Tool Wear Characteristics Research Articles

Physics-Based Simulations of Chip Flow over Micro-Textured Cutting Tool in Orthogonal Cutting of Alloy Steel

Physics-based process simulations have the potential to allow virtual process design and the development of digital twins for smart machining applications. This paper presents 3D cutting simulations using the finite element method (FEM) and investigates the physical state variables that are fundamental to the reduction in cutting forces, friction, and tool wear when micro-textured cutting tools are employed. For this goal, textured cemented carbide cutting tool inserts are designed, fabricated, and tested in the orthogonal dry cutting of a nickel-chromium-molybdenum alloy steel. Cutting forces and friction coefficients are compared against the non-textured tool, revealing the effects of texture parameters. Chip flow over the textured tool surface and process variables at the chip-tool contact are investigated and compared. The results reveal the fundamental sources of such improvements. Archard’s wear rate as a composition of process variables is utilized to compare experimental and simulated wear on the textured cutting tools. The effects of texture and cutting conditions on tool wear and adhesion characteristics are further discussed on the simulation results with experimental comparisons. It was found that the results obtained from these simulations provide further fundamental insights about the micro-textured cutting tools.

Investigation of the Wear Performance of TiB2 Coated Cutting Tools during the Machining of Ti6Al4V Alloy.

The machining of Ti6Al4V alloy, especially at low cutting speeds, is associated with strong Built-Up Edge (BUE) formation. The PVD coatings applied on cutting tools to machine such materials must have the necessary combination of properties to address such an underlying wear mechanism. The present work investigates and shows that TiB2 PVD coating can be designed to have certain mechanical properties and tribological characteristics that improve machining in cases where BUE formation is observed. Three TiB2 coatings were studied: one low hardness coating and two high hardness coatings with varied coating thicknesses. Wear performances for the various TiB2 coated carbide tools were evaluated while rough turning Ti6Al4V. Tool wear characteristics were evaluated using tool life studies and the 3D wear volume measurements of the worn surface. Chip morphology analyses were done to assess the in-situ tribological performance of the coatings. The micro-mechanical properties of the coatings were also studied in detail to co-relate with the coatings’ performances. The results obtained show that during the rough turning of Ti6Al4V alloy with intensive BUE formation, the harder TiB2 coatings performed worse, with coating delamination on the rake surface under operation, whereas the softer version of the coating exhibited significantly better tool life without significant coating failure.

Optimization and wear characteristics of TiAlN coated carbide when machining hardened high thermal conductivity (HTCS-150) die steel.

High Thermal Conductivity Steel-150 (HTC-150) is a specific steel designed for use in the hot stamping process as a stamping die. HTCS-150 die steel was difficult to machine due to its high strength, hardened state, and high thermal conductivity characteristics, which necessitated parameter control for a fine surface finish and maximum tool life. The characteristics of tool wear when machining HTCS-150 hardened steel (52 HRC) with a ball nose end mill TiAlN coated carbide insert is presented in this study. Cutting speed, feed rate, and axial depth of cut have all been varied in machining trials. Response surface methodology experimental design was used to create a parametric optimisation model. The results indicate that the model develops an accurate prediction, with comparisons between measured and expected results suggesting that the model operates within the 90% prediction interval with an error of less than 10%. The lowest tool wear was achieved at 130 m min−1 cutting speed, 0.4 mm/tooth feed rate, and 0.1 mm axial depth of cut, according to the optimisation results. The most influenced cutting parameters were found to be feed rate and depth of cut, followed by cutting speed. The wear surface texture analysis revealed coating delamination, adhesion, built-up edge formation, and tool edge chipping. The findings of this experimental study can be used to machine the HTCS-150 for the longest possible tool life while maintaining a fine surface finish.

Influence of machining conditions on tool wear and surface characteristics in hot turning of AISI630 steel

Hot machining as one of the clean production techniques is used to improve the machinability of hard-to-cut materials. In this technique, an external heat source is used to soften the workpiece material and improves its machinability and sustainability by reducing the machining forces and eliminating cutting fluid application. In the present work, the conventional and hot turning of precipitation-hardened AISI630 stainless steel was evaluated in both numerical and experimental methods. Turning experiments were carried out using PVD-(Ti, Al)N/(Al, Cr)2O3-coated carbide tools, and the flank wear was measured by using scanning electron microscopy (SEM). Numerical analysis was carried out by finite elements method (FEM) to calculate the cutting tool temperature, cutting force values, and cutting force fluctuation. The numerical results showed that the hot turning not only decreases the cutting force but also decreases the amplitude of cutting force fluctuations up to 47%. The experimental results showed that hot turning at 300 °C reduces the tool’s flank wear and machined surface roughness up to 33% and 23%, respectively. Therefore, hot machining could be one of the realistic alternatives that can rise the machining processes on a sustainable level.

