Tool Wear Model Research Articles

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.

Experimental Investigation to Predict Tool Wear and Vibration Displacement in Turning – A Base for Tool Condition Monitoring

AbstractIn the present work investigation primarily focuses on identifying the presence of cutting tool vibrations during face turning process. For this purpose an online non-contact vibration transducer i. e. laser Doppler Vibrometer is used as part of a novel approach. The revisions in the values of cutting forces, vibrations and acoustic optic emission signals with cutting tool wear are recorded and analyzed. This paper presents a mathematical model in an attempt to understand tool lifeunder vibratory cutting conditions. Tool wear and cutting force data are collected in the dry machiningof AISI 1040 steel at different vibrationinduced test conditions. Identifying the correlation among tool wear, cutting forces and displacement due to vibration is a critical task in the present study. These results are used to predict the evolution of displacement and tool wear in the experiment. Specifically, the research tasks include: to provide an appropriate experimental data to prove the mathematical model of tool wear based on the influence of cutting tool vibrations in turning.The modeling is focused on demonstrating the scientific relationship between the process variables such as vibration displacement, vibration amplitude, feedrate, depth of cut and spindle speed while getting into account machine dynamics effect and the effects such as surface roughness and tool wear generated in the operation. Present work also concentrates on the improvement in machinability during vibration assisted turning with different cutting tools. The effect of work piece displacement due to vibration on the tool wear is critically analyzed. Finally, tool wear is established on the basis of the maximum displacement that can be tolerated in a process for an effective tool condition monitoring system.

Effect of cutting parameters on workpiece and tool properties during drilling of Ti-6Al-4V

Abstract The main aim of machining is to provide the dimensional preciseness together with surface and geometric quality of the workpiece to be manufactured within the desired limits. Today, it is quite hard to drill widely utilized Ti-6Al-4 V alloys owing to their superior features. Therefore, in this study, the effects of temperature, chip formation, thrust forces, surface roughness, burr heights, hole diameter deviations and tool wears on the drilling of Ti-6Al-4 V were investigated under dry cutting conditions with different cutting speeds and feed rates by using tungsten carbide (WC) and high speed steel (HSS) drills. Moreover, the mathematical modeling of thrust force, surface roughness, burr height and tool wear were formed using Matlab. It was found that the feed rate, cutting speed and type of drill have a major effect on the thrust forces, surface roughness, burr heights, hole diameter deviations and tool wears. Optimum results in the Ti-6Al-4 V alloy drilling process were obtained using the WC drill.

Parameter Inference Under Uncertainty in End-Milling γ′-Strengthened Difficult-to-Machine Alloy

Nickel-based alloys are those of materials that are maintaining their strength at high temperature. This feature makes these alloys a suitable candidate for power generation industry. However, high wear rate and tooling cost are known as the challenges in machining Ni-based alloys. The high wear rate causes a rapid failure of the tool, and therefore, fewer data will be available for model development. In addition, variations in material properties and hardness, residual stress, tool runout, and tolerances are some uncontrollable effects adding uncertainties to the currently developed models. To address these challenges, a probabilistic Bayesian approach using Markov Chain Monte Carlo (MCMC) method has been used in this work. The MCMC method is a powerful tool for parameter inference and quantification of embedded uncertainties of models. It is shown that by adding a prior probability to the observation probability, fewer experiments are required for inference. This is specifically useful in model development for difficult-to-machine alloys where high wear rate lowers the cardinality of the dataset. The combined Gibbs–Metropolis algorithm as a subset of MCMC method has been used in this work to quantify the uncertainty of the unknown parameters in a mechanistic tool wear model for end-milling of a difficult-to-machine Ni-based alloy. Maximum of 18% error and average error of 11% in the results show a good potential of this modeling in prediction of parameters in the presence of uncertainties when limited experiments are available.

Wear characteristics of cemented carbide tool in dry-machining Ti-6Al-4V

ABSTRACTIn machining titanium alloys, due to the low thermal conductivity and high chemical activity of titanium alloys, tool wear is serious and processing efficiency is very low. To avoid the effects of impurities, which were brought by the cutting fluid, the uncoated cemented carbide tool (WC-Co), which was suitable for cutting titanium alloys, was used for the experiments of dry-turning titanium alloy Ti-6Al-4V. A scanning electron microscopy (SEM) equipped with energy-dispersive X-ray spectrometer (EDS) was used to analyze tool wear mechanism. Based on analyzing the friction characteristic of tool–chip interface, tool wear mechanism was also studied and a physical evolution model of tool wear was established. The results showed that there existed serious adhesion, diffusion and oxidation at tool–chip interface and increasing cutting speed accelerated their occurrence. The physical evolution of tool wear behavior can reflect the loss process of tool material very well.

