Online Tool Condition Monitoring System Research Articles

A simple online tool condition monitoring system using artificial neural networks

During machining occurrence of tool wear is a common phenomenon. As tool wear increases, rubbing of flank surface and workpiece also gets increased. Then, the desired quality of workpiece is not possible to achieve. To minimize this loss of quality, a cutting tool should be changed or ground after reaching certain amount of average flank wear (0.3 mm for uniform and 0.6 mm or non-uniform flank wear). For the detection of worn out state of a cutting tool, condition monitoring is required. During past decades, a lot of research works had been done on both offline and online monitoring of cutting tool. Most of the researchers used high cost setups and sensors for wear detection purpose. In this work, tool wear is detected using spindle speed as the wear detection parameter. Artificial Neural Networks is used as data analysing tool. Back propagation algorithm is used as learning algorithm. Results show that proposed methodology is capable to detect tool wear satisfactorily.

Application of spindle power signals in tool condition monitoring based on HHT algorithm

In the present study, we present the application of spindle power signals in tool condition monitoring (TCM) under different cutting conditions based on the Hilbert-Huang transform (HHT) algorithm. We extracted two features from the original collected data using the HHT algorithm to detect the tool wear and conducted six sets of cutting experiments to verify the feasibility of this tool condition monitoring method. The results show that these features are highly correlated with the wear state of cutting tools, regardless of the cutting parameters, workpiece materials, and machining methods. The calculated correlation coefficients between the extracted features and the actual tool wear reach 0.79–0.98. This demonstrates that the HHT algorithm is suitable for extracting features from the spindle power signals to construct the online tool condition monitoring system.

Machine learning based tool condition classification using acoustic emission and vibration data in high speed milling process using wavelet features

It is important to develop an intelligent tool condition monitoring system to increase productivity and promoting automation in metal cutting process. Many attempts have been made in the past to develop such systems using signals from various sensors such as dynamometer, current, accelerometer, aco ustic emission, current and voltage, etc. But the successes of different sensor based systems are limited due to the complexity of tool wear process. The research is still ongoing for improved tool condition monitoring system with applications of advance signal processing techniques and artificial intelligent models. In this study, tool conditions are monitored using the vibration and acoustic emission signatures during high speed machining of titanium alloy (Ti-6Al-4V). Using discrete wavelet transforms wavelets coefficients of vibration and acoustic emission signals are extracted using haar, daubechies, biorthogonal and reverse biorthogonal wavelets. Machine learning algorithms such as decision tree, naive bayes, support vector machine and artificial neural networks are used to predict the tool condition. Results indicate the effectiveness of acoustic emission and vibration data using wavelets for classifying the tool conditions with the aid of machine learning algorithms. A correlation is established between the tool conditions and sensor data. Support vector machine trained by vibration data appears to be predicting the tool conditions with good accuracy compared to decision trees, naive bayes and artificial neural network. Results obtained in this study will be useful to develop an intelligent on-line tool condition monitoring system.

A multi-sensor based online tool condition monitoring system for milling process

Tool condition monitoring has been considered as one of the key enabling technologies for manufacturing optimization. Due to the high cost and limited system openness, the relevant developed systems have not been widely adopted by industries, especially Small and Medium-sized Enterprises. In this research, a cost-effective, wireless communication enabled, multi-sensor based tool condition monitoring system has been developed. Various sensor data, such as vibration, cutting force and power data, as well as actual machining parameters, have been collected to support efficient tool condition monitoring and life estimation. The effectiveness of the developed system has been validated via machining cases. The system can be extended to wide manufacturing applications.