Tool wear characteristics in rough turning of Inconel 718 with coated carbide tool under conventional and high-pressure coolant supplies

The machining of nickel-based superalloys is often challenging due to their excellent physical properties. High-temperature gradients are generated at the cutting zone, which accelerates the tool wear and impairs its performance. However, introducing high-pressure coolant (HPC) lubrication has been shown to improve both the tool life and surface integrity. In this investigation, attention has been mainly focused on the tool wear characteristics in the rough turning of Inconel 718. The longitudinal turning experiments have been conducted under conventional and HPC lubrication with two cutting speeds. Coated carbide inserts have been analyzed using several techniques including scanning electron microscopy, energy-dispersive X-ray spectroscopy, and 3D scanning. The experimental procedure has embraced the tool life, the tool wear, the generated forces, and the wear mechanisms. The examination of the worn tool surfaces demonstrates that although the depth of cut notching is more pronounced, it is possible to reduce the flank wear by using HPC lubrication. The results also highlight a marginal reduction in cutting and axial forces, a good chip fragmentation, and a significant improvement of the tool life. Furthermore, the investigations show that the wear mechanisms are strongly influenced using HPC under the various investigated cases.

On the grinding performance of metal-bonded aggregated cBN grinding wheels based on open-pore structures

Metal-bonded aggregated cubic boron nitride (cBN) grinding wheels were developed by employing open-pore structures in abrasive layers, aiming to solve the problems, such as severe tool wear, low machining efficiency, and poor quality stability with conventional abrasive grinding wheels. Comparative grinding performances, including grinding forces and force ratio, grinding temperature, specific grinding energy, ground surface roughness were performed between vitrified monocrystalline cBN wheels and porous aggregated cBN wheels. Characterisation of morphologies, such as pore structures and tool wear features were also conducted. Results show that the as-sintered porous aggregated cBN wheels possess the lower grinding forces (reduced by 35.9%–43.3% for Fn′; 3.9%–18.8% for Ft′), a smaller grinding temperature (decreased by 10.4%–71.8%), and a stable grinding force ratio of 2.5 in comparison of vitrified monocrystalline cBN wheels. In addition, the ground surface roughness of porous aggregated cBN wheels ranges from 0.76 to 1.19 μm, whilst the other one possesses over 1.65 μm. Furthermore, the worse ground surface quality is obtained, resulting from the smaller chip storage space and poor self-sharpening ability of vitrified monocrystalline cBN wheels compare to porous aggregated cBN wheels.

Cutting performance and wear mechanism of honeycomb ceramic tools in interrupted cutting of nickel-based superalloys

Nickel-based superalloys are favored because of high fatigue strength, corrosion resistance and oxidation resistance. However, the characteristics of high hardness, poor heat dissipation and sticking make the machinability of nickel-based superalloys poor, which is more serious for interrupted processing. Therefore, honeycomb ceramic tools are utilized to turn GH4169. Cutting and impact force, wear patterns and mechanism of honeycomb ceramic tools are analyzed. The results demonstrate that the impact force is greater than the average cutting force. Fractures, chipping and adhesion are the main wear characteristics of honeycomb ceramic tools. Furthermore, adhesive, abrasive and diffusion wear are the main wear mechanisms.

Investigation of the Wear Behavior of PVD Coated Carbide Tools during Ti6Al4V Machining with Intensive Built Up Edge Formation

Tool wear phenomena during the machining of titanium alloys are very complex. Severe adhesive interaction at the tool chip interface, especially at low cutting speeds, leads to intensive Built Up Edge (BUE) formation. Additionally, a high cutting temperature causes rapid wear in the carbide inserts due to the low thermal conductivity of titanium alloys. The current research studies the effect of AlTiN and CrN PVD coatings deposited on cutting tools during the rough turning of a Ti6Al4V alloy with severe BUE formation. Tool wear characteristics were evaluated in detail using a Scanning Electron Microscope (SEM) and volumetric wear measurements. Chip morphology analysis was conducted to assess the in situ tribological performance of the coatings. A high temperature–heavy load tribometer that mimics machining conditions was used to analyze the frictional behavior of the coatings. The micromechanical properties of the coatings were also investigated to gain a better understanding of the coating performance. It was demonstrated that the CrN coating possess unique micromechanical properties and tribological adaptive characteristics that minimize BUE formation and significantly improve tool performance during the machining of the Ti6Al4V alloy.

