Tool flank wear prediction in CNC turning of 7075 AL alloy SiC composite

Flank wear occurs on the relief face of the tool and the life of a tool used in a machining process depends upon the amount of flank wear; so predicting of flank wear is an important requirement for higher productivity and product quality. In the present work, the effects of feed, depth of cut and cutting speed on flank wear of tungsten carbide and polycrystalline diamond (PCD) inserts in CNC turning of 7075 AL alloy with 10 wt% SiC composite are studied; also artificial neural network (ANN) and co-active neuro fuzzy inference system (CANFIS) are used to predict the flank wear of tungsten carbide and PCD inserts. The feed, depth of cut and cutting speed are selected as the input variables and artificial neural network and co-active neuro fuzzy inference system model are designed with two output variables. The comparison between the results of the presented models shows that the artificial neural network with the average relative prediction error of 1.03% for flank wear values of tungsten carbide inserts and 1.7% for flank wear values of PCD inserts is more accurate and can be utilized effectively for the prediction of flank wear in CNC turning of 7075 AL alloy SiC composite. It is also found that the tungsten carbide insert flank wear can be predicted with less error than PCD flank wear insert using ANN. With Regard to the effect of the cutting parameters on the flank wear, it is found that the increase of the feed, depth of cut and cutting speed increases the flank wear. Also the feed and depth of cut are the most effective parameters on the flank wear and the cutting speed has lesser effect.     

Role of temperature and surface finish in predicting tool wear using neural network and design of experiments

The present work focuses on the two of the techniques, namely design of experiments and the neural network for predicting tool wear. In the present work, flank wear, surface finish and cutting zone temperature were taken as response (output) variables measured during turning and cutting speed, feed and depth of cut were taken as input parameters. Predictions for all the three response variables were obtained with the help of empirical relation between different responses and input variables using design of experiments (DOE) and also through neural network (NN) program. Predicted values of the responses by both techniques, i.e. DOE and NN were compared with the experimental values and their closeness with the experimental values was determined. Relationship between the surface roughness and the flank wear and also between the temperature and the flank wear were found out for indirect measurement of the flank wear through surface roughness and cutting zone temperature.     

Modeling of tool wear in drilling by statistical analysis and artificial neural network

The useful life of a cutting tool and its operating conditions largely control the economics of the machining operations. Hence, it is imperative that the condition of the cutting tool, particularly some indication as to when it requires changing, to be monitored. The drilling operation is frequently used as a preliminary step for many operations like boring, reaming and tapping, however, the operation itself is complex and demanding.

Back propagation neural networks were used for detection of drill wear. The neural network consisted of three layers input, hidden and output. Drill size, feed, spindle speed, torque, machining time and thrust force are given as inputs to the ANN and the flank wear was estimated. Drilling experiments with 8 mm drill size were performed by changing the cutting speed and feed at two different levels. The number of neurons in the hidden layer were selected from 1, 2, 3, …, 20. The learning rate was selected as 0.01 and no smoothing factor was used. The estimated values of tool wear were obtained by statistical analysis and by various neural network structures. Comparative analysis has been done between statistical analysis, neural network structures and the actual values of tool wear obtained by experimentation.     

Prediction of flank wear by using back propagation neural network modeling when cutting hardened H-13 steel with chamfered and honed CBN tools

Productivity and quality in the finish turning of hardened steels can be improved by utilizing predicted performance of the cutting tools. This paper combines predictive machining approach with neural network modeling of tool flank wear in order to estimate performance of chamfered and honed Cubic Boron Nitride (CBN) tools for a variety of cutting conditions. Experimental work has been performed in orthogonal cutting of hardened H-13 type tool steel using CBN tools. At the selected cutting conditions the forces have been measured using a piezoelectric dynamometer and data acquisition system. Simultaneously flank wear at the cutting edge has been monitored by using a tool makers microscope. The experimental force and wear data were utilized to train the developed simulation environment based on back propagation neural network modeling. A trained neural network system was used in predicting flank wear for various different cutting conditions. The developed prediction system was found to be capable of accurate tool wear classification for the range it had been trained.     

Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks

In machining of parts, surface quality is one of the most specified customer requirements. Major indication of surface quality on machined parts is surface roughness. Finish hard turning using Cubic Boron Nitride (CBN) tools allows manufacturers to simplify their processes and still achieve the desired surface roughness. There are various machining parameters have an effect on the surface roughness, but those effects have not been adequately quantified. In order for manufacturers to maximize their gains from utilizing finish hard turning, accurate predictive models for surface roughness and tool wear must be constructed. This paper utilizes neural network modeling to predict surface roughness and tool flank wear over the machining time for variety of cutting conditions in finish hard turning. Regression models are also developed in order to capture process specific parameters. A set of sparse experimental data for finish turning of hardened AISI 52100 steel obtained from literature and the experimental data obtained from performed experiments in finish turning of hardened AISI H-13 steel have been utilized. The data sets from measured surface roughness and tool flank wear were employed to train the neural network models. Trained neural network models were used in predicting surface roughness and tool flank wear for other cutting conditions. A comparison of neural network models with regression models is also carried out. Predictive neural network models are found to be capable of better predictions for surface roughness and tool flank wear within the range that they had been trained.Predictive neural network modeling is also extended to predict tool wear and surface roughness patterns seen in finish hard turning processes. Decrease in the feed rate resulted in better surface roughness but slightly faster tool wear development, and increasing cutting speed resulted in significant increase in tool wear development but resulted in better surface roughness. Increase in the workpiece hardness resulted in better surface roughness but higher tool wear. Overall, CBN inserts with honed edge geometry performed better both in terms of surface roughness and tool wear development.     

Gradual wear monitoring of turning inserts using wavelet analysis of ultrasound waves

Discrete wavelet transforms of ultrasound waves is used to measure the gradual wear of carbide inserts during turning operations. Ultrasound waves, propagating at a nominal frequency of 10 MHz, were pulsed into the cutting tools towards the cutting edge at a burst frequency of 10 KHz. The reflected waves off the mark, nose and flank surfaces were digitized at a sampling rate of 100 MHz. Daubechies Quadrature Mirror Filter pair was used to decompose ultrasound signals into frequency packets using a tree structure.Normalized signals in each level of decomposition were used to search for a neural network architecture that correlates the ultrasound measurements to the wear level on the tool. A three-layer Multi-Layer Perceptron architecture yielded the best correlation (95.9%) using the wave packets from the fourth level of decomposition with frequencies 3.75–4.375 and 5.625–6.875 MHz.     

Evaluation of wear of turning carbide inserts using neural networks

Recent trends, being towards mostly unmanned automated machining systems and consistent system operations, need reliable on-line monitoring processes. A proper on-line cutting tool condition monitoring system is essential for deciding when to change the tool. Many methods have been attempted in this connection.Recently, artificial neural networks have been tried for this purpose because of its inherent simplicity and reasonably quick data-processing capability. The present work uses the back propagation algorithm for training the neural network of 5-3-1 structure. The technique shows close matching of estimation of average flank wear and directly measured wear value. Thus the system developed demonstrates the possibility of successful tool wear monitoring on-line.     

An active method of monitoring tool wear states by impact diagnostic excitation

This paper presents an active method of monitoring tool wear states by using impact diagnostic excitation in the machining process. Because the dynamic characteristics of tool vibration in machining process will change with the tool wear development, the damping ratio, which is one of the important dynamic characteristics of tool vibration, will be used for monitoring the tool wear states in machining. In order to obtain the damping ratio, impact diagnostic excitation was applied to the tool in the feed direction and the signals of the tool vibration were measured for some flank wears under different cutting conditions. The signals were analyzed through FFT analyzer and computer, and then the damping ratio of the tool vibration in the feed direction was calculated. The experimental results have shown that the damping ratio measured by impact excitation increases linearly with tool wear development and the increment of the damping ratio is different for each cutting condition, but the damping ratio can be uniquely determined through the flank worn area. To explain the reason for increase with tool wear development, the damping mechanism on the flank worn land was also discussed. The results of the discussions and numerous cutting experiments have indicated that the presented active method could be used for effectively monitoring the tool wear states in machining.     

