Progressive cutting tool wear detection from machined surface images using Voronoi tessellation method

Tool condition monitoring by machine vision approach has been gaining popularity day by day since it is a low cost and flexible method. In this paper, a tool condition monitoring technique by analysing turned surface images has been presented. The aim of this work is to apply an image texture analysis technique on turned surface images for quantitative assessment of cutting tool flank wear, progressively. A novel method by the concept of Voronoi tessellation has been applied in this study to analyse the surface texture of machined surface after the creation of Voronoi diagram. Two texture features, namely, number of polygons with zero cross moment and total void area of Voronoi diagram of machined surface images have been extracted. A correlation study between measured flank wear and extracted texture features has been done for depicting the tool flank wear. It has been found that number of polygons with zero cross moment has better linear relationship with tool flank wear than that of total void area.     

An overview of approaches to end milling tool monitoring

The increase in awareness regarding the need to optimise manufacturing process efficiency has led to a great deal of research aimed at machine tool condition monitoring. This paper considers the application of condition monitoring techniques to the detection of cutting tool wear and breakage during the milling process. Established approaches to the problem are considered and their application to the next generation of monitoring systems is discussed. Two approaches are identified as being key to the industrial application of operational tool monitoring systems.Multiple sensor systems, which use a wide range of sensors with an increasing level of intelligence, are seen as providing long-term benefits, particularly in the field of tool wear monitoring. Such systems are being developed by a number of researchers in this area. The second approach integrates the control signals used by the machine controller into a process monitoring system which is capable of detecting tool breakage. Initial findings mainly under laboratory conditions, indicate that both these approaches can be of major benefit. It is finally argued that a combination of these approaches will ultimately lead to robust systems which can operate in an industrial environment.     

Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods

The state of a cutting tool is an important factor in any metal cutting process as additional costs in terms of scrapped components, machine tool breakage and unscheduled downtime result from worn tool usage. Several methods to develop monitoring devices for observing the wear levels on the cutting tool on-line while engaged in cutting have been attempted. This paper presents a review of some of the methods that have been employed in tool condition monitoring. Particular attention is paid to the manner in which sensor signals from the cutting process have been harnessed and used in the development of tool condition monitoring systems (TCMSs).     

Quantification of tool wear using white light interferometry and three-dimensional computational metrology

Although several wear modes can result from machining, the most common tend to be what are referred to as flank wear and crater wear. Flank wear can be easily measured directly from images of a worn cutting tool, and this is the typical method used to quantify the condition of a tool. On the other hand, crater wear is difficult to quantify, and thus has typically been treated in a qualitative manner. The inability to characterize and compare the two wear modes in a quantitative way is an increasingly important problem as the precision of machining operations improves and cutting moves almost exclusively to the nose radius of cutting tools. This paper introduces a new approach to this problem by proposing a technique to quantify both wear modes for direct comparison. The technique measures the volumetric wear loss in the two regions by comparing three-dimensional wear data obtained by white light interferometry with ideal representations of unworn cutting tools. The resulting wear measurements are compared and related to changes in the cutting process, specifically increases in cutting forces and changes in the topography of machined surfaces.     

Sensorless tool failure monitoring system for drilling machines

It is well known that on-line tool condition monitoring has great significance in modern manufacturing processes. In order to prevent possible damages to the workpiece or the machine tool, reliable techniques are required providing an on-line response to an unexpected tool failure. Drilling is one of the most fundamental machining operations and two of the most crucial issues related to it are tool wear and fracture. During the spindle process, the motor driver current is related to the drill condition: power consumption is higher for a worn drill in comparison to a sharp drill for the same process. This difference in power consumption can be self-correlated to obtain the resulting waveform variance to provide a merit figure for tool condition. This paper describes a driver current signal analysis to estimate the tool condition by using the discrete Wavelet Transform in order to extract the information from the original cutting force, and through an autocorrelation algorithm evaluate the tool wear in the form of an asymmetry weighting function. The current is monitored from the motor driver to give a sensorless approach. Experimental results are presented to show the algorithm performance, a complete sensorless tool failure system which allows the detection of tool failure as a function of spindle current in real time.     