Statistical Analysis of Deep Drilling Process Conditions Using Vibrations and Force Signals

Cooling systems is a key point for hot forming process of Ultra High Strength Steels (UHSS). Normally, cooling systems is made using deep drilling technique. Although deep twist drill is better than other drilling techniques in term of higher productivity however its main problem is premature tool breakage, which affects the production quality. In this paper, analysis of deep twist drill process parameters such as cutting speed, feed rate and depth of cut by using statistical analysis to identify the tool condition is presented. The comparisons between different two tool geometries are also studied. Measured data from vibrations and force sensors are being analyzed through several statistical parameters such as root mean square (RMS), mean, kurtosis, standard deviation and skewness. Result found that kurtosis and skewness value are the most appropriate parameters to represent the deep twist drill tool conditions behaviors from vibrations and forces data. The condition of the deep twist drill process been classified according to good, blunt and fracture. It also found that the different tool geometry parameters affect the performance of the tool drill. It believe the results of this study are useful in determining the suitable analysis method to be used for developing online tool condition monitoring system to identify the tertiary tool life stage and helps to avoid mature of tool fracture during drilling process.

Tool Condition Monitoring System: A Review

Increasing demands of process automation for un-manned manufacturing attracted many researchers in the field of on-line monitoring of machining processes. In view of this, extensive research work is taking place world-wide in the area of on-line tool condition monitoring system (TCMS). Tool wear is the most undesirable characteristic of machining processes as it adversely affects the tool life, which is of foremost importance in metal cutting owing to its direct impact on the surface quality of the machined surface, and its dimensional accuracy, and consequently, the economics of machining operations. Therefore, methods for cutting tool wear sensing are crucial in view of the optimum use of cutting tools. With an effective monitoring system, the damages to the machine tool, downtime and scrapped components can be avoided. This paper provides brief overview on tool condition monitoring.

Design of a TCM System Based on Vibration Signal for Metal Turning Processes

This paper presents a study about the identification of cutting tool wear state by means of vibration signal analysis in steel dry turning operations. Analyzing the RMS value evolution and FFT frequency spectra of signal an on-line tool condition monitoring system has been developed. The main purpose is to determine the instant from which tool condition is considered unacceptable affecting machining process quality. After the analysis was carried out, the main conclusion is that both the RMS and frequency amplitude ranges of certain spectrum bands are related to tool wear.

A support vector machine-based online tool condition monitoring for milling using sensor fusion and a genetic algorithm

In machining systems, the quality of the manufactured part is directly related to the condition of the tool used. Sharp tools are mostly used on the final machining pass to obtain enhanced dimensional accuracy and surface smoothness. Worn tools on the other hand are typically used for coarse machining. The operator usually makes tool assignments based on his experience, the wear levels of the tools and the type of machining task. However, this kind of operator judgment is bound to errors and may not be reliable in processes requiring high precision. Therefore, a tool condition monitoring system is highly desirable to achieve the best results in machining quality. In this study, three-axis cutting forces, torque, three-axis accelerometer and acoustic emission signals were analyzed and used for the development of an online tool condition monitoring system. Various time domain and statistical features extracted from these signals were used to train support vector machine models in a binary decision tree, which was used to predict the condition of the cutting tool. The genetic algorithm was employed for reducing the dimensionality of the feature set by selecting the features that correlates best with the tool condition. Nine experiments were carried out at different cutting conditions. Experimental results demonstrated the efficacy of the proposed scheme. The classification rates for the tool condition monitoring system before and after inclusion of the genetic algorithm step were determined as 89% and 100%, respectively.

Tool wear monitoring in bandsawing using neural networks and Taguchi’s design of experiments

