Tool life determination based on the measurement of wear and tool force ratio variation

Non-linear regression analysis techniques are used to establish models for wear and tool life determination in terms of the variation of a ratio of force components acting at the tool tip. The ratio of the thrust component of force to the power, or vertical, force component has been used to develop models for (i) its initial value as a function of feed, (ii) wear, and (iii) tool lifetimes. Predictions of the latter model have been compared with the results of experiments, and with predictions of an extended Taylor model. In all cases, good predictive capability of the model has been demonstrated. It is argued that the models are suitable for use in adaptive control strategies for centre lathe turning.     

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.     

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.     

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.     

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.     

On-line monitoring of flank wear in turning with multilayered feed-forward neural network

A multilayer feed-forward neural network (MLFF N-Network) algorithm is presented for on-line monitoring of tool wear in turning operations. The algorithm is based on the cutting conditions (cutting speed and feed rate) and measured cutting forces, which are used as inputs to a three-layer MLFF N-Network. The network is first trained using a set of workpiece material (P20 mold steel) and a tungsten carbide (H13A) cutting tool at various cutting conditions. The algorithm is later successfully verified on-line during turning of the same mold steel at conditions that differ from the data used in training. The algorithm is packaged in a software module, and integrated to an open Intelligent Machining Module used on industrial CNC systems.     

Analytical model for tool wear monitoring in turning operations using ultrasound waves

This paper presents an analytical model to monitor the gradual wear of cutting tools, on-line, during turning operations using ultrasound waves. Ultrasound waves at a frequency of 10 MHz were pulsed continuously inside several cutting tools, towards their cutting edge. The change in tool geometry, due to gradual wear, has been related, in a mathematical form, to the change in the acoustic behavior of ultrasound waves inside the body of the cutting tools. Physical laws governing the propagation and reflection of ultrasound waves along with geometrical analysis of the wear area were used in deriving the mathematical model. The experimental setup and model evaluation is based on a previously published research work by the author, which presented an empirical model showing a corresponding change in the ultrasound behavior with tool gradual wear. The current work emphasizes the previous findings and presents the relation between the acoustic behavior of ultrasound waves and the progressive tool gradual wear in a mathematical form that can be easily used in machine control operations.     

Tool wear monitoring of micro-milling operations

The mechanical removal of materials using miniature tools, known as micro-mechanical milling processes, has unique advantages in creating miniature 3D components using a variety of engineering materials, when compared with photolithographic processes. Since the diameter of miniature tools is very small, excessive forces and vibrations significantly affect the overall quality of the part. In order to improve the part quality and longevity of tools, the monitoring of micro-milling processes is imperative. This paper examines factors affecting tool wear and a tool wear monitoring method using various sensors, such as accelerometers, force and acoustic emission sensors in micro-milling. The signals are fused through the neuro-fuzzy method, which then determines whether the tool is in good shape or is worn. An optical microscope is used to observe the actual tool condition, based upon the edge radius of the tool, during the experiment without disengaging the tool from the machine. The effectiveness of tool wear monitoring, based on a number of different sensors, is also investigated. Several cutting tests are performed to verify the monitoring scheme for the miniature micro-end mills.     

Tool wear estimation using resource allocation network

This paper presents tool wear estimation in face milling operations using the resource allocation network (RAN). Acoustic emission (AE) signals, surface roughness parameters and cutting conditions (cutting speed, feed) have been used to formulate input patterns. The performance of RAN has been compared with the multi-layer perceptron (MLP) trained using back-propagation (BP) algorithm, and the results are presented.     

Tool wear and chip formation during hard turning with self-propelled rotary tools

This paper presents a performance assessment of rotary tool during machining hardened steel. The investigation includes an analysis of chip morphology and modes of tool wear. The effect of tool geometry and type of cutting tool material on the tool self-propelled motion are also investigated. Several tool materials were tested for wear resistance including carbide, coated carbide, and ceramics. The self-propelled coated carbide tools showed superior wear resistance. This was demonstrated by evenly distributed flank wear with no evidence of crater wear. The characteristics of temperature generated during machining with the rotary tool are studied. It was shown that reduced tool temperature eliminates the diffusion wear and dominates the abrasion wear. Also, increasing the tool rotational speed shifted the maximum temperature at the chip–tool interface towards the cutting edge.     

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.     