Tool wear detection and fault diagnosis based on cutting force monitoring

In metal cutting processes, an effective monitoring system, which depends on a suitably developed scheme or set of algorithms can maintain machine tools in good condition and delay the occurrence of tool wear. In this paper, an approach is developed for fault detection and diagnosis based on an observer model of an uncertain linear system. A robust observer is designed, using the derived uncertain linear model, to yield the necessary and key information from the system. Subsequently, it is used as a state (tool wear) estimator, and fault detection is carried out by using the observed variables and cutting force. The developed approach is applied to milling machine center. Several linear models are identified based on different working conditions. A dominant model plus uncertain terms is derived from these model set and used as an observer. Threshold values are proposed for detecting the fault of the milling machine. Examples taken from experimental tests shown that the developed approach is effective for the fault detection. The approach can be used for fault detection of failures arising from sensor or actuator malfunction.     

Driver current analysis for sensorless tool breakage monitoring of CNC milling machines

Tool wear and breakage detection is one of the most important problems found during manufacture in automated CNC machines. From several techniques devoted to sense tool condition, driver current monitoring has been used for a sensorless approach. In order to efficiently use the driver current monitoring technique an exhaustive analysis on the nature of the real components of the signal is required. The novelty of this paper is to present a driver current signal analysis to estimate the influence of the most important spurious signal components in order to determine the optimal parameters for signal conditioning. Beside the cutting force signal, the spurious signals considered in the analysis are high-frequency noise, current control commutation and ball screw effects. The analysis is compared with experimental data in order to validate the model and a case study is presented to show the general procedure.     

A new method for monitoring micro-electric discharge machining processes

Micro-electric discharge machining (μ-EDM) is a very complex phenomenon in terms of its material removal characteristics since it is affected by many complications such as adhesion, short-circuiting and cavitations. This paper presents a new method for monitoring μ-EDM processes by counting discharge pulses and it presents a fundamental study of a prognosis approach for calculating the total energy of discharge pulses. For different machining types (shape-up and flat-head) and machining conditions (mandrel rotation and tool electrode vibration), the results obtained using this new monitoring method with the prognosis approach show good agreement between the discharge pulses number and the total energy of discharge pulses to the material removal and tool electrode wear characteristic in μ-EDM processes. On applying tool electrode vibration, the machining time becomes shorter, because it removes adhesion. The effect of tool electrode vibration in order to remove adhesion can be monitored with good results. In order to achieve high accuracy, the tool wear compensation factor has been successfully calculated, since the amount of tool electrode wear is different in each machining type and condition. Consequently, a deeper understanding of the μ-EDM process has been achieved.     

Real-time tool wear monitoring in milling using a cutting condition independent method

This paper describes a new method to monitor end milling tool wear in real-time by tracking force model coefficients during the cutting process. The behavior of these coefficients are shown to be independent from the cutting conditions and correlated with the wear state of the cutting tool. The tangential and radial force model coefficients are normalized and combined into a single parameter for wear monitoring. A number of experiments with different workpiece materials are run to investigate the feasibility of tool wear monitoring using this method. We show that this method can be used in real-time to track tool wear and detect the transition point from the gradual wear region to the failure region in which the rate of wear accelerates.     

Development of a tool wear-monitoring system for hard turning

This paper describes an in-depth study on the development of a system for monitoring tool wear in hard turning. Hard turning is used in the manufacturing industry as an economic alternative to grinding, but the reliability of hard turning processes is often unpredictable. One of the main factors affecting the reliability of hard turning is tool wear. Conventional wear-monitoring systems for turning operations cannot be used for monitoring tools used in hard turning because a conglomeration of phenomena, such as chip formation, tool wear and surface finish during hard turning, exhibits unique behavior not found in regular turning operations. In this study, various aspects associated with hard turning were investigated with the aim of designing an accurate tool wear-monitoring system for hard turning. The findings of the investigation showed that the best method to monitor tool wear during hard turning would be by means of force-based monitoring with an Artificial Intelligence (AI) model. The novel formulation of the proposed AI model enables it to provide an accurate solution for monitoring crater and flank wear during hard turning. The suggested wear-monitoring system is simple and flexible enough for online implementation, which will allow more reliable hard turning in industry.     