Tool wear measurement in turning using force ratio

The aim of this work was to develop a reliable method to predict flank wear during the turning process. The present work developed a mathematical model for on-line monitoring of tool wear in a turning process. Force signals are highly sensitive carriers of information about the machining process and, hence, they are the best alternatives for monitoring tool wear. In the present work, determination of tool wear has been achieved by using force signals. The relationship between flank wear and the ratio of force components was established on the basis of data obtained from a series of experiments. Measurement of the ratio between the feed force and the cutting force components (F_f/F_c) has been found to provide a practical method for an in-process approach to the quantification of tool wear. A series of experiments was conducted to study the effects of tool wear as well as other cutting parameters on the cutting force signals, and to establish a relationship between the force signals, tool wear and other cutting parameters. The flank wear and the ratio of forces at different working conditions were collected experimentally to develop a mathematical model for predicting flank wear. The model was verified by comparing the experimental values with the predicted values. The relationship was then used for determination of tool flank wear.     

Modelling the correlation between cutting and process parameters in high-speed machining of Inconel 718 alloy using an artificial neural network

An artificial neural network (ANN) model was developed for the analysis and prediction of the relationship between cutting and process parameters during high-speed turning of nickel-based, Inconel 718, alloy. The input parameters of the ANN model are the cutting parameters: speed, feed rate, depth of cut, cutting time, and coolant pressure. The output parameters of the model are seven process parameters measured during the machining trials, namely tangential force (cutting force, F_z), axial force (feed force, F_x), spindle motor power consumption, machined surface roughness, average flank wear (VB), maximum flank wear (VB_max) and nose wear (VC). The model consists of a three-layered feedforward backpropagation neural network. The network is trained with pairs of inputs/outputs datasets generated when machining Inconel 718 alloy with triple (TiCN/Al₂O₃/TiN) PVD-coated carbide (K 10) inserts with ISO designation CNMG 120412. A very good performance of the neural network, in terms of agreement with experimental data, was achieved. The model can be used for the analysis and prediction of the complex relationship between cutting conditions and the process parameters in metal-cutting operations and for the optimisation of the cutting process for efficient and economic production.     

A computer algorithm for flank and crater wear estimation in CNC turning operations

In order to increase the productivity of turning processes, several attempts have been made in the recent past for tool wear estimation and classification in turning operations. The tool flank and crater wear can be predicted by a number of models including statistical, pattern recognition, quantitative and neural network models. In this paper, a computer algorithm of new quantitative models for flank and crater wear estimation is presented. First, a quantitative model based on a correlation between increases in feed and radial forces and the average width of flank wear is developed. Then another model which relates acoustic emission (AE_rms) in the turning operation with the flank and crater wear developed on the tool is presented. The flank wear estimated by the first model is then employed in the second model to predict the crater wear on the tool insert. The influence of flank and crater wear on AE_rms generated during the turning operation has also been investigated. Additionally, chip-flow direction and tool–chip rake face interfacing area are also examined. The experimental results indicate that the computer program developed, based on the algorithm mentioned above, has a high accuracy for estimation of tool flank wear.     

Tool wear when turning hardened AISI 4340 with coated PCBN tools using finishing cutting conditions

Tool wear is one of the most important aspects in metal cutting, especially when machining hardened steels. The present work shows the results of tool wear, cutting force and surface finish obtained from the turning operation on hardened AISI 4340 using PCBN coated and uncoated edges. Three different coatings were tested using finishing conditions: TiAlN, TiAlN-nanocoating and AlCrN. The lowest tool wear happened with TiAlN-nanocoating followed by TiAlN, AlCrN and uncoated PCBN. Forces followed the same pattern, increasing in the same order, after flank wear appears. At the beginning of cutting, there was no significant difference amongst the coated tools, only the uncoated one showing higher cutting force. Ra values were between 0.7 and 1.2 μm with no large differences amongst the tools. Finite element method (FEM) simulations indicated that temperature at the chip–tool interface was around 800 °C in absence of flank wear, independently of coating. In that range only the TiAlN coating oxidize since AlCrN needs higher than 1000 °C. Therefore, due to a combination of high hardness in the cutting temperature range and the presence of an oxidizing layer, TiAlN-nanocoating performed better in terms of tool wear and surface roughness.     