A robust condition monitoring methodology for grinding wheel wear identification using Hilbert Huang transform

Grinding is a finishing operation performed to obtain the desired finish on the component. Wheel wear is one of the primary constraints in achieving the desired productivity in grinding. A new methodology is proposed for accurate and timely identification of wheel wear in cylindrical grinding using Hilbert Huang transform and support vector machine. During the grinding of EN31 carbon steel, the condition of the wheel and its wear was monitored with an accelerometer and power cell. Both vibration and power signals captured were used to identify the condition of the wheel and its wear. An exhaustive feature set is generated in the frequency and the time-frequency domain. Hilbert Huang transform, an adaptive time-frequency analysis technique, was used to extract the features of tool wear in the time-frequency domain. The first three IMF constituents were further chosen for feature extraction of statistical parameters based on their mean energy. Random forests algorithm was used to identify the relevant features. The methodology was validated with several grinding experiments and, is found to give an accuracy of 100% with both low and high cutting depths. The results indicated the robust and reliable wheel wear detection in cylindrical grinding with the use of relatively cheap sensors like accelerometers. The proposed method can be widely used in many applications in the industry where grinding is predominantly used as the finishing operation.

Study of tool wear characteristics while machining mono and graphite reinforced hybrid zinc - aluminium-based MMCs

Study of tool wear characteristics while machining mono and graphite reinforced hybrid zinc - aluminium-based MMCs

Prediction of tool wear in CFRP drilling based on neural network with multicharacteristics and multisignal sources

Carbon fiber-reinforced polymer (CFRP) drilling is a typical process in the aircraft industry. Because the components of CFRP are different and uneven, it is difficult to extract tool wear characteristics from the machining signals, which are composed of the processing characteristics of various materials and the tool state characteristics. The aim of this work is to present a new comprehensive approach based on multicharacteristics and multisignal sources to predict the tool wear state during CFRP drilling through a combination of a backpropagation (BP) artificial neural network (ANN) model and an efficient automatic system depending on the sliding window algorithm. It was verified that the peak factor and Kurtosis coefficient of different signals and the energy value of the d5 layer of the thrust force signal and the d3 layer of the vibration signal after wavelet decomposition were related to tool wear. Among them, the energy value of the d3 layer of the vibration signal was selected as the wear indicator and was able to describe the state of the tool during the CFRP drilling process regardless of the drilling conditions and individual tool differences. A confirmatory drilling experiment using 6-mm-diameter polycrystalline diamond twist drilling under different processing parameters was conducted to verify the ANN model based on multicharacteristics and multisignal sources. A lower feed speed and a higher cutting speed were both highly correlated with the VB value of flank wear. Drill wear accelerated because of the occurrence of adhesive wear when the number of drilled holes reached around 90. The accuracy of the neural network model is 80–87% when using the value of only one characteristic but clearly increases based on multicharacteristics and multisignal sources in real time, indicating that the BP ANN model has higher accuracy in predicting the tool state in CFRP drilling through the sensor signal fusion method.

Study of tool wear characteristics while machining mono and graphite reinforced hybrid zinc - aluminium-based MMCs

The effect of graphite addition to conventional composites and making it hybrid for better machinability is the motto behind this work. Here, ZA43+SiC mono composite and ZA43+SiC+graphite hybrid composite are machined under dry condition. Machining is executed using commercially available coated carbide SNMG 090308 tool. Cutting variables viz. machining speed, feed rate and depth of machining are varied and for each machining trial fresh cutting edge is used. Tool damage is measured while machining both the categories of materials and are compared. It is noticed that the hybrid composite causes less damage in comparison with mono composite. The reason may be due to powdered graphite present in the composite which is responsible for reduction in the friction. Graphite being solid lubricant prevents tools getting abraded by the hard workpiece and promotes easy machinability. Statistical analysis done on the results gives the details about most influencing variable to the machining process. The tool wear during composite machining is greatly influenced by machining speed and feed. Optimum machining parameters can be drawn considering material composition for less tool wear condition.

