Monitoring of tool fracture in end milling using induction motor current

This paper describes the use of induction motor current to monitor tool fracture in end milling operations. The principles of induction motors are studied in this paper to establish the relationship between the motor current and the motor torque. It is shown that the square of the stator current of induction motors is approximately proportional to the motor torque. Since the occurrence of tool fracture will cause variations in the motor torque, measurement of the stator current appears to be an indirect technique for monitoring tool fracture. A sensitivity analysis of the stator current to the occurrence of tool fracture is also reported. Finally, experimental results under varying cutting conditions have been presented to demonstrate the effectiveness of this approach for the detection of tool fracture in end milling operations.     

End milling tool breakage detection using lifting scheme and Mahalanobis distance

In this paper, a novel method based on lifting scheme and Mahalanobis distance (MD) is proposed for detection of tool breakage via acoustic emission (AE) signals generated in end milling process. The method consists of three stages. First, by investigating the specialty of AE signals, a biorthogonal wavelet with impact property is constructed using lifting scheme, and wavelet transform is carried out to separate AE components from the original signals. Second, Hilbert transform is adopted to demodulate signal envelope on wavelet coefficients and salient features indicating the tool state (i.e., normal conditions, slight breakage, and serious breakage) are extracted. Finally, tool conditions are identified directly through the recognition of these features by means of MD. Practical application results on a CNC vertical milling machine tool show that the proposed method is accurate for feature extraction and efficient for condition monitoring of cutting tools in end milling process.     

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.     

Flute breakage detection during end milling using Hilbert–Huang transform and smoothed nonlinear energy operator

Real time tool condition monitoring has great significance in modern manufacturing processes. In order to prevent possible damages to the workpiece or the machine tool, reliable monitoring techniques are required to provide fast response to the unexpected tool failure. Milling is one of the most fundamental machining operations. During the milling process, the current of feed motor is weakly related to the cutter condition, the change of power consumption is not significant to identify tool condition. Thus, current of motor-based tool condition still requires some new approaches to sort out significant pattern that could be employed to indicate tool condition. In this paper, a new approach is proposed to detect end mill flute breakage via the feed-motor current signals, which implements Hilbert–Huang transform (HHT) analysis and a smoothed nonlinear energy operator (SNEO) to extract the crucial characteristics from the measured signals to indicate tool breakage. Experiments on a CNC Vertical Machining Centre are presented to show the algorithm performance. The results show that this method is feasible and can accurately and efficiently monitor the conditions of the end mill under varying cutting conditions.     

Discrete wavelet transform for tool breakage monitoring

Detection of tool breakage is of vital importance in automated manufacturing. Various methods have been attempted, and it is considered that the use of discrete wavelet transform (DWT), which is much more efficient and just as accurate wavelet analysis, may provide a realistic solution to the detection of tool breakage in operation. The DWT uses an analyzing wavelet function which is localized in both time and frequency to detect a small change in the input signals. In addition, it requires less computation than Fast Fourier Transformation (FFT). This paper discusses a tool breakage monitoring system based on DWT of an acoustic emission (AE) and an electric feed current signal using an effective algorithm. The experiment results show overall 98.5% reliability and the good real-time monitoring capability of the proposed methodology for detecting tool breakage during drilling.     

On-line detection of the breakage of small diameter drills using current signature wavelet transform

This paper presents on-line tool breakage detection of small diameter drills by monitoring the AC servo motor current. The continuous wavelet transform was used to decompose the spindle AC servo motor current signal and the discrete wavelet transform was used to decompose the feed AC servo motor current signal in time–frequency domain. The tool breakage features were extracted from the decomposed signals. Experimental results show that the proposed monitoring system possessed an excellent on-line capability; in addition, it had a low sensitivity to change of the cutting conditions and high success rate for the detection of the breakage of small diameter drills.     

Analytical prediction of chatter stability in ball end milling with tool inclination

The paper presents an analytical method to predict chatter stability in ball end milling with tool inclination. The chatter stability limits in ball end milling without the tool inclination have been predicted in the previous study by deriving directional milling force coefficients and then solving a simple quadratic equation. However, the tool is generally inclined and not perpendicular to the cut surface in practice. Therefore, a new method is developed to compute the directional milling force coefficients considering the tool inclination. It is confirmed that the chatter stability predicted by the proposed method agrees well with the experiments.     

An approach based on current and sound signals for in-process tool wear monitoring

This paper presents a tool condition monitoring system (TCMS) for on-line tool wear monitoring in turning. The proposed TCMS was developed taking into account the necessary trade-off between cost and performance to be applicable in practice, in addition to a high success rate. The monitoring signals were the feed motor current and the sound signal. The former was used to estimate the feed cutting force using the least squares version of support vector machines (LS-SVM). Singular spectrum analysis (SSA) was used to extract information correlated with tool wear from the sound signal. The estimated feed cutting force and the SSA decomposition of the sound signal alone with the cutting conditions constitute the input data to the TCMS. Again LS-SVM was used to estimate tool condition and its reliability for on-line implementation was validated by experiments using AISI 1040 steel. The results showed that the proposed TCMS is fast and reliable for tool condition monitoring.     