Experimental and numerical studies on the performance of alloyed carbide tool in dry milling of aerospace material

Titanium alloy is widely used in the aerospace industry for applications requiring high strength at elevated temperature and high mechanical resistance. The difficulty of dislocation motion through the microstructure is responsible for its high yield strength. However, the main problems encountered when machining titanium alloy are the low material removal rate and the short tool life.This study investigated the suitability of uncoated cemented carbide tools in ball-end milling of the aerospace titanium alloy Ti-6242S. The experiments were carried out under dry cutting condition. Cutting speeds in the range of 60–150 m/min were considered. The axial and radial depths of cut were kept constant at 2.0 and 8.8 mm, respectively, and the feed rate values of 0.1 and 0.15 mm/tooth were selected. SEM analysis has been carried out on the worn tools and shows that flank wear and excessive chipping on the flank edge are the main tool failure modes. For both feed rates, the results demonstrate that the higher the cutting speed the better is the surface finish. The FEM simulation provides good results on modelling of chip formation and can be helpful to calculate the contact parameters and to understand the tool wear mechanisms when dry machining aerospace titanium alloys.     

On temperatures and tool wear in machining hypereutectic Al–Si alloys with vortex-tube cooling

This study investigates dry machining of hypereutectic silicon–aluminum alloys assisted with vortex-tube (VT) cooling. The objective is to reduce cutting temperatures and tool wear by enhanced heat dissipation through the chilled air generated by a VT. A machining experiment, cutting mechanics analysis, and temperature simulations are employed to (1) model the heat transfer of a cutting tool system with VT cooling applied, (2) explore effects of cooling setting and machining parameters on the cooling efficiency, and (3) evaluate VT cooling effects on tool wear. A390 alloy is machined by tungsten carbides with cutting forces and geometry measured for heat source characterizations as the input of temperature modeling and simulations. VT cooling is approximated as an impinging air jet to estimate the heat convection coefficient that is incorporated into the heat transfer models. The major findings include: (1) VT cooling may reduce tool wear in A390 machining depending upon machining conditions, and the outlet temperature is more critical than the flow rate, (2) cooling effects on temperature reductions, up to 20 °C, decrease with the increase of the cutting speed and feed, and (3) tool temperature decreasing by VT cooling shows no direct correlations with tool wear reductions.     

The relation between chip morphology and tool wear in ultra-precision raster milling

In the field of ultra-precision machining, the study of the relation between chip morphology and tool wear is significant, since tool wear characteristics can be reflected by morphologies of cutting chips. In this research, the relation between chip morphology and tool flank wear is first investigated in UPRM. A cutting experiment was performed to explore chip morphologies under different widths of flank wear land. A geometric model was developed to identify the width of flank wear land based on chip morphology. Theoretical and experimental results reveal that the occurrence of tool flank wear can make the cutting chips truncated at both their cut-in and cut-out sides, and reduce the length of cutting chips in the feed direction. The width of truncation positions of the cutting chip can be measured and used to calculate the width of flank wear land with the help of the mathematical model. The present research is potentially used to detect tool wear and evaluate machined surface quality in intermittent cutting process.     

Real time implementation of on-line tool condition monitoring in turning

This paper describes a real-time tool condition monitoring system for turning operations. The system uses a combination of static and dynamic neural networks with off-line and on-line training and cutting force components are used as diagnostic signals. The system is capable of monitoring several wear components simultaneously. The wear estimation system has been implemented experimentally to evaluate its suitability for use in shop floor conditions. The tests were performed in real time with different cutting conditions. The experimental results showed that the system was successful in predicting three wear components in real time. However, the accuracy of the wear prediction was not the same for all three wear components. The crater wear predictions were less accurate partly because of the opposing effects of crater and flank wear components on cutting force components.     

