The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
N. Mohan Rao - One of the best experts on this subject based on the ideXlab platform.
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Cutting Tool Condition monitoring by analyzing surface roughness, work piece vibration and volume of metal removed for AISI 1040 steel in boring
Measurement: Journal of the International Measurement Confederation, 2013Co-Authors: K. Venkata Rao, B. S N Murthy, N. Mohan RaoAbstract:The vibration is one of the intensive problems in boring process. Machining and Tool wear are affected more by vibration of Tool due to length of boring bar. The present work is to estimate the effect of Cutting parameters on work piece vibration, roughness on machined surface and volume of metal removed in boring of steel (AISI1040). A laser Doppler vibrometer (LDV) was used for online data acquisition and a high-speed FFT analyzer used to process the AOE signals for work piece vibration. A design of experiments was prepared with eight experiments with two levels of Cutting parameters such as spindle rotational speed, feed rate and Tool nose radius. Taguchi method has been used to optimize the Cutting parameters and a multiple regression analysis is done to obtain the empirical relation of Tool life with roughness of machined surface, volume of metal removed and amplitude of work piece vibrations. © 2013 Elsevier Ltd. All rights reserved.
A. D. Hope - One of the best experts on this subject based on the ideXlab platform.
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a hybrid pattern recognition architecture for Cutting Tool Condition monitoring
2008Co-Authors: Pan Fu, A. D. HopeAbstract:One of the important developments in modern manufacturing industry has been the trend towards cost savings through stuff reductions whilst simultaneously improving the product quality. Traditional Tool change strategies are based on very conservative estimates of Tool life from past Tool data and this leads to a higher Tool change frequency and higher production costs. Intelligent sensor based manufacturing provides a solution to this problem by coupling various transducers with intelligent data processing techniques to deliver improved information relating to Tool Condition. This makes optimization and control of the machining process possible. Many researchers have published results in the area of automatic Tool Condition monitoring. The research work of Scheffer C. etc. showed that proper features for a wear monitoring model could be generated from the Cutting force signal, after investigating numerous features. An approach was developed to use feed force measurements to obtain information about Tool wear in lathe turning (Balazinski M. etc.). An analytical method was developed for the use of three mutually perpendicular components of the Cutting forces and vibration signature measurements (Dimla D. E. etc.). A Tool Condition monitoring system was then established for Cutting Tool-state classification (Dimla D. E. etc.). In another study, the input features were derived from measurements of acoustic emission during machining and topography of the machined surfaces (Wilkinson P. Etc.). Li, X etc. showed that the frequency distribution of vibration changes as the Tool wears (Li X. etc.). Tool breakage and wear Conditions were monitored in real time according to the measured spindle and feed motor currents, respectively (LI X. L. Etc. ). Advanced signal processing techniques and artificial intelligence play a key role in the development of Tool Condition monitoring systems. Sensor fusion is also found attractive since loss of sensitivity of one of the sensors can be compensated by other sensors. A new on-line fuzzy neural network (FNN) model with four parts was developed (Chungchoo C. etc.). They have the functions of classifying Tool wear by using fuzzy logic; normalizing the inputs; using modified least-square back propagation neural network to estimate flank and crater wear. A new approach for online and indirect Tool wear estimation in turning using neural networks was developed, using a physical process model describing the influence of Cutting Conditions on measured process parameters (Sick B.). Two methods using Hidden O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m
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Intelligent Cutting Tool Condition monitoring based on a hybrid pattern recognition architecture
2008 International Conference on Machine Learning and Cybernetics, 2008Co-Authors: Pan Fu, A. D. HopeAbstract:In manufacturing processes it is very important that the Condition of the Cutting Tool, particularly the indications when it should be changed, can be monitored. Cutting Tool Condition monitoring is a very complex process and thus sensor fusion techniques and artificial intelligence signal processing algorithms are employed in this study. The multi-sensor signals reflect the Tool Condition comprehensively. A unique fuzzy neural hybrid pattern recognition algorithm has been developed. The weighted approaching degree can measure difference of signal features accurately and the neurofuzzy network combines the transparent representation of fuzzy system with the learning ability of neural networks. The algorithm has strong modeling and noise suppression ability. These leads to successful Tool wear classification under a range of machining Conditions.