A summary of methods applied to tool condition monitoring in drilling

This paper presents a summary of the monitoring methods, signal analysis and diagnostic techniques for tool wear and failure monitoring in drilling that have been tested and reported in the literature. The paper covers only indirect monitoring methods such as force, vibration and current measurements, i.e. direct monitoring methods based on dimensional measurement etc. are not included. Signal analysis techniques cover all the methods that have been used with indirect measurements including e.g. statistical parameters and Fast Fourier and Wavelet Transform. Only a limited number of automatic diagnostic tools have been developed for diagnosis of the condition of the tool in drilling. All of these rather diverse approaches that have been available are covered in this study. In the reported material there are both success stories and also those that have not been so successful. Only in a few of the papers have attempts been made to compare the chosen approach with other methods. Many of the papers only present one approach and unfortunately quite often the test material of the study is limited especially in what comes to the cutting process parameter variation, i.e. variation of cutting speed, feed rate, drill diameter and material and also workpiece material.     

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.     

A critical analysis of effectiveness of acoustic emission signals to detect tool and workpiece malfunctions in milling operations

The industrial demands for automated machining systems to increase process productivity and quality in milling of aerospace critical safety components requires advanced investigations of the monitoring techniques. This is focussed on the detection and prediction of the occurrence of process malfunctions at both of tool (e.g. wear/chipping of cutting edges) and workpiece surface integrity (e.g. material drags, laps, pluckings) levels. Acoustic emission (AE) has been employed predominantly for tool condition monitoring of continuous machining operations (e.g. turning, drilling), but relatively little attention has been paid to monitor interrupted processes such as milling and especially to detect the occurrence of possible surface anomalies.This paper reports for the first time on the possibility of using AE sensory measures for monitoring both tool and workpiece surface integrity to enable milling of “damage-free” surfaces. The research focussed on identifying advanced monitoring techniques to enable the calculation of comprehensive AE sensory measures that can be applied independently and/or in conjunction with other sensory signals (e.g. force) to respond to the following technical requirements: (i) to identify time domain patterns that are independent from the tool path; (ii) ability to “calibrate” AE sensory measures against the gradual increase of tool wear/force signals; (iii) capability to detect workpiece surface defects (anomalies) as result of high energy transfer to the machined surfaces when abusive milling is applied.Although some drawbacks exist due to the amount of data manipulation, the results show good evidence that the proposed AE sensory measures have a great potential to be used in flexible and easily implementable solutions for monitoring tool and/or workpiece surface anomalies in milling operations.     

Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs)

Monitoring of tool wear condition for drilling is a very important economical consideration in automated manufacturing. Two techniques are proposed in this paper for the on-line identification of tool wear based on the measurement of cutting forces and power signals. These techniques use hidden Markov models (HMMs), commonly used in speech recognition. In the first method, bargraph monitoring of the HMM probabilities is used to track the progress of tool wear during the drilling operation. In the second method, sensor signals that correspond to various types of wear status, e.g., sharp, workable and dull, are classified using a multiple modeling method. Experimental results demonstrate the effectiveness of the proposed methods. Although this work focuses on on-line tool wear condition monitoring for drilling operations, the HMM monitoring techniques introduced in this paper can be applied to other cutting processes.     