Evaluation of Tool Wear Characteristics during Machining Ni-Based Super-alloy (Inconel 625) Under Different Lubricating Conditions

Evaluation of Tool Wear Characteristics during Machining Ni-Based Super-alloy (Inconel 625) Under Different Lubricating Conditions

Size effect and micro endmilling performance while sustainable machining on Inconel 718

ABSTRACT Micro endmilling is emerged as a potential micromachining technique in many industries like biomedical, aerospace, optics, electronics and marine, etc. due to the huge demand for micro components with precision. However, relatively higher tool wear and poor surface characteristics are limiting its applicability in many areas. Therefore, the need has been raised to improve the machining characteristics of micro endmilling sustainably. The size effect and micro endmilling performance such as wear behavior, cutting force, and microhardness under dry and sunflower oil based MQL environments while machining on Inconel 718 was studied in this work. Results showed that the sunflower oil based MQL environments have the potential to reduce the size effect region and tool wear significantly. Workpiece adhesion and abrasion on the cutting edge were identified as the main underlying wear mechanism for both conditions. The edge radius, tool flank wear, and cutting forces were found to be reduced by 25%, 30%, and 36% respectively with the application of MQL compared to dry conditions. In addition, an increase of 7% in microhardness with relatively smooth surfaces as well as reduced micro cracks, prows, and voids on the machined surfaces were observed with the application of MQL.

Micro-milling tool wear monitoring under variable cutting parameters and runout using fast cutting force coefficient identification method

Extracting discriminative tool wear features is of great importance for tool wear monitoring in micro-milling. However, due to the dependency on tool runout and cutting parameters, the traditional tool wear features are incompetent to monitor the tool wear condition in micro-milling with significant tool runout and varied cutting parameter interactions. In this study, micro-milling cutting force is represented by a parametric model including variable cutting parameters, tool runout, and tool wear. The cutting force coefficient in the model, which is not only discriminative to the tool wear condition but also independent to the tool runout and cutting parameters, is extracted as the micro-milling tool wear feature. To reduce the computation cost, a fast neural network–based method is proposed to identify the tool runout and the cutting force coefficient from the cutting force signal. Experimental results show that the proposed cutting force coefficient–based approach is efficient to monitor the micro-milling tool wear under varied cutting parameters and tool runout.

Machining condition-based stochastic modeling of cutting tool’s life

One of the major problems in the application of machining processes is the cutting tool life estimation. In this regard, different studies with various assumptions have been conducted to analyze tool wear characteristics under various cutting conditions to achieve different objectives. Traditional models for the analysis of tool life are mostly based on deterministic approaches, and the variations in cutting conditions are overlooked, and the tool life is not precisely matched with predicted values by these methods. In recent years, researchers have considered using the stochastic approach in forecasting tool life. Among them, Weibull distribution has special significance. One problem in using these approaches is the accurate estimation of tool’s life distribution functions based on the empirical information. In other words, although many researchers have considered Weibull an appropriate distribution for the cutting tool life modeling, however, the estimation of its parameters has certain inherent complexities. In this research, a hybrid methodology is presented to determine the parameters of the tool life distribution, by using the design of experiment (DOE) based on Box-Behnken design (BBD), total time on the test (TTT) transform, and golden section search (GSS). The estimation method of Weibull distribution parameters in this paper is compared with well-known techniques such as the least square method and maximum likelihood estimation. Finally, the proposed methodology was implemented in a case study, and the results were reported. The values of R2 for shape and scale parameters are 92.52% and 96.80%, respectively, which confirm the adequacy of the proposed methodology in the practical applications.

Discrimination and Prediction of Tool Wear State Based on Gray Theory

Abstract In allusion to the problems that random noise causes that interfere with the extraction of wear characteristics from cutting vibration signals and cause low accuracy of lathe tool wear state discrimination and prediction, a new method was proposed in this study. The proposed method extracted the wear time-domain characteristics of tool vibration signals through wavelet packet transform and the correlation coefficient method. Next, noises in wear time-domain characteristics were reduced by singular-value decomposition. The gray proximity correlation between the characteristic data series corresponding to the initial cutting and current cutting was calculated and used to represent the characteristic of tool wearing and discriminate the wear state. The gray decision-making model of metabolism GM(1,1) was established based on the wear characteristic series and was used to predict the variation trend of tool wear state, thus deciding the necessity of cutting tool changing. Cutting wear experiments and wear state predictions were carried out on the ZCK20 numerical control lathe (Tuoman, Zhejiang, China) by using three pieces of WNMG080408-TM T9125 lathe tools (Tungaloy Corporation, Iwaki, Japan). Experimental results demonstrated that the proposed method could eliminate noise effectively, acquire the optimal wear characteristics of tools, and discriminate and predict the wear state of tools accurately.

