Tool Wear Level Research Articles

Detection process approach of tool wear in high speed milling

The cutting tool wear degrades the quality of the product in the manufacturing process, for this reason an on-line monitoring of the cutting tool wear level is very necessary to prevent any deterioration. Unfortunately there is no direct manner to measure the cutting tool wear on-line. Consequently we must adopt an indirect method where wear will be estimated from the measurement of one or more physical parameters appearing during the machining process such as the cutting force, the vibrations, or the acoustic emission, etc. The main objective of this work is to establish a relationship between the acquired signals variation and the tool wear in high speed milling process; so an experimental setup was carried out using a horizontal high speed milling machine. Thus, the cutting forces were measured by means of a dynamometer whereas; the tool wear was measured in an off-line manner using a binocular microscope. Furthermore, we analysed cutting force signatures during milling operation throughout the tool life. This analysis was based on both temporal and frequential signal processing techniques in order to extract the relevant indicators of cutting tool state. Our results have shown that the variation of the variance and the first harmonic amplitudes were linked to the flank wear evolution. These parameters show the best behavior of the tool wear state while providing relevant information of this later.

Tool condition classification using Hidden Markov Model based on fractal analysis of machined surface textures

The texture of a machined surface generated by a cutting tool, with geometrically well-defined cutting edges, carries essential information regarding the extent of tool wear. There is a strong relationship between the degree of wear of the cutting tool and the geometry imparted by the tool on to the workpiece surface. The monitoring of a tool’s condition in production environments can easily be accomplished by analyzing the surface texture and how it is altered by a cutting edge experiencing progressive wear and micro-fractures. This paper discusses our work which involves fractal analysis of the texture of surfaces that have been subjected to machining operations. Two characteristics of the texture, high directionality and self-affinity, are dealt with by extracting the fractal features from images of surfaces machined with tools with different levels of tool wear. The Hidden Markov Model is used to classify the various states of tool wear. In this paper, we show that fractal features are closely related to tool condition and HMM-based analysis provides reliable means of tool condition prediction.

An analysis of the residual stresses generated in Inconel 718™ when turning

Inconel 718 is one of a family of nickel based superalloys that are used extensively by the aerospace industry in the hot sections of gas turbine engines. The literature detailing the effects of varying operating parameters on tool life when machining nickel based superalloys is comprehensive, however, relatively little of this data refers to their effects on machined workpiece surface integrity and residual stress generation. Greater knowledge of the effects of operating parameters on surface integrity is critical to the acceptance of new cutting tool materials, tool geometries and strategies. The paper initially reviews prior work on the surface integrity achieved when turning Inconel 718. Following on from this a series of experiments evaluating the effects of varying cutting tool material, geometry, wear level and operating parameters are detailed. The results show that the largest influence on surface integrity was tool wear. Cutting with a worn tool resulted in greater microstructural deformation, microhardness changes and high surface tensile stresses. High tensile stresses were also formed in the surface layer when cutting with a coated tool, while cutting with an uncoated tungsten carbide insert at the same operating parameters produced deep compressive stresses beneath a reduced tensile layer.

Statistical properties of acoustic emission signals from metal cutting processes

Acoustic emission (AE) data from single point turning machining are analyzed in this paper in order to gain a greater insight of the signal statistical properties for tool condition monitoring applications. A statistical analysis of the time series data amplitude and root mean square (rms) value at various tool wear levels are performed, finding that aging features can be revealed in all cases from the observed experimental histograms. In particular, AE data amplitudes are shown to be distributed with a power-law behavior above a crossover value. An analytic model for the rms values probability density function is obtained resorting to the Jaynes’ maximum entropy principle; novel technique of constraining the modeling function under few fractional moments, instead of a greater amount of ordinary moments, leads to well-tailored functions for experimental histograms.

Monitoring of flank wear of coated tools in high speed machining with a neural network ART2

A monitoring system for classifying the levels of the tool flank wear of coated tools into some categories has been developed using an unsupervised and self-organizing artificial neural network, ART2. The input pattern used for the ART2 was an array of normalized mean wavelet coefficients of the feed force, which was affected by not only the flank wear but also the severe crater wear observed in high speed machining. The outputs of ART2 were classified into four or five categories of wear levels: the incipient stage, one or two intermediate stages, final stage and hazardous stage. For two apparently different series of input data obtained under the same cutting conditions, which are often experienced in the experiment, the ART2 neural network showed very similar classification of tool wear levels from the beginning to the end of cutting. Further study proved that this monitoring system detected the excessive wear in the hazardous stage for different cutting speeds 5–7 m/s and different feed rates 0.10–0.20 mm/rev.