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ICNC (2) - A Neural-fuzzy Pattern recognition Algorithm based Cutting Tool Condition Monitoring Procedure
Third International Conference on Natural Computation (ICNC 2007), 2007Co-Authors: Pan Fu, A. D. HopeAbstract:Cutting Tool Condition monitoring is the key technique for realizing automatic and "un-manned" manufacturing processes. This project applies Cutting force and acoustic emission transducers to monitor metal Cutting processes. A B-spline neurofuzzy networks based Tool wear state monitoring model has been presented. The model can accurately describe the nonlinear relation between the Tool wear value and signal features. Compared with the normal neural networks, such as BP type ANNs, this model has the advantages of fast convergence and having local learning capabilities. Large amounts of monitoring experiments show that the application of B-spline neurofuzzy networks can improve the accuracy and reliability of the Tool wear Condition monitoring processes.
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a neural fuzzy pattern recognition algorithm based Cutting Tool Condition monitoring procedure
International Symposium on Neural Networks, 2007Co-Authors: Pan Fu, A. D. HopeAbstract:An intelligent Tool wear monitoring system for metal Cutting process will be introduced in this paper. The system is equipped with four kinds of sensors, signal transforming and collecting apparatus and a micro computer. A knowledge based intelligent pattern recognition algorithm has been developed. The fuzzy driven neural network can carry out the integration and fusion of multi-sensor information. The weighted approaching degree can measure the difference of signal features accurately and ANNs successfully recognize the Tool wear states. The algorithm has strong learning and noise suppression ability. This leads to successful Tool wear classification under a range of machining Conditions.
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ISNN (2) - A Neural-Fuzzy Pattern Recognition Algorithm Based Cutting Tool Condition Monitoring Procedure
Advances in Neural Networks – ISNN 2007, 2007Co-Authors: Pan Fu, A. D. HopeAbstract:An intelligent Tool wear monitoring system for metal Cutting process will be introduced in this paper. The system is equipped with four kinds of sensors, signal transforming and collecting apparatus and a micro computer. A knowledge based intelligent pattern recognition algorithm has been developed. The fuzzy driven neural network can carry out the integration and fusion of multi-sensor information. The weighted approaching degree can measure the difference of signal features accurately and ANNs successfully recognize the Tool wear states. The algorithm has strong learning and noise suppression ability. This leads to successful Tool wear classification under a range of machining Conditions.
Tamas Szecsi - One of the best experts on this subject based on the ideXlab platform.
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a dc motor based Cutting Tool Condition monitoring system
Journal of Materials Processing Technology, 1999Co-Authors: Tamas SzecsiAbstract:Abstract In this paper a Cutting Tool Condition monitoring system for CNC lathes is presented. The system is based on the measurement of the main DC motor current of the lathes. It consists of current and rotation speed sensors, a Cutting Tool – part touch sensor, analogue memory, amplifiers, filters and a personal computer. The system is capable of functioning in three modes depending on the machined surface. It can take into account the part diameter variation and the acceleration of the spindle. The system is trained by a genetic algorithm based fuzzy rule set. The monitoring system can be used in an unmanned manufacturing environment.
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Cutting force modeling using artificial neural networks
Journal of Materials Processing Technology, 1999Co-Authors: Tamas SzecsiAbstract:Abstract Modeling of Cutting forces has always been one of the main problems in metal Cutting theory. The large number of interrelated parameters that influence the Cutting forces (Cutting speed, feed, depth of cut, primary and secondary Cutting edge angles, rake angle, nose radius, clearance angle, Cutting edge inclination angle, Cutting Tool wear, physical and chemical characteristics of the machined part, etc.) makes it extremely difficult to develop a proper model. Although an enormous amount of Cutting force related data is available in machining handbooks, most of them attempt to define the relationship between a few of the possible Cutting parameters whilst fixing the other parameters. Also, proper mechanisms for extracting general models from existing machining data are still to be developed. In this paper an approach for modeling Cutting forces with the help of artificial neural networks is proposed. Feed-forward multi-layer neural networks, trained by the error back-propagation algorithm are used. The training of the networks is performed with experimental machining data. The developed model can be used for simulation purposes and to define threshold force values in Cutting Tool Condition monitoring systems.