An experimental technique for the measurement of temperature on CBN tool face in end milling

An infrared radiation pyrometer with two optical fibers connected by a fiber coupler was developed and applied to the measurement of tool–chip interface temperature in end milling with a binderless CBN tool. The infrared rays radiated from the tool–chip interface and transmitted through the binderless CBN are accepted by the optical fiber inserted in the tool and are then sent to the pyrometer. A combination of the two fibers and the fiber coupler makes it possible to transmit the accepted rays to the pyrometer, which is set up outside of the machine tool. This method is very practical in end milling for measuring the temperature history at tool–chip interface during chip formation. The maximum tool–chip interface temperature in up milling of a 0.55% carbon steel is 480 °C when the cutting speed is 2.2 m/s and 560 °C at 4.4 m/s, and in the down milling, 500 °C at 2.2 m/s and 600 °C at 4.4 m/s.     

An investigation of workpiece temperature variation in end milling considering flank rubbing effect

Better prediction about the magnitude and distribution of workpiece temperatures has a great significance for improving performance of metal cutting process, especially in the aviation industry. A thermal model is presented to describe the cyclic temperature variation in the workpiece for end milling. Owing to rapid tool wear in the machining of aeronautical components, flank rubbing effect is considered. In the proposed heat source method for milling, both the cutting edge and time history of process are discretized into elements to tackle geometrical and kinematical complexities. Based on this concept, a technique to calculate the workpiece temperature in stable state, which supposes the tool makes reverse movement, is developed. And a practicable solution is provided by constructing a periodic temperature rise function series. This investigation indicates theoretically and experimentally the impact of different machining conditions, flank wear widths and cutter locations on the variation of workpiece temperature. The model results have been compared with the experimental data obtained by machining 300M steel under different flank wear widths and cutting conditions. The comparison indicates a good agreement both in trends and values. With the alternative method, an accurate simulation of workpiece temperature variation can be achieved and computational time of the algorithm is obviously shorter than that of finite element method. This work can be further employed to optimize cutting conditions for controlling the machined surface integrity.     

Tool wear evaluation by vibration analysis during end milling of AISI D3 cold work tool steel with 35 HRC hardness

In this study, the relationship between vibration and tool wear was investigated during end milling. For this purpose, a series of experiment were conducted in a vertical milling machine. An indexable CBN insert and AISI D3 cold work tool steel hardened to 35 HRC were used as material twin in the experiments. The vibration was measured only in the machining direction, which has more dominant signals than in the other two directions. The measurements were taken by using an acceleration sensor assembled on a machinery analyzer. Tool wear was measured by a toolmaker's microscope. It was observed that there was an increase in vibration amplitude with increasing tool wears. This situation was evident especially by monitoring vibration of displacement type. It was also observed that the first three multiplies of tooth passing frequency (1×, 2×, 3×) gave the best information about the tool wear. Results showed that there was no considerable increase in the vibration amplitude until a flank wear value of 160 μm was reached, above which the vibration amplitude increased significantly.     

An experimental study of tool wear and cutting force variation in the end milling of Inconel 718 with coated carbide inserts

Inconel 718 is a difficult-to-cut nickel-based superalloy commonly used in aerospace industry. This paper presents an experimental study of the tool wear propagation and cutting force variations in the end milling of Inconel 718 with coated carbide inserts. The experimental results showed that significant flank wear was the predominant failure mode affecting the tool life. The tool flank wear propagation in the up milling operations was more rapid than that in the down milling operations. The cutting force variation along with the tool wear propagation was also analysed. While the thermal effects could be a significant cause for the peak force variation within a single cutting pass, the tool wear propagation was believed to be responsible for the gradual increase of the mean peak force in successive cutting passes.     

Active integration of tool deflection effects in end milling. Part 2. Compensation of tool deflection

This study presents a compensation method in milling machining in order to take into account tool deflection during tool-path generation. Tool deflection that occurs during machining, and especially when flexible tools such as end mills are used, can result in dimensional errors on workpieces. The study presented here is part two of a two-part paper. In part one the cutting force models and the surface prediction method have been presented.Here the focus is on tool deflection effects' integration during the generation of the tool path. A strategy is proposed that modifies the nominal tool trajectory, compensates for the machining errors due to tool deflection, without degrading the production performance and the machined accuracy. The methodology allows optimization of the tool path trajectory in order to achieved a specified tolerance. Some experimental results are presented.     

