Cutting Force Signals Research Articles

A Model-Based Monitoring and Fault Diagnosis Methodology for Free-Form Surface Machining Process

In this paper, a process model-based approach has been proposed for monitoring and fault diagnosis in the multi-axis ball end milling process. Besides its ability to deal with complex cutting geometry in free-form surface machining, the method has the capability of not only detecting the presence but also estimating the magnitudes of faults, which include flute chipping, breakage and spindle/cutter axes runout. A threshold-based fault detection method is developed based on the analysis of harmonic power distribution in the cutting force signal. A genetic algorithm approach is used to search and determine the fault pattern and magnitudes. The new approach is validated through both constant cross-section cut and free-form surface machining tests on 1018 steel.

In-process estimation of radial immersion ratio in face milling using cutting force

In order to prevent tool breakage in milling, maximum total cutting force is regulated at a specific constant level, or threshold, through feed rate control. Since the threshold is a function of the immersion ratio, an estimation of the immersion ratio is necessary to flexibly determine the threshold. In this paper, a method of in-process estimation of the radial immersion ratio in face milling is presented. When an insert finishes sweeping, a sudden drop in cutting forces occurs. These force drops are equal to the cutting forces that act upon a single insert at the swept cutting angle and they can be acquired from cutting force signals in the feed and cross-feed directions. Average cutting forces per tooth period can also be calculated from the cutting force signals in two directions. The ratio of cutting forces acting upon a single insert at the swept angle of cut and the ratio of average cutting forces per tooth period are functions of the swept angle of cut and the ratio of radial to tangential cutting force. Using these parameters, the radial immersion ratio is estimated. Various experiments are performed to verify the proposed method. The results show that the radial immersion ratio can be estimated by this method regardless of other cutting conditions.

Microstructure-Level Force Prediction Model for Micro-milling of Multi-Phase Materials

A mechanistic model for the micro-endmilling process is developed that explicitly accounts for the different phases while machining heterogeneous materials. It is shown that frequencies in the cutting force signal higher than those that can be explained by the kinematics of the process can be explained by considering the multiple phases in the material. Experiments are performed on two compositions of ductile iron, pure ferrite and pearlite workpieces. These experiments show that the nature of the variation in the ductile iron cutting force signals can be attributed to the mixture of the phases. Additionally, simulation studies show that the frequency component of the variation is related to the spacing of the secondary (ferrite) phase and the magnitude of this component is determined by the size of the secondary phase particles.

Tool condition monitoring in broaching

The paper reports on research which attempts to correlate the condition of broaching tools to the output signals obtained from multiple sensors, namely, acoustic emission (AE), vibration, cutting forces and hydraulic pressure, connected to a hydraulic broaching machine. Broaching tools in a variety of conditions, namely, freshly ground teeth, one or more chipped teeth, one or more weakened teeth, one or more broken teeth and tools showing uniform wear, were investigated. Single tooth and multiple teeth broaching tools were employed during the broaching trials and the sensory signals were analysed both in the time and frequency domains. The results show that AE, vibration and cutting force signals are all sensitive to tool condition and a correlation can be made between the broaching tool condition and sensory signals using a variety of signal analysis techniques. A brief review of the advantages and the disadvantages of each sensor/signal and its associated analysis technique is presented. It is concluded that the most sensitive sensors to changes in tool condition are not necessarily those that are easy to mount or incorporate in a broaching machine. This can impose restrictions on the types of sensors that can be retrofitted to current machines. The condition monitoring system is currently being extended to incorporate sensor fusion techniques to increase the flexibility of the monitoring system and improve confidence in decision-making.

パラレル認識手法による切削加工監視の改善

In this paper, an approach is proposed to apply parallel decision-making systems on the same target, and to identify the cutting state synthetically based on evaluation of the results obtained by the plural subsystems. In the present work, a system was developed having two subsystems to estimate chipping of cutting edges during end milling. The subsystems employ artificial neural networks based on the cutting force signals. The chipping of the cutting edge was well identified for different cases of end milling. It is concluded that the flexibility and the reliability of the monitoring system is much improved by operating the subsystems in parallel as proposed in the present study.

401 Comprehensive Monitoring System for Cutting Process

One of the problems of current monitoring systems is lack of flexibility against changes in machining environment. The purpose of this research is to construct a monitoring system that can flexibly cope with changes in the machining environment without sacrificing the accuracy of detection. Developed monitoring system is conducted by plural decision-making systems, and is applied to monitoring of chipping of the end mill. The subsystems employ artificial neural networks based on the cutting force signals and the cutting conditions. It is concluded that the flexibility and the reliability of the monitoring system is much improved by operating the subsystems in parallel.