Connectivity oriented fast Hough transform for tool wear monitoring

Tool wear monitoring can be achieved by analyzing the texture of machined surfaces. In this paper, we present the new connectivity oriented fast Hough transform, which easily detects all line segments in binary edge images of textures of machined surfaces. The features extracted from line segments are found to be highly correlated to the level of tool wear. A multilayer perceptron neural network is applied to estimate the flank wear in various machining processes. Our experiments show that this Hough transform based approach is effective in analyzing the quality of machined surfaces and could be used to monitor tool wear. A performance analysis of our Hough transform is also provided.

A statistical analysis of acoustic emission signals for tool condition monitoring (TCM)

The statistical properties of acoustic emission signals for tool condition monitoring (TCM) applications in mechanical lathe machining are analyzed in this paper. Time series data and root mean square (RMS) values at various tool wear levels are shown to exhibit features that can be put into relation with aging in both cases. In particular, the histograms of raw data show power-law distributions above a cross-over value, in which newer cutting tools exhibit more numerous larger events compared with more worn-out ones. For practical purposes, statistics based on RMS values are more feasible, and the analysis of these also reveals discriminating age-related features. The assumption that experimental RMS histograms follow a Beta (β) distribution has also been tested. The residuals of the modeling β functions indicate that the search for a more appropriate fitting function for the experimental distribution is desirable.

Work material and the effectiveness of coated tools

Earlier studies have suggested through the use of wear maps, that significant benefits of coated tools (in terms of lower tool wear rates) may only be realized when the appropriate machining conditions (cutting speed and speed rate) are chosen. This paper investigates the effects of work material on the wear improvement of coated tools by comparing uncoated and titanium carbide (TiC)-coated cemented carbide tools when cutting two medium steel grades: AISI 1045; and AISI 4340. Wear maps constructed for these tool-work combinations show that the TiC coating is more effective when machining the softer plain carbon 1045 grade, decreasing tool wear rates by half an order of magnitude or more over a wide range of cutting conditions. More modest reductions are achieved with the harder 4340 alloy. Nonetheless, with both steel grades, the TiC coating still significantly expands the range of machining conditions under which acceptable levels of tool wear are obtained.

Dynamic management of cutting tools for flexible and quality machining

Traditional economic tool-life models assume a homogeneous cutting environment, where a tool's continued service is irrespective of its condition. It is wellknown that the quality of a machining process is significantly impacted by a tool's wear-and-tear. To ensure good machining quality, tool assignment should consider the wear level of the tool as well as the type of machining job to be performed. This paper presents a dynamic management model for cutting tools that emphasizes the cost of machining quality. The model describes a heterogeneous environment typical of computerized manufacturing systems, where a tool carries out variable machining assignments during its life. The formulation is a stochastic dynamic programme, which determines optimal preventive actions based on a periodic evaluation of the tool's operating conditions. Tool deterioration is described as movement to different operating states (increasing levels of tool wear) and job assignment of tools is state-dependent. A tool's optimal economic life is also determined within the context of variable machining. The cost of quality-deviation is assessed using Taguchi's quality-loss function.

Experimental Evaluation of the Laser Assisted Machining of Silicon Nitride Ceramics

To assess the feasibility of the laser assisted machining (LAM) process for the machining of difficult-to-machine materials such as structural ceramics, experiments were performed on silicon nitride workpieces for a wide range of operating conditions. Data for cutting forces and surface temperatures indicate that the lower bound of the material removal temperature for avoidance of cutting tool and/or workpiece fracture corresponds to the YSiAlON glass transition temperature (920–970°C). As temperatures near the cutting tool increase to values above the glass transition temperature, the glassy phase softens, facilitating visco-plastic flow and, correspondingly, the production of semi-continuous or continuous chips. The silicon nitride workpiece machined had a surface roughness of Ra=0.39 μm at the nominal LAM operating condition. Examination of the machined surfaces and chips reveals no detectable sub-surface cracking or significant changes in microstructure, respectively. Relative to grinding, the most significant advantage of LAM is its ability to achieve much larger material removal rates with high workpiece surface quality and reasonable levels of tool wear. [S1087-1357(00)00704-8]

