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.     

Tool breakage diagnosis in face milling by support vector machine

This paper introduces a new diagnosis technique for tool breakage in face milling using a support vector machine (SVM). The features of spindle displacement signals are first fed into the kernel-based SVM decision function. After the SVM learning procedure, the SVM can respond in real-time to automatically diagnose tool fracture under varying cutting conditions. Experimental results show that this new approach can detect tool breakage in a wide range of face-milling operations.     

The shape characteristic detection of tool breakage in milling operations

Detection of tool failure is very important in automated manufacturing. All previously developed tool breakage detection approaches in milling operations have adopted the strategy of parameter detection in which the detection of tool breakage was carried out according to values of specific parameters selected to reflect tool state (with or without tool breakage). In this paper the new concept of shape characteristic detection of tool breakage in milling operations is proposed. The detection of tool breakage is conducted according to the shape characteristics of discrete dyadic wavelet decomposition of cutting force. By means of the proposed method, the influence caused by the variation of cutting parameters and transients is eliminated. The proposed method is conducted in two steps. In the first step, cutting force signals are decomposed by discrete dyadic wavelet, with the shape characteristic vectors then being generated by the proposed shape characteristic vector-generating algorithm. In the second step, the shape characteristic vectors are fast classified by the ART2 neural networks. The accuracy and effectiveness of the proposed method are verified by numerous experiments.     

Study of milling cutting force pulsation applied to the detection of tool breakage

A study of the cutting force pulsation due to tool breakage is presented. Monitoring algorithms extracting the cutting force signal changes caused by tool breakage and further processing the extracted cutting force signal to recognize tool breakage are proposed. Theoretical studies and experimental results performed in milling operations have proven the feasibility of the algorithms proposed.     

Detection of tool breakage in milling operations—II. The neural network approach

On-line monitoring of tool cutting conditions and tool breakage is very important for automated factories of the future. In this paper, the time series based tooth period modeling technique (TPMT) is proposed for detecting tool breakage by monitoring a cutting force or torque signal in any direction. TPMT uses the fast a posteriori error sequential technique (FAEST) for on-line modeling of cutting force or torque signals. Tool breakage is detected by evaluating variations of the characteristics of the monitored signal in each tooth period. TPMT was tested on simulated and experimental end milling data. The proposed technique detected tool breakage in all of the test cases without giving any false alarms in the transition cases.     

Tool wear monitoring and breakage detection based on “intelligent filtering”

Several physical quantities can be used for indirect tool wear monitoring and breakage detection. Cutting forces are appropriate, since they determine the suitability of a tool for cutting. However, disturbances make the accurate and fast tool condition monitoring based upon force analysis difficult. To eliminate disturbances averaging or filtering within a predetermined bandwidth has often been applied. A new method of decomposing the force signal into components having close relationships with physical phenomena taking place during cutting is presented. The term “intelligent filtering” denotes decomposition based on on-line identification of model(s) of the cutting process. Application of “intelligent filtering” for the milling process with two cutting insert tools is discussed.     

Cutting state monitoring in milling by a neural network

The application of a neural network to cutting state monitoring in face milling was introduced and evaluated on multiple sensor data such as cutting forces and vibrations. This monitoring system consists of a statistically based adaptive preprocessor (autoregressive (AR) time series modeling) for generating features from each sensor, followed by a highly parallel neural network for associating the preprocessor outputs (sensor fusion) with the appropriate decisions. AR model parameters were used as features, and the cutting states (normal, unstable and tool life end) were successfully detected by monitoring the evolution of model parameters during face milling. The proposed system offers fast operation through recursive preprocessing and highly parallel association, and a data-driven training scheme without explicit rules or a priori statistics. It appears proven on limited experimental data.     

A three-dimensional analytical cutting force model for micro end milling operation

In the present day manufacturing arena one of the most important fields of interest lies in the manufacturing of miniaturized components. End milling with fine-grained carbide micro end mills could be an efficient and economical means for medium and small lot production of micro components. Analysis of the cutting force in micro end milling plays a vital role in characterizing the cutting process, in estimating the tool life and in optimizing the process. A new approach to analytical three-dimensional cutting force modeling has been introduced in this paper. The model determines the theoretical chip area at any specific angular position of the tool cutting edge by considering the geometry of the path of the cutting edge and relates this with tangential cutting force. A greater proportion of the helix face of the cutter participating in the cutting process differs the cutting force profile in micro end milling operations a bit from that in conventional end milling operations. This is because of the reason that the depth-of-cut to tool diameter ratio is much higher in micro end milling than the conventional one. The analytical cutting force expressions developed in this model have been simulated for a set of cutting conditions and are found to be well in harmony with experimental results.     

