An effective sensor for tool wear monitoring in face milling: Acoustic emission

Acoustic Emission (AE) has been widely used for monitoring manufacturing processes particularly those involving metal cutting. Monitoring the condition of the cutting tool in the machining process is very important since tool condition will affect the part size, quality and an unexpected tool failure may damage the tool, work-piece and sometimes the machine tool itself. AE can be effectively used for tool condition monitoring applications because the emissions from process changes like tool wear, chip formation i.e. plastic deformation, etc. can be directly related to the mechanics of the process. Also AE can very effectively respond to changes like tool fracture, tool chipping, etc. when compared to cutting force and since the frequency range is much higher than that of machine vibrations and environmental noises, a relatively uncontaminated signal can be obtained. AE signal analysis was applied for sensing tool wear in face milling operations. Cutting tests were carried out on a vertical milling machine. Tests were carried out for a given cutting condition, using single insert, two inserts (adjacent and opposite) and three inserts in the cutter. AE signal parameters like ring down count and rms voltage were measured and were correlated with flank wear values (VB max). The results of this investigation indicate that AE can be effectively used for monitoring tool wear in face milling operations.     

Tool condition monitoring in milling using vibration analysis

Monitoring the condition of cutting tools in any machining operation is very important to avoid unexpected machining trouble and improve machining accuracy. This paper presents the use of vibration analysis of the cutting process in milling to indicate the presence and progression of damage incurred by an end mill. The metal cutting experiments were performed on a mild steel workpiece without using any coolant to accelerate damage to cutter, and classical processing schemes in time and frequency domains were applied to the resulting vibrations of cutting process to obtain diagnostic information. Moreover, developing fault features were also illustrated using both scalogram and its mean frequency variation. It has been found that scalogram and its mean frequency are both capable of revealing the features of not only localized, but progressive fault more clearly in the presence of strong noise than conventional time and frequency domain analyses. Furthermore, the global average of the mean frequency variation provides a useful indicator signifying the progression of wear, whereas time domain statistics do not give any consistent trend.     

Sensor fusion for online tool condition monitoring in milling

The objective of this paper is to combine a direct sensor (vision) and an indirect sensor (force) to create an intelligent integrated tool condition monitoring (TCM) system for online monitoring of flank wear and breakage in milling, using the complementary strengths of the two types of sensors. For flank wear, images of the tool are captured and processed in-cycle using successive moving-image analysis. Two features of the cutting force, which closely indicate flank wear, are extracted in-process and appropriately pre-processed. A self-organizing map (SOM) network is trained in a batch mode after each cutting pass, using the two features derived from the cutting force, and measured wear values obtained by interpolating the vision-based measurement. The trained SOM network is applied to the succeeding machining pass to estimate the flank wear in-process. The in-cycle and in-process procedures are employed alternatively for the online monitoring of the flank wear. To detect breakage, two features in time domain derived from cutting force are used, and the thresholds for them are determined dynamically. Again, vision is used to verify any breakage identified in-process through the cutting force monitoring. Experimental results show that this sensor fusion scheme is feasible and effective for the implementation of online tool condition monitoring in milling, and is independent of the cutting conditions used.     

On-line condition monitoring of tool wear in end milling using acoustic emission

The monitoring of tool wear is important in maintaining the quality of workpieces produced. This paper presents a methodology to monitor on-line tool wear in end milling using acoustic emission. It is well known that the root-mean-square (RMS) value of the acoustic emission is directly proportional to the power expended in turning. A mathematical model has been developed to predict the RMS value of the acoustic-emission signal in milling. This mathematical model incorporates the machining parameters as variables. The accuracy of the model has been verified by a series of experiments. The experiments were carried out on a Bridgeport milling machine and data was collected and analysed by using an on-line computer data acquisition system. A comparison of the experimental and theoretical RMS values indicates a very good agreement between them. A control strategy similar to the moving average/moving range charts has been developed for monitoring on-line tool wear. The limits for the control charts were obtained from the theoretical equation of statistical quality control. An observation of the control charts clearly indicates the region of tool failure and the time at which the tool failed. The philosophy behind the use of control charts is based on the ease of implementation and widespread use in industry.     

Improved performance evaluation of tool condition identification by manufacturing loss consideration

When neural networks are used to identify tool states in machining processes, the main interest is often the recognition ability. It is usually believed that a higher classification rate from pattern recognition can improve the accuracy and reliability of tool condition monitoring, thereby reducing the manufacturing loss. Nevertheless, the two objectives are not identical in most practical manufacturing systems. The aim is to address this issue and propose a new performance evaluation function so that the recognition ability of tool condition monitoring can be evaluated more reasonably. On this basis, two kinds of manufacturing loss due to misclassification are analysed: the over-prediction caused by misclassifying the worn tool condition; and the under-prediction caused by misclassifying the fresh tool condition. By using both to calculate corresponding weights in the performance evaluation function, the potential manufacturing loss is introduced to evaluate the recognition performance of tool condition monitoring. Based on this performance evaluation function, a modified support vector machine approach with two regularization parameters is employed to learn the information of every tool state. In this support vector machine design, the effective feature set extracted from acoustic emission signals is used as inputs, and a five-fold cross-validation is used to tune the parameters. The experimental results show that the proposed method can reliably identify tool flank wear and reduce the overdue prediction of worn tool conditions and its relative loss. Experimental results show that this approach may effectively identify tool state over a range of cutting conditions and reduce the manufacturing loss in the practical industry process.     

A data acquisition system for machine tool condition monitoring. Part 2—some of the signals acquired

This paper presents some of the machine tool condition monitoring signals logged by the data acquisition system (DAS) designed and implemented as part of the European Union research project BREU-CT91–0463. Cause and effect relationships between machine components and signal features are highlighted, along with a demonstration of the suitability of vibration signal kurtosis as a measure of machine tool spindle bearing condition.     

Vibration — A tool for machine diagnostics and condition monitoring

Vibration is an effective tool in detecting and diagnosing some of the incipient failures of machines and equipment. The present paper deals with the basic principles, which may help in identifying its diagnostic ability, the scope of its diagnostic capabilities, the instrumentation in vogue for its monitoring and the state-of-the-art of the monitoring techniques and programs. A few case studies are also given to illustrate how machine troubles/failures are diagnosed with the help of vibration signatures.     

Differential evolution-based feature selection and parameter optimisation for extreme learning machine in tool wear estimation

Cutting tool wear degrades the product quality in manufacturing processes. Hence, real-time online estimation of tool wear is important for suggesting a tool replacement before the wear limit is reached, in order to protect the workpiece and the CNC machine from damage and breakdown. In this study, using both statistical features and wavelet features extracted from sensor signals, an adaptive evolutionary extreme learning machine (ELM) learning paradigm is developed for tool wear estimation in high-speed milling process. In the proposed method, a discrete differential evolution (DE) algorithm is used to select input features for the ELM, and a continuous DE algorithm is used for parameter optimisation of the mixed kernel function for the ELM. The experimental results indicate that the proposed adaptive evolutionary ELM-based tool wear estimation model can effectively estimate the tool wear in high-speed milling process. Empirical comparisons show that the proposed model performs better than existing approaches in estimating the tool wear.