Journal ArticleDOI
Acoustic emission for tool condition monitoring in metal cutting
TLDR
In this article, an attemopt is made to extract maximum information from acoustic emission (AE) signals acquired during machining, and a statistical method, the time series modelling technique, is used to extract parameters called features representing the state of the cutting process.About:
This article is published in Wear.The article was published on 1997-11-30. It has received 133 citations till now. The article focuses on the topics: Cutting tool & Acoustic emission.read more
Citations
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Journal ArticleDOI
A brief review: acoustic emission method for tool wear monitoring during turning
TL;DR: A review of acoustic emission (AE)-based tool wear monitoring in turning is presented in this article, where the main contents included: 1. AE generation in metal cutting processes, AE signal classification, and AE signal correction, and 2. AE signal processing with various methodologies, including time series analysis, FFT, wavelet transform, etc.
Journal ArticleDOI
A review of machining monitoring systems based on artificial intelligence process models
TL;DR: In this paper, the authors present a generic view of machining monitoring systems and facilitate their implementation, and present six key issues involved in the development of intelligent machining systems: (1) the different sensor systems applied to monitor machining processes, (2) the most effective signal processing techniques, (3) most frequent sensory features applied in modelling machining process, (4) the sensory feature selection and extraction methods for using relevant sensory information, (5) the design of experiments required to model a machining operation with the minimum amount of experimental data and (6) the
Journal ArticleDOI
Estimation of tool wear during CNC milling using neural network-based sensor fusion
Nirmalya Ghosh,Y.B. Ravi,Amit Patra,Siddhartha Mukhopadhyay,Soumitra Paul,Amiya R Mohanty,A.B. Chattopadhyay +6 more
TL;DR: In this article, a neural network-based sensor fusion model has been developed for tool condition monitoring (TCM), where features extracted from a number of machining zone signals, namely cutting forces, spindle vibration, spindles current, and sound pressure level have been fused to estimate the average flank wear of the main cutting edge.
Journal ArticleDOI
Hidden Markov model-based tool wear monitoring in turning
TL;DR: Experimental results show that successful tool state detection rates as high as 97% can be achieved by using the proposed new modeling framework for tool wear monitoring in machining processes using hidden Markov models.
Journal ArticleDOI
In-process tool condition monitoring in compliant abrasive belt grinding process using support vector machine and genetic algorithm
TL;DR: In this paper, the authors proposed a tool condition monitoring predicting system, which not only helps to optimise the utilisation of the tool's life cycle but also secures the surface quality of finished components.
References
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Book
Pattern recognition principles
Julius T. Tou,Rafael C. Gonzalez +1 more
TL;DR: The present work gives an account of basic principles and available techniques for the analysis and design of pattern processing and recognition systems.
Journal ArticleDOI
A Critical Review of Sensors for Unmanned Machining
J. Tlusty,G.C. Andrews +1 more
TL;DR: In this paper, a survey of commercially available sensors for unmanned machining is presented, including dimensional and proximity sensors, cutting force, spindle force and feed force sensors, and spindle motor (torque and power) sensors.
Journal ArticleDOI
Sensor Integration Using Neural Networks for Intelligent Tool Condition Monitoring
S.S. Rangwala,David Dornfeld +1 more
Journal ArticleDOI
Tool Wear Detection Using Time Series Analysis of Acoustic Emission
S. Y. Liang,David Dornfeld +1 more
TL;DR: Monitoring of cutting tool wear based on time series analysis of acoustic emission signals suggests that tool wear detection can be achieved by monitoring the evolution of the model parameter vector during machining processes.
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Tool Wear Detection Using Time Series Analysis of Acoustic Emission
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