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automatic Cutting Tool Condition monitoring on cnc lathes
Journal of Materials Processing Technology, 1998Co-Authors: Tamas SzecsiAbstract:Abstract This paper addresses the problem of automatically defining Cutting-Tool wear on CNC lathes. The problem is solved by analysing the axial force during the pressing of the Cutting edge of an insert into the workpiece while the Cutting process is stopped. During machining the feed movement is interrupted and the position of the Cutting Tool at this moment becomes a reference point. With no feed movement the rotation of the workpiece continues for a few revolutions, the Cutting edge being moved off the transient surface in the axial direction, after which the rotation stops. With slow axial movement the Cutting edge is pressed into the surface of the workpiece and at the same time the pressing depth relative to the reference point and the axial force are measured. Analysis of the results shows a close relationship between the axial force, measured at a fixed pressing depth and the flank wear of the Cutting Tool. The paper also presents some results on Cutting-Tool Condition monitoring using artificial neural networks.
K. Venkata Rao - One of the best experts on this subject based on the ideXlab platform.
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Cutting Tool Condition monitoring by analyzing surface roughness, work piece vibration and volume of metal removed for AISI 1040 steel in boring
Measurement: Journal of the International Measurement Confederation, 2013Co-Authors: K. Venkata Rao, B. S N Murthy, N. Mohan RaoAbstract:The vibration is one of the intensive problems in boring process. Machining and Tool wear are affected more by vibration of Tool due to length of boring bar. The present work is to estimate the effect of Cutting parameters on work piece vibration, roughness on machined surface and volume of metal removed in boring of steel (AISI1040). A laser Doppler vibrometer (LDV) was used for online data acquisition and a high-speed FFT analyzer used to process the AOE signals for work piece vibration. A design of experiments was prepared with eight experiments with two levels of Cutting parameters such as spindle rotational speed, feed rate and Tool nose radius. Taguchi method has been used to optimize the Cutting parameters and a multiple regression analysis is done to obtain the empirical relation of Tool life with roughness of machined surface, volume of metal removed and amplitude of work piece vibrations. © 2013 Elsevier Ltd. All rights reserved.
B. S N Murthy - One of the best experts on this subject based on the ideXlab platform.
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Cutting Tool Condition monitoring by analyzing surface roughness work piece vibration and volume of metal removed for aisi 1040 steel in boring
Measurement, 2013Co-Authors: B. S N MurthyAbstract:Abstract The vibration is one of the intensive problems in boring process. Machining and Tool wear are affected more by vibration of Tool due to length of boring bar. The present work is to estimate the effect of Cutting parameters on work piece vibration, roughness on machined surface and volume of metal removed in boring of steel (AISI1040). A laser Doppler vibrometer (LDV) was used for online data acquisition and a high-speed FFT analyzer used to process the AOE signals for work piece vibration. A design of experiments was prepared with eight experiments with two levels of Cutting parameters such as spindle rotational speed, feed rate and Tool nose radius. Taguchi method has been used to optimize the Cutting parameters and a multiple regression analysis is done to obtain the empirical relation of Tool life with roughness of machined surface, volume of metal removed and amplitude of work piece vibrations.
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Cutting Tool Condition monitoring by analyzing surface roughness, work piece vibration and volume of metal removed for AISI 1040 steel in boring
Measurement: Journal of the International Measurement Confederation, 2013Co-Authors: K. Venkata Rao, B. S N Murthy, N. Mohan RaoAbstract:The vibration is one of the intensive problems in boring process. Machining and Tool wear are affected more by vibration of Tool due to length of boring bar. The present work is to estimate the effect of Cutting parameters on work piece vibration, roughness on machined surface and volume of metal removed in boring of steel (AISI1040). A laser Doppler vibrometer (LDV) was used for online data acquisition and a high-speed FFT analyzer used to process the AOE signals for work piece vibration. A design of experiments was prepared with eight experiments with two levels of Cutting parameters such as spindle rotational speed, feed rate and Tool nose radius. Taguchi method has been used to optimize the Cutting parameters and a multiple regression analysis is done to obtain the empirical relation of Tool life with roughness of machined surface, volume of metal removed and amplitude of work piece vibrations. © 2013 Elsevier Ltd. All rights reserved.