Tool wear in laser ultrasonically combined turning of tungsten carbide with PCD tools

Tungsten carbides are typical difficult-to-cut materials due to their high hardness and brittleness, which cause severe tool wear and shorten tool life when being machined during conventional processing methods, leading to a loss of quality and accuracy of the machined parts. In this paper, a novel laser ultrasonically assisted turning technology for tungsten carbides was proposed, and the primary objective of this study was to investigate the behavior of polycrystalline diamond (PCD) tools when turning tungsten carbides with the application of ultrasonic vibration to tools and laser-induced heating to workpieces. To determine suitable parameters, thermal modeling of laser ultrasonically assisted turning of tungsten carbide was employed. Through contrast experiments carried out during conventional turning, ultrasonically elliptical vibration turning, laser-assisted turning, and laser ultrasonically combined turning of tungsten carbides with PCD tools, the tool wear characteristics and dominant wear mechanisms were investigated, and the relation between tool wear and surface quality of workpiece was determined by analyzing the effects of tool wear on the surface roughness values and the microtopography of the machined surface. A significant reduction in the cutting force and a substantial improvement in the tool life were obtained in laser ultrasonically combined turning of tungsten carbides with PCD tools when compared with conventional turning, ultrasonically elliptical vibration turning, and laser-assisted turning; therefore, lower surface roughness values and a good surface quality of the workpiece were obtained. During laser ultrasonically combined turning of tungsten carbides with PCD tools, the wear on the rake face was characterized by micro tipping and chipping, the flank face had shallow micro-grooves oriented along the cutting direction, and the dominant failure mechanisms of the PCD tools were the synergistic interaction of diffusion, oxidation, tipping, and chipping. With the increase in tool wear, the surface roughness values steadily increased in laser ultrasonically combined turning, and the cutting mode was changed from ductile mode turning to brittle mode turning, which resulted in many defects, including pits, voids, grooves, etc., on the machined surfaces.

Measurement and analysis of the geometric characteristics of microholes and tool wear for varying tool-workpiece gaps in electrochemical discharge drilling

In the last decade, the Electrochemical Discharge Drilling (ECDD) has emerged as an alternative to conventional laser ablation to create blind and through-holes in non-conductive materials such as fused silica, quartz, and glass workpiece. The initial tool electrode and workpiece (T-W) gap play a vital role in microhole formation and tool wear. Therefore, an experimental investigation into the effect of the initial T-W gap with different machining time and electrolyte concentrations on the blind microhole formation in the glass and tool wear in the velocity-feed ECDD is reported. The microholes having superior surface quality and lower overcut were fabricated with KOH electrolyte and lower concentration. A novel numerical model based on inverse heat flux reduction for varying initial T-W gaps was studied for the first time. The obtained experimental results were compared to the numerical results predicted with the finite element based numerical simulations. The opening size of the microholes increased up to a critical T-W gap and then decreased with a further increase in the gap. The critical gap is found to be 20 µm and 10 µm for NaOH and KOH electrolyte, respectively. The depth of the microholes was the maximum at a zero-gap condition; however, the tool wear was severe. The highest average depth of 175 µm was obtained at 30 wt% NaOH for the machining time of 60 s. Tool wear tends to be decreasing with increasing the T-W gap. The least average tool wear of 9 µm was observed for 20 wt% KOH electrolyte for machining time of 60 s. The experimental results indicated that the conventionally used gravity-feed mechanism is not suitable from the tool wear point-of-view, and a particular initial T-W gap should be maintained during the ECDD process. The T-W gap between 10 and 30 µm for the NaOH electrolyte and 5–20 µm for the KOH electrolyte is recommended.

Experimental investigations on cryo-machining of Hastelloy C-276 with tool wear characteristics

The super alloy exhibits great strength and fatigue behaviour when nickel (Ni) is present in major quantities. Moreover, it possesses good corrosive resistant behaviour at high temperatures. However, these alloys are very difficult to machine under normal machining conditions due to their great strength and low heat dissolution. In this work, machining was performed on Hastelloy C276 under various machining conditions (speed, feed and depth) and environments (dry and cryogenic). Liquid nitrogen was used as a coolant in the machining region. The machinability of Hastelloy C276 was investigated with machining forces, temperature, surface roughness and hardness under different cutting conditions. Turning experiments that resulted from passing LN2 drastically reduced temperature by up to 40%. Machining forces were minimal under cryogenic machining due to its effective lubrication property. Surface finish of the machined area improved by about 26% under cryogenic conditions. Both dry and cryogenic machining improved the hardness of the work material. The high cooling efficiency of LN2 improved hardness of the machined surface was about 8-15%. Chip width and side-flow of chip material were reduced under cryogenic cooling. Moreover, adhesion and abrasion wear were observed minimally in cryogenic machining compared to dry machining. But no significant difference was observed in notch wear for both types of machining. Machinability of Hastelloy C276 significantly improved when LN2 used as a cutting fluid.