The assessment of cutting tool wear

Flank wear of cutting tools is often selected as the tool life criterion because it determines the diametric accuracy of machining, its stability and reliability. This paper argues that the existing criteria of flank wear are insufficient for its proper characterization. Their existence is due to the lack of knowledge on the contact conditions at the tool flank–workpiece interface. Known attempts to evaluate the physical processes at this interface do not help to resolve this issue. This paper compares different characteristics of the evaluation of flank wear. The contact process at the mentioned interface is analyzed through the experimental assessment of the contact stresses, and the full validity of Makarow’s law is confirmed, i.e. minimum tool wear occurs at the optimum cutting speed. A new concept of tool resources is proposed and discussed. This resource is defined as the limiting amount of energy that can be transmitted through the cutting wedge until it fails.     

Prediction of surface roughness profiles for milled surfaces using an artificial neural network and fractal geometry approach

Artificial neural networks (ANNs) models were developed for the analysis and prediction of the relationship between the cutting conditions and the corresponding fractal parameters of machined surfaces in face milling operation. These models can help manufacturers to determine the appropriate cutting conditions, in order to achieve specific surface roughness profile geometry, and hence achieve the desired tribological performance (e.g. friction and wear) between the contacting surfaces. The input parameters of the “ANNs” models are the cutting parameters: rotational speed, feed, depth of cut, pre-tool flank wear and vibration level. The output parameters of the model are the corresponding calculated fractal parameters: fractal dimension “D” and vertical scaling parameter “G”. The model consists of three-layered feed-forward back-propagation neural network. ANNs models were utilized successfully for modeling and predicting the fractal parameters “D” and “G” in face milling operations. Moreover, W–M fractal function was integrated with the developed ANNs models in order to generate an artificially fractal predicted profiles at different cutting conditions. The predicted profiles were found statistically similar to the actual measured profiles of test specimens.     

Cutting edge rounding: An innovative tool wear criterion in drilling CFRP composite laminates

An evenly and smoothly distributed abrasion wear, observed along the entire cutting edge of an uncoated carbide drill bit in drilling CFRPs, is due to the highly abrasive nature of the carbon fibres. A very few researchers have only quoted this wear mode as being responsible for giving rise to the rounding of the cutting edge, or its bluntness. However, this wear feature has seldom been investigated, unlike the conventional flank wear in practice. This paper offers a new approach in unveiling and introducing the cutting edge rounding (CER) – a latent wear characteristic as a measure of sharpness/bluntness – of uncoated cemented carbide tools during drilling CFRP composite laminates. Four different types of drills (conventional and specialised) were tested to assess the applicability and relevance of this new wear feature. Mechanical loads (drilling thrust and torque) were recorded, and the hole entry and exit delamination were quantified. For the utilised tools, the accruing magnitude of CER was also recorded, in parallel with studying their conventional flank wear. Very appreciable correlations between the CER and the drilling loads, and also the quantitative delamination results are observed. Results reveal that this new wear type develops almost similarly for the selected tools and is practically independent of their respective conventional flank wear patterns. Moreover, a distinct, non-zero magnitude of the CER for a very fresh tool state may provide researchers with some lucid information in further studying the results during wear tests, more emphatically. The CER correlations with quantitative delamination results are noticed quite comparable to those of the conventional flank wear via statistical linear regression analyses.     

Assessment of some factors influencing tool wear on the machining of glass fibre-reinforced plastics by coated cemented carbide tools

Glass fibre-reinforced plastics (GFRP) composite materials are used in many different engineering fields. The need for machining of GFRP composites has not been eliminated fully. The tool wear reduction is an important aspect during machining. In the present work, an attempt has been made to assess the factors influencing tool wear on the machining of GFRP composites. Experimental design concept has been used for experimentation. The machining experiments are carried out on lathe using two levels of factors. The factors considered are cutting speed, fibre orientation angle, depth of cut and feed rate. A procedure has been developed to assess and optimize the chosen factors to attain minimum tool wear by incorporating (i) response table and effect graph; (ii) normal probability plot; (iii) interaction graphs; (iv) analysis of variance (ANOVA) technique. The results indicated that cutting speed is a factor, which has greater influence on tool flank wear, followed by feed rate. Also the determined optimal conditions really reduce the tool flank wear on the machining of GFRP composites within the ranges of parameters studied.