Sensor Fusion for Cutting Tool State Identification in Metal Turning Through the Application of Perceptron Neural Networks

The demand on better returns by investors and the quest for continued customer satisfaction through assured quality products, have culminated in a rapid increase in factory automation processes within the last 20 years. This paper outlines an investigative study, where Artificial Neural Networks are applied to automatically detect the state of a cutting tool. The ensuing wear on the tool is chosen as the parameter to be detected, and process parameters from the cutting environment are measured on-line, fused through the network and its output gives an indication of tool wear level. The implemented Single and Multiple Layer Perceptron Neural Networks achieved success accuracy rates of > 75% and > 95% respectively.

DESIGN AND IMPLEMENTATION OF SENSOR-BASED TOOL-WEAR MONITORING SYSTEMS

This article addresses the design of sensor-based tool-wear monitoring systems and their implementation, and specifically focuses on interpretation of signals from multiple sensors in terms of tool-wear level. Keeping in mind that the absence of a well-accepted realiable methodology and the ignorance of practical issues in implementation are two major reasons to prevent a successful on-line monitoring system from being realised in industries, four critical issues are raised and discussed, which are: (1) expensive information on correct tool condition; (2) troublesome off-line preselection of features from original sensor signals; (3) impractical fresh/worn dichotomy; and (4) fallible signal-interpretation with stationary sensor information. We provide a detailed suggestion of methodology to handle each issue effectively; adoption of an unsupervised clustering-type method (issue 1), development of a customised feature evaluation algorithm (issue 2), tendency of our methodology to avoid serious misinterpretation errors with a finer distinction of wear level (issue 3), and cooperative interpretation with sensor information from multiple time points close to each other (issue 4). Our methodology is evaluated with machining data from a simplified monitoring situation, and proves to be able to bridge the gap between academic enthusiasm and industrial needs for a practical and reliable sensor-based toolwear monitoring system.

Study on the turning characteristics of alumina-based ceramics

In this study, the machinability of advanced ceramics sintered at three different sintering temperatures is investigated. The results show that although pre-sintered (1300°C/30 min) ceramics can be machined with both carbide tools and ceramic tools, the tools sustain significant levels of tool wear after a very short period of machining. The best overall performance is achieved by using the diamond tool; the latter also being found to be essentially the only tool which can be used to machine the fine-sintered (1500°C/30 min) ceramics. In addition, it is found that when cutting ceramics with a coolant or using a sucker to suck the chip from out of tool face, results in more severe tool wear than does dry cutting. The effect is contrary to that normally expected and would appear to be largely due to the chip discharge not proceeding smoothly. However, when tool face near to the cutting edge is heated and sucked, the tool wear can be improved significantly.

A Customized Neural Network for Sensor Fusion in On-Line Monitoring of Cutting Tool Wear

A customized neural network for sensor fusion of acoustic emission and force in on-line detection of tool wear is developed. Based on two critical concerns regarding practical and reliable tool-wear monitoring systems, the maximal utilization of “unsupervised” sensor data and the avoidance of off-line feature analysis, the neural network is trained by unsupervised Kohonen’s Feature Map procedure followed by an Input Feature Scaling algorithm. After levels of tool wear are topologically ordered by Kohonen’s Feature Map, input features of AE and force sensor signals are transformed via Input Feature Scaling so that the resulting decision boundaries of the neural network approximate those of error-minimizing Bayes classifier. In a machining experiment, the customized neural network achieved high accuracy rates in the classification of levels of tool wear. Also, the neural network shows several practical and reliable properties for the implementation of the monitoring system in manufacturing industries.

On-line cutting state recognition in turning Using a neural network

Tool wear, chatter vibration, chip breaking and built-up edge are the main phenomena to be monitored in modern manufacturing processes. Much work has been carried out in the analysis and detection of these phenomena. However, most work has been mainly concerned with single, isolated detection of such phenomena. The relationships between each fault have so far received very little attention. This paper presents a neural-network-based on-line fault diagnosis scheme which monitors the level of tool wear, chatter vibration and chip breaking in a turning operation. The experimental results show that the neural network has a high prediction success rate.