Hybrid ABC Optimized MARS-Based Modeling of the Milling Tool Wear from Milling Run Experimental Data.

Milling cutters are important cutting tools used in milling machines to perform milling operations, which are prone to wear and subsequent failure. In this paper, a practical new hybrid model to predict the milling tool wear in a regular cut, as well as entry cut and exit cut, of a milling tool is proposed. The model was based on the optimization tool termed artificial bee colony (ABC) in combination with multivariate adaptive regression splines (MARS) technique. This optimization mechanism involved the parameter setting in the MARS training procedure, which significantly influences the regression accuracy. Therefore, an ABC–MARS-based model was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc. Regression with optimal hyperparameters was performed and a determination coefficient of 0.94 was obtained. The ABC–MARS-based model's goodness of fit to experimental data confirmed the good performance of this model. This new model also allowed us to ascertain the most influential parameters on the milling tool flank wear with a view to proposing milling machine's improvements. Finally, conclusions of this study are exposed.

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.

切削参数对新型光学铝级金刚石车削刀具磨损的影响

Critical components in optical devices and optical measuring systems are mainly produced through the ultra-high precision machining. Aluminium alloys have been proven to be advantageous and very commonly used in the photonics industry. This ever-increasing use and demand in optics have led to the development of newly modified grades of aluminium alloys produced by rapid solidification in the foundry process. The newer grades are characterised by the finer microstructures and can improve mechanical and physical properties. Their main inconvenience currently lies in their having a very limited machining database as there is not enough adequate research contributions on their performance in terms of machinability when diamond turned. This paper investigates the machinability of rapidly solidified aluminium RSA 905 by varying diamond cutting parameters and measuring the diamond tool wear over a cutting distance of 4 km. The machining parameters varied were cutting speed, feed rate and depth of cut. The results show that the cutting speed is the most influential parameter on the diamond tool wear. The highest tool wear of 12.2 μm was achieved at the spindle speed of 500 rpm, the feed rate of 25 mm/min and the depth of cut of 15 μm. The lowest tool wear of 2.45 μm was recorded at the spindle of 1750 rpm speed, the feed rate of 5 mm/min and the depth of cut of 5 μm. Generally, a combination of higher cutting speeds, lower feed rates and smaller depths of cut caused less diamond tool wear. Statistical analysis was performed to develop a model for the diamond tool wear. Wear maps were generated from the model to identify zones where the cutting parameters produced the least wear. The results prove the rapidly solidified aluminium is a superior alternative to traditional aluminium alloys and can also provide a reference for machinists using this material.

Research on the Tool Wear Mechanism of Cemented Carbide Ball End Mill Machining Titanium Alloy

In order to achieve the high efficiency machining of titanium, the cutting force model is verified through the cutting experimental platform in machining cant and curved surface with ball end milling. And then the influence of cutting parameters and surface curvature on cutting force and tool wear are investigated. Finally, the prediction model of tool wear is established based on the orthogonal test and the least square method. This study proposes that the tool wear and each tooth feeding have a major impact on cutting force and that the convex surface from a small curvature to larger and the concave surface from a large curvature to smaller can effectively improve the life of tool in machining curved surface.

Computer system for identification of tool wear model in hot forging

The aim of the research was to create a methodology that will enable effective and reliable prediction of the tool wear. The idea of the hybrid model, which accounts for various mechanisms of tool material deterioration, is proposed in the paper. The mechanisms, which were considered, include abrasive wear, adhesive wear, thermal fatigue, mechanical fatigue, oxidation and plastic deformation. Individual models of various complexity were used for separate phenomena and strategy of combination of these models in one hybrid system was developed to account for the synergy of various mechanisms. The complex hybrid model was built on the basis of these individual models for various wear mechanisms. The individual models expanded from phenomenological ones for abrasive wear to multi-scale methods for modelling micro cracks initiation and propagation utilizing virtual representations of granular microstructures. The latter have been intensively developed recently and they form potentially a powerful tool that allows modelling of thermal and mechanical fatigue, accounting explicitly for the tool material microstructure.

