of cut. Thereafter, observations are made on the machined surface, worn tool and the generated chips by scanning electron microscope (SEM) to establish the process. Abrasion was the major wear mechanism found during hard turning within the studied range. Chip morphology indicates the formation saw-tooth/serrated chips at higher feed due to reduction of chip thickness, results in degradation of surface finish. Additionally, effect of tool wear on surface roughness has been studied. The experimental data were further analyzed to predict the optimal range of surface roughness and flank wear. Finally, based on response surface methodology (RSM) mathematical models are developed for surface roughness (Ra, Rq and Rz) and flank wear (VB) with 95% confidence level. Effectiveness, adequacy, statistical significance and validity, and fit of data of the developed model has been checked using ANOVA analysis (depending on P value, F value and R2 value), Anderson–Darling test and normal probability plot. The proposed experimental and statistical techniques initiate authentic methodologies to optimize, model and improve the hard turning process, which can be prolonged effectively to analyse other machining processes.

Application of response surface methodology on investigating flank wear in machining hardened steel using PVD TiN coated mixed ceramic insert

Article history: Received February 8 2013 Received in revised format July 7 2013 Accepted July 8 2013 Available online July 8 2013 The paper presents the development of flank wear model in turning hardened EN 24 steel with PVD TiN coated mixed ceramic insert under dry environment. The paper also investigates the effect of process parameter on flank wear (VBc). The experiments have been conducted using three level full factorial design techniques. The machinability model has been developed in terms of cutting speed (v), feed (f) and machining time (t) as input variable using response surface methodology. The adequacy of model has been checked using correlation coefficients. As the determination coefficient, R (98%) is higher for the model developed; the better is the response model fits the actual data. In addition, residuals of the normal probability plot lie reasonably close to a straight line showing that the terms mentioned in the model are statistically significant. The predicted flank wear has been found to lie close to the experimental value. This indicates that the developed model can be effectively used to predict the flank wear in the hard turning. Abrasion and diffusion has been found to be the dominant wear mechanism in machining hardened steel from SEM micrographs at highest parametric range. Machining time has been found to be the most significant parameter on flank wear followed by cutting speed and feed as observed from main effect plot and ANOVA study. © 2013 Growing Science Ltd. All rights reserved