STATISTICAL ASSISTED FUZZY-NETS BASED IN-PROCESS SURFACE ROUGHNESS PREDICTION (S-FN-IPSRP) SYSTEM IN END MILLING OPERATIONS

ABSTRACT A statistical assisted fuzzy-nets (S-FN) based in-process surface roughness prediction (IPSRP) system using resultant cutting forces and cutting parameters has been developed. This system receives the instant cutting force signals and cutting parameters to predict surface roughness in a real time fashion. A statistical assisted fuzzy-nets learning procedure has been developed to construct the fuzzy rule bank for the proposed prediction model. The proposed S-FN-IPSRP system was developed using real time experimental data and the system also has been tested using 30 testing data sets in real time. The results demonstrated that the system has an average 94% accuracy rate.

Unmanned Turning Force Control Based on the Spindle Drive Characteristics.

While the rough turning process is machining a workpiece at various cutting depth, the feedrate is usually selected based on the maximum depth of cut. Even if this selection can avoid power saturation or tool breakage, it is very conservative compared to the capacity of machine tools and can reduce the productivity significantly. Many adaptive control techniques have been reported that can adjust the feedrate to maintain the constant cutting force. However, these controllers are not very widely used in manufacturing industry because of the limitations in measuring the cutting force signals and selecting the appropriate cutting force level. In this paper, an unmanned turning process control system is proposed based on the spindle drive characteristics. A synthesized cutting force monitor is introduced to estimate the cutting force as accurately as a dynamometer does. The reference cutting force level as well as the feed-rate is selected considering the spindle motor characteristics. Because the cutting process is highly nonlinear, a fuzzy logic controller is applied to maintain the desired cutting force level. The experimental results demonstrate that the proposed system can be easily realized in CNC lathe with requiring little additional hardware.

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.

The Application of an ANFIS and Grey System Method in Turning Tool-Failure Detection

The cutting process is a major material removal process; hence, it is important to search for ways of detecting tool failure. This paper describes the results of the application of an adaptive-network-based fuzzy inference system (ANFIS) for tool-failure detection in a single-point turning operation. In a turning operation, wear and failure of the tool are usually monitored by measuring cutting force, load current, vibration, acoustic emission (AE) and temperature. The AE signal and cutting force signal provide useful information concerning the tool-failure condition. Therefore, five input parameters of the combined signals (AE signal and cutting force signal) have been used in the ANFIS model to detect the tool state. In this model, we adopted three different types of membership function for analysis for ANFIS training and compared their differences regarding the accuracy rate of the tool-state detection. The result obtained for the successful classification of tool state with respect to only two classes (normal or failure) is very good. The results also indicate that a triangular MF and a generalised bell MF have a better rate of detection. We also applied grey relational analysis to determine the order of influence of the five cutting parameters on tool-state detection.

Tool Wear Monitoring in Turning: A Neural Network Application

Tool wear monitoring is the most difficult task in the area of tool condition monitoring for metal-cutting manufacturing processes. The main objective is to improve the process reliability, but the production costs need to be reduced as well. This article summarises a new approach for online and indirect tool wear estimation or classification in turning using neural networks. This technique uses a physical process model describing the influence of cutting conditions (such as the feed rate) on measured process parameters (here: cutting force signals) in order to separate signal changes caused by variable cutting conditions from signal changes caused by tool wear. Features extracted from the normalised process parameters are taken as inputs of a dynamic, but nonrecurrent neural network that estimates the current state of the tool. It is shown that the estimation error can be reduced significantly with this combination of a hard computing and a soft computing technique. The article represents an extended summary of the author's investigations and publications in the area of online and indirect tool wear monitoring in turning by means of artificial neural networks.

Correlation of cutting force features with tool wear in a metal turning operation

Interrupted turning has been conducted and the cutting forces recorded on-line while measurements of the cutting tool wear forms were made. The acquired data were analysed in the time and frequency domain for cutting tool wear correlation. The method employed was geared towards developing an understanding of the spectra energy content of the dynamic cutting force signals and how it could be used as an input sensor signal in the development of a tool wear monitoring system. Analysis of the data indicated that the spectra energy content correlated well with the measured tool wear at certain frequencies.

Prediction of the Amount of Tool Fracture in Face Milling Using Cutting Force Signal

Tool fracture index(TFI) was developed in order not only to detect tool fracture but also to predict the amount of tool fracture in face milling. TFI is calculated by using peak-to-valley values of cutting force acting on teeth and their ratio between the adjacent teeth. When the tool fractures, a large value of TFI proportional to the amount of tool fracture was obtained periodically and decreased gradually. It was found that TFI is independent of cutter runout and it almost does not vary during transient cutting such as cutting condition change during machining. The threshold of tool fracture can be analytically determined by TFI developed in this paper, because the magnitude of TFI was shown to be dependent on the ratio of the amount of tool fracture to feed per tooth and immersion ratio. It was possible to predict the amount of tool fracture in experiments by using the proposed TFI.