Simulation of Tool Wear in Prestressed Cutting Superalloys

In high speed machining superalloys processes, tool wear is strongly influenced by the cutting temperature and contact stresses. Finite element analysis of machining can be used as a supplementary to the physical experiment, this paper provides investigations in 2D and 3D finite element modeling and simulation of prestressed cutting for GH4169 superalloy, a tool wear model for the specified tool and workpiece pair is developed based on the Usui's wear model, furthermore, tool temperature, wear rate and nodal displacement on the face of tool in prestressed cutting of superalloy is analyzed under various prestress condition and cutting parameters, and Python language is adopted to modify the Abaqus code used to allow tool wear calculation and tool geometry updating. The results of the simulation indicate that the tool wear rate increases with the increase of cutting time, and the influence of the prestress to tool wear in prestressed cutting process of shaft part is unremarkable.

Modeling and Optimization of Tool Wear in a Passively Damped Boring process using Response Surface Methodology

Regenerative chatter in machining process is a root cause for tool wear and poor surface finish resulting in low production rate. Hence research has to be focused to identify an opportunity to suppress these chatters. This investigation is attempted to reduce the tool wear in boring process by incorporation of shape memory alloy (SMA) mass dampers in boring tool. The copper and Zinc based SMA mass dampers were placed at various position of boring bar and the effect of damper position and machining parameters on the tool wear was studied. Boring process was carried out on grey cast iron pipe by varying the damper position (54, 64 and 74 mm), cutting velocity (25, 50 and 75 m/min) and depth of cut (0.2, 0.4 and 0.6 mm). The prediction model was developed to correlate the relationship between machining parameters and tool wear. The optimum damper position and machining parameters to minimize the tool wear was determined using response surface methodology (RSM). The confirmation test on the optimized value has witnessed efficient prediction of the damper position and machining parameters for reduced tool wear in boring process.

Analysis of Tool Wear while Milling Hybrid Metal Matrix Composites

In the present work, an attempt has been made to analyze the factors influencing tool wear while milling Al/Al2O3/Gr particulate composites. Materials used for the present investigation are Al 6061-aluminium alloy reinforced with alumina (Al2O3) of size 45 microns and graphite (Gr) of an average size 60 microns, which are produced by stir casting route. Central composite design (CCD) was employed in developing the tool wear model in relation to machining parameters such as feed rate, cutting speed, depth of cut and weight fraction of Alumina. From the Analysis of variance (ANOVA), it is found that feed is the dominant parameter for tool wear whereas weight fraction of alumina shows minimal effect on tool wear compared to other parameters. From the Scanning Electron Microscope (SEM), the Al2O3 and Gr particles get adhered to the tool surface owing to the high pressure generated at the tool-workpiece interface.

The influences of tool wear on Ti6Al4V cutting temperature and burn defect

Owing to the low thermal conductivity and elasticity, cutting burn defect is easy to produce during machining of titanium alloy Ti6Al4V. Firstly, the relationship between the tool wear rate and cutting burn was studied. Nine sets of experiments with different cutting parameters were conducted to obtain the tool wear morphology after different cutting lengths. The tool flank wear and cutting force were measured and collected. Accordingly, the tool wear model formula was built based on the multiple linear regression analysis using the least-squares method. Then, the tool worn model was established according to the experimental tool wear morphology and used in the Deform 3D FEM software to simulate the cutting burn temperature. At last, the cutting burn temperature and its corresponding tool wear were obtained. The results show that the biggest factors that influence the wear rate were cutting speed, feed rate, cutting width, and cutting depth in order, and the cutting burn defect could occur as long as the cutting temperature reached to 1000 °C and the tool flank wear was larger than 0.6 mm.