Tribology in interrupted machining: role of interruption cycle and work material

In modern flexible manufacturing practice, machining cycles associated with many components are often short and of varied nature involving a number of interrupted machining modes (IMM). This paper for the first time presents a test method for evaluating tool wear under IMM conditions and the associated machinability of work materials. Systematic turning tests involving a number of programmed tool interruptions resulting in different combinations of cutting ( T c ) and idle ( T i ) durations were used to develop a standard test method. The study indicated the critical role of the duration of the cutting cycle as well as the transient stage associated with tool entry and withdrawal upon the observed tool flank wear. For very short cycles below the critical time, the tool wear level exceeds the corresponding wear in continuous machining. Turning tests using steel SS 2506 and coated carbide inserts for a range of cutting speeds and feeds indicated that the relation between the cutting to idle cycle duration (affecting the average tool temperature levels), the nature of transient forces and friction (affected by the deformation behaviour of the tool-work-machine tool system) as well as the actual tool path, all affect tool wear in a complex way. Since these characteristics could vary from machine to machine, the tool wear characteristics under IMM conditions are machine specific. At high metal removal rates, IMM gave a positive result, as the interruption duration appears to facilitate the cooling of the tool. Tests with other low alloyed steels and stainless steel indicated that, for the recommended cutting conditions, IMM could adversely affect tool wear drastically (for Ca treated quenched and tempered steel), or marginally (ball bearings and stainless steels).

The turning of an Al/SiC metal-matrix composite

Metal-matrix composites are a relatively new range of materials possessing several characteristics that make them useful in situations where low weight, high strength, high stiffness, and an ability to operate at elevated temperatures are required. These materials are difficult to machine, however, consisting of a hard abrasive ceramic reinforcing medium set within a more ductile matrix material. This paper presents results from a series of turning tests in which a number of different cutting-tool materials were used to machine an Al/25 vol% SiC metal-matrix composite. The influence of the cutting speed on the tool wear, the surface finish, and the cutting forces was established for each tool material. It was found that carbide tools, both coated and uncoated, sustained significant levels of tool wear after a very short period of machining. The best overall performance was achieved using a solid cubic boron nitride, CBN, insert whilst the worst was encountered using a solid silicon nitride, Sylon, tool.

Process-controlled machining of gray cast iron

Prior research (ref. 1) reported a model to predict surface roughness for specified values of cutting tool nose radius, feed rate, cutting speed and tool wear. The model is based on experimental data from finish turning of gray cast iron using uncoated tungsten carbide inserts. The results suggest that feed rate and cutting speed can be modified to produce a specified surface roughness in the presence of increasing levels tool wear. The method used in the paper to calculate values of feed rate and cutting speed is based on a multiple objective goal programming optimization scheme. The method predicts optimal machining conditions under the conflicting objectives of maximal production rate and minimal cost, subject to realistic constraints imposed by the capability of the machine tool and restrictions on the process parameters. Further adjustment of the cutting parameters is then made based on specified values of surface roughness and levels of tool wear.

On the reliability of the cutting temperature for monitoring tool wear

This paper examines the reliability of monitoring tool wear by measuring the cutting temperature. Long duration turning tests employing different speed, feed, and depth of cut were conducted. The cutting temperature was sensed on-line using the tool-workpiece thermocouple technique. Flank wear was also examined, however off-line at regular intervals. The experimental results show the thermal voltage signal to be one which is very sensitive to the cutting conditions. The noise component produced by this signal, while it averaged ± 1 per cent of the signal level, and therefore can be classified as a low noise signal, appeared to be large when compared with the small increase in the thermal voltage and hence the cutting temperature due to tool wear. The latter amounted to a maximum of about 3·7 per cent during the life of the tool, and the noise component averaged about 60 per cent of this increase. This was reflected in relatively low correlation coefficients of the thermal voltage-cutting time relation. This, along with the fact that the thermal voltage and therefore the cutting temperature first increases with tool wear and later levels off, makes the reliability of on-line tool wear monitoring by the cutting temperature very doubtful.