Tool Wear Identification in Turning Titanium Alloy Based on SVM

2014 ◽  
Vol 800-801 ◽  
pp. 446-450 ◽  
Author(s):  
Zhi Rong Liao ◽  
Sheng Ming Li ◽  
Yong Lu ◽  
Dong Gao
Keyword(s):  
Support Vector Machine ◽  
Acoustic Emission ◽  
Titanium Alloy ◽  
Tool Wear ◽  
Cutting Tool ◽  
Support Vector ◽  
Test Set ◽  
Tool Condition ◽  
Good Classification ◽  
Ae Signal

Titanium alloy is difficult cutting materials,the samples of toolwear features are hard to acquire because of short tool life. In terms of the characteristic, Support Vector Machine (SVM) is proposed in this paper to monitor tool condition, the energy ratio of six different frequency bands of acoustic emission (AE) signal are extracted as cutting tool state features , SVM is trained and tested using these features ,Good classification results were achieved by using test set.

Acoustic Emission-Based Tool Condition Classification in a Precision High-Speed Machining of Titanium Alloy: A Machine Learning Approach

2018 ◽  
Vol 17 (03) ◽  
pp. 1850017 ◽  
Author(s):  
P. Krishnakumar ◽  
K. Rameshkumar ◽  
K. I. Ramachandran
Keyword(s):  
Acoustic Emission ◽  
Titanium Alloy ◽  
Tool Wear ◽  
High Speed ◽  
Machining Process ◽  
Support Vector ◽  
Tool Condition ◽  
Wide Range ◽  
Cutting Zone ◽  
Tungsten Carbide Tool

Mechanical and chemical properties of titanium alloy have led to its wide range of applications in aerospace and biomedical industries. The heat generation and its transfer from the cutting zone are critical in machining of titanium alloys. The process of transferring heat from the primary cutting zone is difficult due to poor thermal conductivity of titanium alloy, and it will lead to rapid tool wear and poor surface finish. An effective tool monitoring system is essential to predict such variations during machining process. In this study, using a high-speed precision mill, experiments are conducted under optimum cutting conditions with an objective of maximizing the life of tungsten carbide tool. Tool wear profile is established and tool conditions are arrived on the basis of the surface roughness. Acoustic emission (AE) signals are captured using an AE sensor during machining of titanium alloy. Statistical features are extracted in time and frequency domain. Features that contain rich information about the tool conditions are selected using J48 decision tree (DT) algorithm. Tool condition classification abilities of DT and support vector machines are studied in time and frequency domains.

Tool condition monitoring system based on support vector machine and differential evolution optimization

2016 ◽  
Vol 231 (5) ◽  
pp. 805-813 ◽  
Author(s):  
Guo F Wang ◽  
Qing L Xie ◽  
Yan C Zhang
Keyword(s):  
Support Vector Machine ◽  
Tool Wear ◽  
Differential Evolution ◽  
Condition Monitoring ◽  
Support Vector Machine Classifier ◽  
Tool Condition Monitoring ◽  
Support Vector ◽  
Grid Search ◽  
Tool Condition ◽  
Condition Monitoring System

A tool condition monitoring system based on support vector machine and differential evolution is proposed in this article. In this system, support vector machine is used to realize the mapping between the extracted features and the tool wear states. At the same time, two important parameters of the support vector machine which are called penalty parameter C and kernel parameter [Formula: see text] are optimized simultaneously based on differential evolution algorithm. In order to verify the effectiveness of the proposed system, a multi-tooth milling experiment of titanium alloy was carried out. Cutting force signals related to different tool wear states were collected, and several time domain and frequency domain features were extracted to depict the dynamic characteristics of the milling process. Based on the extracted features, the differential evolution-support vector machine classifier is constructed to realize the tool wear classification. Moreover, to make a comparison, empirical selection method and four kinds of grid search algorithms are also used to select the support vector machine parameters. At the same time, cross validation is utilized to improve the robustness of the classifier evaluation. The results of analysis and comparisons show that the classification accuracy of differential evolution-support vector machine is higher than empirical selection-support vector machine. Moreover, the time consumption of differential evolution-support vector machine classifier is 5 to 12 times less than that of grid search-support vector machine.

Acoustic Emission Signal Analysis for Tool Condition Monitoring in Microturning of Titanium Alloy

2014 ◽  
Vol 984-985 ◽  
pp. 31-36 ◽  
Author(s):  
A. Gopikrishnan ◽  
A.K. Nizamudheen ◽  
M. Kanthababu
Keyword(s):  
Acoustic Emission ◽  
Titanium Alloy ◽  
Tool Wear ◽  
Condition Monitoring ◽  
Dominant Frequency ◽  
Tool Condition Monitoring ◽  
Discrete Wavelet ◽  
Nose Radius ◽  
Emission Signal ◽  
Tool Condition

In this work, an online acoustic emission (AE) monitoring system is developed, to investigate the effect of tool wear during the microturning of titanium alloy with a tungsten carbide insert of nose radius 0.1 mm. The AE signal parameters were analyzed in time domain, frequency domain and discrete wavelet transformation (DWT) techniques to correlate with the tool wear status. The root mean square (AERMS) and specific AE energies are also computed for the decomposed AE signals, using the DWT. The results demonstrated that dominant frequency and DWT techniques are found to be most suitable for online tool condition monitoring, using AE sensors in the microturning of titanium alloy.

scholarly journals Type-2 Fuzzy Modeling for Acoustic Emission Signal in Precision Manufacturing

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Qun Ren ◽  
Luc Baron ◽  
Marek Balazinski
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Fuzzy Modeling ◽  
Machining Process ◽  
Precision Machining ◽  
Precision Manufacturing ◽  
Emission Signal ◽  
Tool Condition ◽  
Ae Signal

This paper presents an application of type-2 fuzzy logic on acoustic emission (AE) signal modeling in precision manufacturing. Type-2 fuzzy modeling is used to identify the AE signal in precision machining. It provides a simple way to arrive at a definite conclusion without understanding the exact physics of the machining process. Moreover, the interval set of the output from the type-2 fuzzy approach assesses the information about the uncertainty in the AE signal, which can be of great value for investigation of tool wear conditions. Experiments show that the development of the AE signal uncertainty trend corresponds to that of the tool wear. Information from the AE uncertainty scheme can be used to make decisions or investigate the tool condition so as to enhance the reliability of tool wear.