The bandsawing as a multi-point cutting operation is the preferred method for cutting off raw materials in industry. Although cutting off with bandsaw is very old process, research efforts are very limited compared to the other cutting process. Appropriate online tool condition monitoring system is essential for sophisticated and automated machine tools to achieve better tool management. Tool wear monitoring models using artificial neural network are developed to predict the tool wear during cutting off the raw materials (American Iron and Steel Institute 1020, 1040 and 4140) by bandsaw. Based on a continuous data acquisition of cutting force signals, it is possible to estimate or to classify certain wear parameters by means of neural networks thanks to reasonably quick data-processing capability. The multi-layered feed forward artificial neural network (ANN) system of a 6 × 9 × 1 structure based on cutting forces was trained using error back-propagation training algorithm to estimate tool wear in bandsawing. The data used for the training and checking of the network were derived from the experiments according to the principles of Taguchi design of experiments planned as L27. The factors considered as input in the experiment were the feed rate, the cutting speed, the engagement length and material hardness. 3D surface plots are generated using ANN model to study the interaction effects of cutting conditions on sawblade. The analysis shows that cutting length, hardness and cutting speed have significant effect on tooth wear, respectively, while feed rate has less effect. In this study, the details of experimentation and ANN application to predict tooth wear have been presented. The system shows that there is close match between the flank wear estimated and measured directly.

Real-time tool condition monitoring of face milling using acousto-optic emission â an experimental approach

In metal cutting, as a result of the cutting motion, the surface of work piece will be influenced by cutting parameters, cutting force, vibrations, etc. The effects of vibrations have been paid little attention. Accurate detection of the tool conditions under given cutting conditions is very important so that worn tools can be identified and replaced in time. Objective of the present work is to predict the effects of displacements due to vibration during face milling and to examine the correlation of surface roughness along with progression of tool wear at different machining combinations so as to develop a base for online tool condition monitoring system. A laser doppler vibrometer and FFT analyser are used for online data acquisition and subsequent processing of signals. The encouraging results of the work pave the way for the development of a real-time and reliable tool-condition-monitoring system.

The design of a single-chip tool monitoring system for on-line turning operation

Tool condition monitoring systems play an important role in a FMS system. By changing the worn tool before or just at the time it fails, the loss caused by defect product can be reduced greatly and thus product quality and reliability is improved. To achieve this, an on-line tool condition monitoring system using a single-chip microcomputer for detecting tool breakage during cutting process is discussed in this paper. Conventionally, PC-based monitoring systems are used in most research works. The major shortcoming of PC-based monitoring systems is the incurred cost. To reduce costs, the tool condition monitoring system was built with an Intel 8051 single-chip microprocessor and the design is described in this paper. The 8051 tool monitoring system uses a strain gauge for measuring cutting force; according to the force feature, the tool monitoring system can easily recognize the breakage of the cutting tool with its tool breakage algorithm. The experimental results show that the low-cost 8051 tool monitoring board can detect tool breakage in three successive products successfully.

On-line tool condition monitoring and control system in forging processes

Cold bulk metal forming has made large-scale production of small complex solid parts economically feasible. Tooling used in metal forming poses many uncertainties in the preliminary cost estimation and production process and continual tool replacement and maintenance dramatically reduces productivity and raises manufacturing cost. In order to tackle this, an on-line tool condition monitoring system using artificial neural network (ANN) to integrate information from multiple sensors for forging process has been developed. Together with the force, acoustic emission signals and process conditions, information developed from theoretical models is integrated into the ANN tool monitoring system to predict tool life and provide the maintenance schedule.

On-line tool condition monitoring system with wavelet fuzzy neural network

In manufacturing systems such as flexible manufacturing systems (FMS), one of the most important issues is accurate detection of the tool conditions under given cutting conditions. An investigation is presented of a tool condition monitoring system (TCMS), which consists of a wavelet transform preprocessor for generating features from acoustic emission (AE) signals, followed by a high speed neural network with fuzzy inference for associating the preprocessor outputs with the appropriate decisions. A wavelet transform can decompose AE signals into different frequency bands in the time domain. The root mean square (RMS) values extracted from the decomposed signal for each frequency band were used as the monitoring feature. A fuzzy neural network (FNN) is proposed to describe the relationship between the tool conditions and the monitoring features; this requires less computation than a back propagation neural network (BPNN). The experimental results indicate the monitoring features have a low sensitivity to changes of the cutting conditions and FNN has a high monitoring success rate in a wide range of cutting conditions; TCMS with a wavelet fuzzy neural network is feasible.