A novel approach to quantifying tool wear and tool life measurements for optimal tool management

It is a common practice in batch production to continually use the same tool to machine different parts, using disparate machining parameters. In such an environment, the optimal points at which tools have to be changed, while achieving minimum production cost and maximum production rate within the surface roughness specifications, have not been adequately studied. The tool wear index (TWI) and the tool life model developed in this study use a novel approach, analyzing wear surface areas and material loss from the tool using micro-optics and image processing/analysis algorithms. With relation to surface roughness, the TWI measures the wear conditions more accurately and comprehensively, and the tool life model enables maximum use of a worn tool and minimum risk for in-process tool failure. The TWI and a surface roughness control model are integrated into an optimal control strategy that shows potential for productivity improvement and reduction of manufacturing cost.     

An experimental investigation on monitoring of crater wear in turning using ultrasonic technique

Tool life prediction and tool change strategies are now based on most conservative estimates of tool life from past tool wear data. Hence usually tools are underutilized. In an unmanned factory, this has the effect of increased frequency of the tool changes and therefore increased cost. An ultrasound online monitoring of crater wear of the uncoated carbide insert during the turning operation is presented. The method relies on inducing ultrasound waves in the tool, which are reflected by side flank surface. The amount of reflected energy is correlated with crater wear depth. Various ultrasonic parameters are considered for defining the crater wear and individual contribution of each parameter is analyzed. The ultrasonic parameters, amplitude, pulse width and root mean square (RMS) of the signal are used to quantify the crater depth and width. The power spectrum analysis of received signals shows the importance of frequency components in defining the tool wear. In the presented work, the normalizing of signals are carried out by insert hole, which is provided for clamping. This signal is not influenced by the wear but affected by other factors like tool material variation, improper couplant, temperature, etc. The response of the wear signal is normalized to the response of hole signal by mathematical division. A new approach adaptive neuro-fuzzy inference system (ANFIS) for monitoring of crater in carbide insert is presented. This improves the system accuracy and eliminates the limitation in statistical modeling that was presented in previous studies.     

Flank wear measurement by a threshold independent method with sub-pixel accuracy

This paper presents an image processing procedure to detect and measure the tool flank wear area. Unlike the traditional thresholding-based methods, a rough-to-fine strategy is considered in this paper whereby a binary image is first obtained and used to find the candidate wear bottom edge points; then a threshold-independent edge detection method based on moment invariance is employed for more robust determination of the wear edge with sub-pixel accuracy. To shorten computation time, a critical area is initially defined and the subsequent procedure is confined to processing this area as the region of interest. Images from three types of inserts, A30N, AC325 and ACZ350 under different cutting conditions are captured with the similar illumination conditions after milling. The measured results obtained with the proposed method from these images are compared with those obtained by direct manual measurement with a toolmaker's microscope and a method based totally on binary image contour detection. The proposed method is shown to be effective and suitable for the unmanned measurement of flank wear.     

A brief review: acoustic emission method for tool wear monitoring during turning

Research during the past several years has established the effectiveness of acoustic emission (AE)-based sensing methodologies for machine condition analysis and process monitoring. AE has been proposed and evaluated for a variety of sensing tasks as well as for use as a technique for quantitative studies of manufacturing processes. This paper reviews briefly the research on AE sensing of tool wear condition in turning. The main contents included are:

1. The AE generation in metal cutting processes, AE signal classification, and AE signal correction.

2. AE signal processing with various methodologies, including time series analysis, FFT, wavelet transform, etc.

3. Estimation of tool wear condition, including pattern classification, GMDH methodology, fuzzy classifier, neural network, and sensor and data fusion.

A review of AE-based tool wear monitoring in turning is an important step for improving and developing new tool wear monitoring methodology.     

在线金属切削刀具磨损状态监测研究的回顾与展望Ⅲ:模式识别方法

刀具状态监测的目的是为了开发出实用的刀具状态监测设备,为了降低设备成本和提高监测的准确率,在监测信号的选择和特征提取的基础上,选择合适的模式识别方法至关重要。对近年来在学术期刊上公开发表的关于刀具磨损在线监测研究中所采用的主要模式识别方法作了简要的回顾与归纳,为后续研究者的快速入门及选择适当的模式识别方法提供参考。