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 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.

On-line Estimation of Radial Immersion Ratio Using Cutting Force and Instantaneous Cutting Force Ratio in Face Milling

Radial immersion ratio is an important factor to determine the threshold in face milling and should be estimated in process for automatic force regulation. In this paper, presented is a method of on-line estimation of the radial immersion ratio using cutting force. When a tooth finishes sweeping, sudden drop of cutting forces 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 cutting force signal in feed and cross-feed direction. The ratio of cutting forces in feed and cross-feed directions acting on the single tooth at the swept angle of cut is a function of the swept angle of cut and the ratio of radial to tangential cutting force. In the research, 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. Using the measured cutting force and predetermined ratio, the radial immersion ratio is estimated. 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.

Cutting Force Estimation Systems Based on AC Spindle Drive.

Monitoring of the cutting force signals in turning process has been well emphasized for unmanned machining. Although the cutting force can be directly measured by a tool dynamometer, direct sensing in factory environment is not feasible because of high cost and limitations in setup. In this paper two indirect cutting force estimation systems are developed based on the measured output power and on the calculated input power signals, respectively. These estimation systems consider the cutting force as a disturbance input to the spindle drive and estimate the cutting force based on the inverse dynamic model. The inverse dynamic model represents the dynamic relation between the cutting force, the motor torque and the motor power. The proposed estimation systems are realized on a CNC lathe and their estimation performance is evaluated experimentally.

A matching pursuit approach to small drill bit breakage prediction

Wavelet-based pattern classification methods, which have been used extensively in imaging applications, have barely been explored in machining monitoring and diagnostic systems. Although the engineering literature recognizes wavelet methodologies as a practical alternative to the FFT for signals containing nonstationary information, most of it concerns only the Wavelet Transform. The WT can detect time-localized pre-failure machining signals, but it leaves two practical questions unanswered. The first is how to post-process the wavelet coefficients (the WT output) to extract the needed information. The second is what method to use for 'scanning' the signal with overlapping windows, since the WT covers only a short segment of the input signal. The Matching Pursuit (MP) approach handles both of these problems in a straightforward manner. As a pattern recognition method, it is adaptable to specific machining diagnostic tasks and eliminates the post-processing requirement. The scanning technique is likewise built into the algorithm, making customized windowing methods unnecessary. MP methods, which are widely used in imaging applications such as face recognition, are easily adapted to one-dimensional signals. Moreover, the MP framework can be tailored to the needs of specific applications through several parameters, such as the wavelet family, which are left to the user's discretion. In this paper, we present an MP implementation for small drill-bit breakage prediction. During bit failure, the small-bit thrust cutting force signal is characterized by two types of transients. We show that the MP detects these transients more effectively than an earlier method (also devised by the authors) based on the Discrete Wavelet Transform (DWT).

Prediction of small drill bit breakage by wavelet transforms and linear discriminant functions

Though the discrete wavelet transform (DWT) has gained attention in the field of machining monitoring, the potential uses of DWT-based classification techniques have not yet been fully explored. In this paper, linear discriminant analysis is used to post-process DWT output for on-line prediction of the breakage of small drill bits. Bit failure is characterized by two types of transient (`sawtooth' and `screeching') in the cutting force signal. To detect these transients, instead of traditional Fourier-based methods the DWT is used, which is better suited to analysis of time-localized phenomena. Three index functions (`energy', `waviness' and `irregularity') are adopted to test for the presence of transients in the DWT expansion. The indices are used to perform linear discriminant analysis, thereby classifying the input signals by state (normal or prefailure). Experiments showed that the DWT-based linear discriminant analysis method can accurately identify impending breakage about one to three cycles prior to failure, even when the cutting conditions change.

An investigation of indices based on milling force for tool wear in milling

Abstract In milling, the cutting force pulsates periodically as the inserts intermittently engage and disengage the workpiece with each revolution of the cutter. Several deterministic and stochastic factors contribute to the fluctuation in the cutting force. The resulting complex variations of the cutting force complicate attempts to correlate the tool wear with the measured force signal. Suitable techniques are required to derive effective indices of significant or severe milling tool wear from the cutting force. In this paper, three time series and statistical techniques used to determine tool wear indices from the milling force signals are discussed. From experimental tests in milling, it has been found that the flank wear of the insert is the prominent wear under normal cutting conditions. Hence, the standard criterion that determines significant flank wear of a cutting insert is used to set the corresponding threshold values for the tool wear indices derived from the cutting force signal. The effectiveness of the three techniques in providing reliable indices of significant tool flank wear under conditions of run-out, cutting transients, and changes of cutting conditions are compared over a range of milling conditions.