Carbide tool wear mechanisms in laser-assisted machining of metal matrix composites

Laser-assisted machining (LAM) is an emerging manufacturing process for improving the productivity when machining hard and high-strength materials. Recently, it has been interesting using the process to machine metal matrix composites (MMCs), and published reports indicate that machinability is improved. But there is still ambiguity about whether the new process is beneficial for tool life. The specific wear mechanisms for carbide tools in LAM remain unknown. This work clearly evaluates the tool life and wear mechanism for uncoated and coated tungsten carbide tools in LAM of Al/SiCp/45 % composites at different cutting conditions. It was concluded that abrasive wear was the most predominant wear mechanisms in LAM, controlling the deterioration and final failure of the carbide tools. The adhesion wear and diffusion wear were accelerated to some extent with an increase in the temperature. However, all of the three cutting tools in LAM have a longer tool life over conventional machining at given conditions. Based on the results from tool wear process in LAM, a physical model of tool wear in LAM has been developed. The paper aims at providing an insight into the tool wear characteristics and showing the great potential in the field of LAM of MMCs.

A New Approach to Spatial Tool Wear Analysis and Monitoring

The tool wear of cutting tools has a very strong impact on the product quality as well as on the efficiency of the machining processes. Despite the current high automation level in the machining industry, a few key issues prevent complete automation of the entire turning process. One of these issues is tool wear, which is usually measured off the machine tool. Therefore, its in-line characterization is crucial. This paper presents an innovative, robust and reliable direct measurment procedure for measuring spatial cutting tool wear in-line, using a laser profile sensor. This technique allows for the determination of 3D wear profiles, which is an advantage over the currently used 2D subjective techniques (microscopes, etc.). The use of the proposed measurement system removes the need for manual inspection and minimizes the time used for wear measurement. In this paper, the system is experimentally tested on a case study, with further in-depth analyses of spatial cutting tool wear performed. In addition to tool wear measurements, tool wear modelling and tool life characterization are also performed. Based on this, a new tool life criterion is proposed, which includes the spatial characteristics of the measured tool wear. The results of this work show that novel tool wear and tool life diagnostics yield an objective and robust methodology allowing tool wear progression to be tracked, without interruptions in the machining process or in the performance of the machining process. This work shows that such an automation of tool wear diagnostics, on a machine tool, can positively influence the productivity and quality of the machining process.

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.

Tool wear simulation of complex shaped coated cutting tools

In this paper FE-based tool wear simulation has been applied to complex shaped and coated cutting tools in turning operations. The coating has been considered by an own functional layer with specific friction and thermal properties as well as wear resistance. Therefore, a modified Usui tool wear model is calibrated for the coating and the substrate and combined in a three-dimensional simulation in order to simulate the wearing process including the baring of the substrate with a change in local thermal, friction and wear properties. Turning of AISI 1045 with PVD-TiAlN-coated and uncoated carbide tools was considered. The simulation results are validated by a comparison of simulated wear and external longitudinal turning experiments for a wide field of cutting parameters.

Modelling and prediction of tool wear using LS-SVM in milling operation

This article focuses on the least squares support vector machine (LS-SVM), which can solve highly nonlinear and noisy black-box modelling problems, and tool wear model based on LS-SVM for ball-end milling cutter is established by considering the joint effect of machining conditions. In the established model, machining parameters and position parameter of ball-end cutter are considered as input and the output of the proposed model is tool wear of cutting edge position. The experimental measured tool wear is used to train the established model, and the interconnection relationship between input and output parameters is determined after training. The analysis and comparison of predicted performance are given by taking different tuning parameters and data regularisation. Some interesting analysis results are deduced from the established LS-SVM-based tool wear model. In order to further show the effectiveness of LS-SVM-based tool wear model, the verified comparison between LS-SVM-based and ANN-based model is given. Finally, the discussion of interactional effect of machining parameters on tool wear estimation is used to evaluate prediction performance of LS-SVM-based model. The verification shows that the LS-SVM-based tool wear model is suitable to predict tool wear at certain range of cutting conditions in milling operation.

Tool wear modelling for constant removal rate in two-bodies automated polishing

Complex shapes such as medical implants or injection moulds require the use of super-finishing operations to minimise geometrical defects, down to mirror effect finish. These pre-polishing and polishing operations are still regularly performed manually by skilled workers. In spite of advantages in terms of repeatability, productivity and geometrical quality, automatic polishing methods are not widely used because they require systematic and significant developments. One of the main issues is the modification of the abrasive tool efficiency during the process. It evolves over time due to abrasive grains tearing and transfers onto the abrasive tool surface of workpiece microchips. Thus a model of tool wear is proposed and compensation strategies are elaborated to ensure a constant material removal rate on the surface. Compensations are performed by optimising the spindle speed and/or the feedrate along the tool path and are validated through experimental investigations.