Optimum orientation of a torus milling cutter: Method to balance the transversal cutting force

In this paper, a new method for tool positioning in milling on torus cutters with round inserts is presented. A new criterion associated with balancing of the transversal cutting force is used to compute a tool orientation. The considered tool inclination is towards the back of the tool. In this case, all inserts work simultaneously and generate a continuous cutting phenomenon. Each of the inserts produces a transversal cutting force; some being positive while others are negative. A small tool axis inclination angle leads to balancing the transversal cutting force exerted on the tool and then reducing deflection and vibrations in milling operations. Firstly, this approach to the dynamic aspects relating to cutting forces in the milling process is significant for mould and die manufacturing since it allows polishing time to be reduced. In addition, as vibrations are reduced, enhanced surface quality can be obtained directly on free-form surfaces such as aeronautic fittings.     

Approach into the use of probabilistic neural networks for automated classification of tool malfunctions in broaching

The condition of broaching tools has crucial importance for the surface quality of the machined components. If undetected, tool malfunctions such as wear, chipping and breakage of cutting teeth can result in severe damage or even scrapping expensive components, with direct implications on increasing the overall manufacturing costs. In contrast with other machining operations, broaching is characterised by non-symmetric distributions of cutting forces vs. time, making more difficult the task of recognising tool malfunctions. The paper reports on a methodology to automatically detect and classify tool malfunctions in broaching. The method was demonstrated through the use of time domain distribution of the push-off cutting force as a key sensory signal to monitor broaching tool condition when machining a nickel-based aerospace alloy. The characteristic features of the sensory signals have been extracted using in-house-developed programs and, afterwards, used to train and test a probabilistic neural network that enables automated classification of tools with fresh, worn, chipped and broken teeth. Inputting new pattern characteristics to the main categories of tool malfunctions, the system successfully classified them even when variations of signal amplitude and ranking of malfunctioned teeth occurred.     

基于声发射的铣刀破损信号特征的提取与优化

铣刀破损监测对实现加工自动化具有重要的意义.提出了基于小波变换的铣刀声发射破损特征提取与优化方法.首先,采用小波变换对铣刀声发射信号进行多分辨率分解,然后提取各频段子信号的能量比作为刀具破损监测的特征量.通过对正常切削、随机冲击和刀具破损这三类信号的比较分析,证明了该特征提取方法能够有效地反映刀具状态.最后,通过正交统计,分析了切削速度、进给速度和切削深度对特征量的影响,并对特征量进行优化.     

Measurement of quasi-mean resultant force using the vibrational signal of spindle in milling

The quasi-mean resultant force has been proven to be useful on the real-time process control and tool monitoring in milling operations. This paper presents a new way to measure the quasi-mean resultant force using the vibrational displacement signal of spindle. The quasi-mean resultant force can be obtained by subtracting the spindle run-out pattern from the average displacement signal per tooth period, then multiplying a constant, k^*. This new approach is illustrated by computational simulations and experimental cutting tests.     

基于改进瞬时刚性力模型的机床铣削加工监测

针对用于切削力预测的瞬时刚性力模型所需参数较多且依赖初步切削实验的问题,提出一种不需要切削实验的新型切削力预测方法,实现在实际工厂中监测机床铣削加工过程。在斜角切削模型和正交切削理论的基础上,对传统的瞬时刚性力模型进行改进,减少切削力预测所需的切削参数。改进后的模型仅需在铣削操作开始时从测量的主轴电机扭矩得到的剪切角参数,无需任何额外的传感器就可以实现铣削力预测。在所提模型中,刀具跳动的影响可通过每个切削刃处的旋转半径偏差表示,以精确预测切削力。为验证该模型的有效性,进行切削实验。结果表明：切削力的预测值与实测值吻合较好,在实际加工过程中,无需任何实验铣削或任何额外的力传感器就可以准确了解机床加工状态。     

Using neural network for tool condition monitoring based on wavelet decomposition

This paper presents a neural network application for on-line tool condition monitoring in a turning operation. A wavelet technique was used to decompose dynamic cutting force signal into different frequency bands in time domain. Two features were extracted from the decomposed signal for each frequency band. The two extracted features were mean values and variances of the local maxima of the absolute value of the composed signal. In addition, coherence coefficient in low frequency band was also selected as a signal feature. After scaling, these features were fed to a back-propagation neural network for the diagnostic purposes. The effect on tool condition monitoring due to the presence of chip breaking was studied. The different numbers of training samples were used to train the neural network and the results were discussed. The experimental results show that the features extracted by wavelet technique had a low sensitivity to changes of the cutting conditions and the neural network has high diagnosis success rate in a wide range of cutting conditions.     