On-line tool wear estimation in CNC turning operations using fuzzy neural network model

In recent past, several neural network models which employ cutting forces and AErms or their derivatives for estimation as well as classification of flank wear have been developed. However, a significant variation in mean cutting forces and AErms at the start of cutting operation for similar new tools can result in estimation and classification error. In order to deal with this problem, a new on-line fuzzy neural network (FNN) model is presented in this paper. This model has four parts. The first part of the model is developed to classify tool wear by using fuzzy logic. The second part of this model is designed for normalizing the inputs for the next part. The third part consisting of modified least-square backpropagation neural network is built to estimate flank and crater wear. The development of forth part was done in order to adjust the results of the third part. Several basic and derived parameters including forces, AErms, skew and kurtosis of force bands, as well as the total energy of forces were employed as inputs in order to enhance the accuracy of tool wear prediction. The experimental results indicate that the proposed on-line FNN model has a high accuracy for estimating progressive flank and crater wear with small computational time.     

Tool wear monitoring for micro-end grinding of ceramic materials

As grinding process usually is a final step of a machining procedure, excessive grinding tool wear could deteriorate both workpiece surface quality and its dimensional accuracy. This becomes more severe in the case of microgrinding than in conventional grinding because microgrinding wheels are more sensitive to tool wear. An effective tool wear monitoring technique is, therefore, crucial for maintaining consistent machining quality and high efficiency in microgrinding. In this paper, the influence of tool wear and tool stiffness on microgrinding process signals such as grinding force, grinding system vibration, acoustic emission signal and spindle load, are analyzed during end grinding of ceramic materials. To indicate the actual wear status of a microgrinding wheel, this study proposed a new monitoring parameter by fusing grinding force and system vibration signals, based on the concept of varying cutting stiffness. This new monitoring parameter is then experimentally tested in microgrinding a series of ceramic miniature features with consistent and inconsistent geometry.     

On-line monitoring of tool wear in turning using a neural network

Tool wear has long been identified as the most undesirable characteristic of the machining operations. Flank wear, in particular directly affects the workpiece dimensions and the surface quality. A reliable and sensitive technique for monitoring the tool wear without interrupting the process, is crucial in realization of the modern manufacturing concepts like unmanned machining centres, adaptive control optimization, etc. In this work an optoelectronic sensor is used in conjunction with a multilayered Neural Network for predicting the flank wear on the cutting tool. The gap sensing system consists of a bifurcated optical fibre, a laser source and a photodiode circuit. The output of the photodiode circuit is amplified and converted to the digital form using an A/D converter. The digitized sensor signal along with the cutting parameters form the inputs to a three layered, feed forward, fully connected Neural Network. The Neural Network, trained off-line using a backpropagation algorithm and the experimental data, is used to predict the flank wear. A geometrical relation is also used to correlate the flank wear on the cutting tool with the change in the workpiece dimension. The values predicted using the Neural Network and those calculated using the geometrical relation are compared with the actual values measured using a tool maker's microscope. Results showed the ability of the Neural Network to accurately predict the flank wear.     

One-step-ahead prediction of flank wear using cutting force

Tool wear prediction plays an important role in the tip geometry compensation during precision machining. The purpose of this study is to develop a reliable method to predict flank wear during a turning process. The force ratio and increment values are applied to predict one-step-ahead flank wear. The results of this paper show that using force ratios, flank wear can be predicted to within 8 and 11.9%, and also using force increment, flank wear can be predicted to within 10.3% of the actual wear for various turning conditions.     

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.     

Tool wear in friction drilling

This study investigates the tool wear in friction drilling, a nontraditional hole-making process. In friction drilling, a rotating conical tool uses the heat generated by friction to soften and penetrate a thin workpiece and create a bushing without generating chips. The wear of a conical tungsten carbide tool used for friction drilling a low carbon steel workpiece is studied. Tool wear characteristics are quantified by measuring its weight change, detecting changes in its shape with a coordinate measuring machine, and making observations of wear damage using scanning electron microscopy. Energy dispersive spectrometry is applied to analyze the change in chemical composition of the tool surface due to drilling. In addition, the thrust force and torque during drilling and the hole size are measured periodically to monitor the effects of tool wear. Results indicate that the carbide tool is durable, showing minimal tool wear after drilling 11,000 holes, but observations also indicate the progressively severe abrasive grooving on the tool tip.     

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.     

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.     

Reduction of wear induced surface zone effects during hard turning by means of new tool geometries

Tool wear during hard turning influences the properties of the workpiece surface and subsurface layer significantly. Due to increasing flank face wear at the cutting edge, the contact conditions between tool and workpiece are changed. The mechanical and thermal load in the workpiece surface increases during the process. This favors the formation of white layers and of residual stress gradients in the subsurface zone of hardened workpieces whereby the components life time is reduced. The article presents novel modifications of the tool geometry, which leads to a considerable prolongation of the tool life time. This advanced tool design enables the production of constant material properties in the surface and subsurface zone during a broad time window.