Real time monitoring of tool breakage in a milling operation using a digital signal processor

A monitoring system that can detect tool breakage and chipping in real time was developed using a digital signal processor (DSP) board in a face milling operation. An autoregressive (AR) model and a band energy method were used to extract the features of tool states from cutting force signals. Then, two artificial neural networks, which have a parallel processing capability, were embedded on the DSP board to discriminate different malfunction states from features obtained by each of the two methods of signal processing. In experiments, we found that feature parameters extracted by AR modeling were more accurate indicators of malfunctions in the process than those from the band energy method, although the computing speed is slower. By using the selected features, we were able to monitor malfunctions in real time.     

Active integration of tool deflection effects in end milling. Part 1. Prediction of milled surfaces

Tools deflection that occurs during machining, and especially when flexible tools such as end mills are used, can result in dimensional errors on workpieces. The study presented here is part one of a two-part paper: it deals with the estimation of cutting forces and the prediction of milled surface. The second part will focus on a methodology that allows to optimize the production rate by compensating the deflection and meeting the part tolerance.Cutting force models have been and are still the subject of a lot of research. The model used is based on Kline and Devor's [5]: a polynomial approximation whose coefficients are obtained by least square methodology is used for the calculation of cutting forces. The machined surface (two axis machining) is determined using the contact point methodology and some experimental tests are done to validate the models.     

Feasibility study of the minimum quantity lubrication in high-speed end milling of NAK80 hardened steel by coated carbide tool

High-speed milling of hardened steels generates high cutting temperature and leads to detrimental effects on tool life and workpiece surface finish. In this paper, feasibility study of the minimum quantity lubrication (MQL) in high-speed end milling of NAK80 hardened steel by coated carbide tool was undertaken. Flood cooling and dry cutting experiments were conducted also for comparison. It is found that cutting under flood cooling condition results in the shortest tool life due to severe thermal cracks while the use of MQL leads to the best performance. MQL is beneficial to tool life both in the lower speed cutting and the higher speed cutting conditions. A less viscous oil of MQL is essential in high cutting speed so that cooling effect can be effective. SEM micrograph of the insert shows that the use of MQL in high-speed cutting can delay welding of chips on the tool and hence prolongs tool life as compared with dry cutting condition. The application of MQL also improves machined surface finish in high-speed milling of die steels.     

Influence of the number of inserts for tool life evaluation in face milling of steels

Tool life tests are often employed to verify the behaviour of one or more inserts in a cutter in order to optimise machining productivity and minimise cost. In milling process, such tests are expensive and require many of tools and a lot of work material to achieve any of the stipulated tool rejection criterion in any of the inserts. In practice, tool life tests are usually carried out using only one or few edges in a face milling cutter in order to minimise cost. The aim of this study is to investigate the effect of the number of tools used in face milling operation and how they relate to the establishment of tool life under specified cutting conditions. Flank wear curves were evaluated for AISI 1045 and 8640 steels using 1, 2, 3 and 6 inserts in a face milling cutter. Test results show that reduction in the number of inserts in the milling cutter led to a reduction in the amount of material removed and also tend to increase tool life when machining at the same feed per tooth. Results obtained using reduced number of inserts in a milling cutter should only be used for comparison between two or more conditions and should not be used to establish tool life.     

Machine ensemble approach for simultaneous detection of transient and gradual abnormalities in end milling using multisensor fusion

In a fully automated manufacturing environment, instant detection of the cutting tool condition is essential for the improved productivity and cost effectiveness. This paper studies a tool condition monitoring system (TCM) via machine learning (ML) and machine ensemble (ME) approach to investigate the effectiveness of multisensor fusion technique when machining 4340 steel with multilayer coated and multiflute carbide end mill cutter. In this study, 135 different features are extracted from multiple sensor signals of force, vibration, acoustic emission and spindle power in the time and frequency domain by using data acquisition and signal processing module. Then, a correlation-based feature selection technique (CFS) evaluates the significance of these features along with machining parameters collected from machining experiments. Next, an optimal feature subset is computed for various assorted combinations of sensors. Finally, machine ensemble methods based on majority voting and stacked generalization are studied for the selected features to classify not only flank wear but also breakage and chipping. It has been found in this paper that the stacked generalization ensemble can ensure the highest accuracy in tool condition monitoring. In addition, it has been shown that the support vector machine (SVM) outperforms other ML algorithms in most cases tested.     

Prediction of drill failure using features extraction in time and frequency domains of feed motor current

In this paper, a drill prefailure prediction method based on the feed motor current is proposed. The characteristic parameters of drill failure (CPDF) are defined in the time and frequency domains to express the features of the feed motor current at drill failure. In the time domain, the CPDFs represent the increase of average value and the standard deviation of the feed motor current at drill failure. In the frequency domain, the CPDFs represent the magnitude of vibration at the spindle rotational frequency and at two times this frequency of the feed motor current. The CPDFs are used as inputs to the neural network. The output of the neural network is defined as the drill state index (DSI). Drill failure is predicted by monitoring the number of times the DSI exceeds the threshold value of DSI. Experiments showed that the proposed algorithm could accurately identify impending failure before drill breakage regardless of cutting conditions and machine tool types.