Geometric and force errors compensation in a 3-axis CNC milling machine

This paper proposes a new off line error compensation model by taking into accounting of geometric and cutting force induced errors in a 3-axis CNC milling machine. Geometric error of a 3-axis milling machine composes of 21 components, which can be measured by laser interferometer within the working volume. Geometric error estimation determined by back-propagation neural network is proposed and used separately in the geometric error compensation model. Likewise, cutting force induced error estimation by back-propagation neural network determined based on a flat end mill behavior observation is proposed and used separately in the cutting force induced error compensation model. Various experiments over a wide range of cutting conditions are carried out to investigate cutting force and machine error relation. Finally, the combination of geometric and cutting force induced errors is modeled by the combined back-propagation neural network. This unique model is used to compensate both geometric and cutting force induced errors simultaneously by a single model. Experimental tests have been carried out in order to validate the performance of geometric and cutting force induced errors compensation model.     

Fuzzy control of feed rate in end milling operations

In order to improve productivity in end milling operations, a new adaptive control system based on fuzzy logics to maintain a constant cutting force is developed. It is shown, by experimental cutting tests, that the cutting tool travels in the air cut with fast feed rate, yet in the varying depths of cut, the tool travels with an adjustable feed rate to prevent the occurrence of tool breakage and maintain a high metal removal rate.     

Prediction of chatter in NC machining based on a dynamic cutting force model for ball end milling

Ball end milling is one of the most widely used cutting processes in the automotive, aerospace, die/mold, and machine parts industries, and the chatter generated under unsuitable cutting conditions is an extremely serious problem as it causes excessive tool wear, noise, tool breakage, and deterioration of the surface quality. Due to the critical nature of detecting and preventing chatter, we propose a dynamic cutting force model for ball end milling that can precisely predict the cutting force for both stable and unstable cutting states because our uncut chip thickness model considers the back-side cutting effect in unstable cutting states. Furthermore, the dynamic cutting force model considers both tool runout and the penetration effect to improve the accuracy of its predictions. We developed software for calculating the cutting configuration and predicting the dynamic cutting force in general NC machining as well as single-path cutting. The chatter in ball end milling can be detected from the calculated cutting forces and their frequency spectra. A comparison of the predicted and measured cutting forces demonstrated that the proposed method provides accurate results.     

Fatigue failure of coated carbide tool and its influence on cutting performance in face milling SKD11 hardened steel

Failure patterns of coated carbide tool were investigated by high-speed face milling of the hardened steel SKD11. Tool failure surface morphology, cutting force and machined surface roughness were also analyzed to reveal the failure mechanisms. The results indicated that the dominant failure pattern of coated carbide tool was breakage. The primary mechanism of tool breakage was fatigue fracture. Under different cutting speeds, the distinctive morphologies of fatigue fracture were presented on the failure surfaces. At low cutting speeds, many fatigue sources were observed on the rake face. The distance between fatigue sources and tool nose was approximately two times of the depth of cut. With the increase of cutting speed, the fatigue striations and riven patterns were observed at the fracture surface. In addition, the fatigue steps and crack deflection were found under high cutting speeds. The main fracture mode was intergranular fracture at lower cutting speeds. However, it was transgranular fracture at higher cutting speeds. Furthermore, the irregular fracture surfaces at low cutting speeds and at high cutting speeds contribute to a larger cutting force increment compared with the medium cutting speeds. The increment of surface roughness in the initial and severe wear stages was lower than that in the steady wear stage, while the deviation of surface roughness was relatively large.     

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.     

Radial immersion angle estimation using cutting force and predetermined cutting force ratio in face milling

Radial immersion ratio is an important factor to determine the threshold for tool conditioning monitoring and automatic force regulation in face milling. In this paper, a method of on-line estimation of the radial immersion angle using cutting force is presented. When a tooth finishes sweeping, a sudden drop of cutting force occurs. This force drop is equal to the cutting force that acts on a single tooth at the swept angle of cut and can be obtained from the cutting force signal in feed and cross-feed directions. The ratio of cutting forces in feed and cross-feed directions acting on the single tooth at the immersion angle is a function of the immersion angle and the ratio of radial-to-tangential cutting force. In this study, it is found that the ratio of radial-to-tangential cutting force is not affected by cutting conditions and axial rake angle. Therefore, the ratio of radial-to-tangential cutting force determined by just one preliminary experiment can be used regardless of the cutting conditions for a given tool and workpiece material. Using the measured cutting force during machining and a predetermined ratio, the radial immersion ratio is estimated in the process. Various experiments show that the radial immersion ratio and instantaneous ratio of the radial to tangential direction cutting force can be estimated very well by the proposed method.