Acoustic Emission Signal Analysis for Tool Condition Monitoring in Microendmilling of Aluminium Alloy

2014 ◽  
Vol 984-985 ◽  
pp. 25-30
Author(s):  
Muniyandi Prakash ◽  
P. Ravisankar ◽  
Mani Kanthababu
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Aluminium Alloy ◽  
Condition Monitoring ◽  
Dominant Frequency ◽  
Tool Condition Monitoring ◽  
Tool Condition ◽  
Ae Energy ◽  
Ae Signal ◽  
Ae Signals

In this study, the effect of tool wear is correlated with acoustic emission (AE) signal during microendmilling of aluminium alloy (AA 1100). The AE signals were acquired using Kistler make AE sensor and the signal features are analyzed in time domain (root mean square (RMS)) and frequency domain (dominant frequency and amplitude). The dominant frequency of the AE signal shows increasing trend with increase in the tool wear, where as AERMSshow uneven trend. The discrete wavelet transformation technique (DWT) has also been carried out by decomposing the required AE signal in different frequency bands. The AERMSand specific AE energy were computed for the decomposed AE signals. From the specific AE energy, it is observed that shearing occurs during microendmilling and also found to be similar that of macro-regieme endmilling. The result demonstrated that the AE signals are potential indicator for tool condition monitoring in microendmilling.

scholarly journals A Novel Machine Learning-Based Methodology for Tool Wear Prediction Using Acoustic Emission Signals

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5984
Author(s):  
Juan Luis Ferrando Chacón ◽  
Telmo Fernández de Barrena ◽  
Ander García ◽  
Mikel Sáez de Buruaga ◽  
Xabier Badiola ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acoustic Emission ◽  
Feature Extraction ◽  
Tool Wear ◽  
Real Time ◽  
Current Trend ◽  
Support Vector ◽  
Wear Prediction ◽  
Vast Number ◽  
Ae Signal

There is an increasing trend in the industry of knowing in real-time the condition of their assets. In particular, tool wear is a critical aspect, which requires real-time monitoring to reduce costs and scrap in machining processes. Traditionally, for the purpose of predicting tool wear conditions in machining, mathematical models have been developed to extract the information from the signal of sensors attached to the machines. To reduce the complexity of developing physical models, where an in-depth knowledge of the system being modelled is required, the current trend is to use machine-learning (ML) models based on data from the tool wear. The acoustic emission (AE) technique has been widely used to capture data from and understand the real-time condition of industrial assets such as cutting tools. However, AE signal interpretation and processing is rather complex. One of the most common features extracted from AE signals to predict the tool wear is the counts parameter, defined as the number of times that the amplitude of the signal exceeds a predefined threshold. A recurrent problem of this feature is to define the adequate threshold to obtain consistent wear prediction. Additionally, AE signal bandwidth is rather wide, and the selection of the optimum frequencies band for feature extraction has been pointed out as critical and complex by many authors. To overcome these problems, this paper proposes a methodology that applies multi-threshold count feature extraction at multiresolution level using wavelet packet transform, which extracts a redundant and non-optimal feature map from the AE signal. Next, recursive feature elimination is performed to reduce and optimize the vast number of predicting features generated in the previous step, and random forests regression provides the estimated tool wear. The methodology presented was tested using data captured when turning 19NiMoCr6 steel under pre-established cutting conditions. The results obtained were compared with several ML algorithms such as k-nearest neighbors, support vector machines, artificial neural networks and decision trees. Experimental results show that the proposed method can reduce the predicted root mean squared error by 36.53%.

AE Signal of early Rotor Cracks Fault Diagnosis Based on PWVD and SVM

2011 ◽  
Vol 216 ◽  
pp. 212-217
Author(s):  
Xue Jun Li ◽  
K. Wang ◽  
Kuan Fang He ◽  
X.C. Li
Keyword(s):  
Support Vector Machine ◽  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Frequency Characteristic ◽  
Feature Vector ◽  
Support Vector ◽  
Early Prediction ◽  
Emission Signal ◽  
Reliable Diagnosis ◽  
Ae Signal

Aiming at inaccurately and inefficiently fault feature of early crack by the vibration method in the environment of strong noise, the acoustic emission signal (AE) is used to cracks defect with the advantages of sensitive. The Pseudo Wigner-Ville Distribution (PWVD) is introduced to extract the amplitude and frequency of AE signal as feature vector, which combines with support vector machine (SVM) to achieve prediction and diagnosis of fault types of different rotor cracks depth. It is shown by experiment that the proposed method have the features of obvious frequency characteristic, early prediction of fault time, accurate and reliable diagnosis results of early cracks fault diagnosis.

scholarly journals Acoustic Emission Studies on Carbon/Phenolic Ablative Composites using PCD and PCBN Tools

1999 ◽  
Vol 8 (3) ◽  
pp. 096369359900800 ◽  
Author(s):  
P. S. Sreejith ◽  
R. Krishnamurthy
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Cutting Tool ◽  
Tool Condition Monitoring ◽  
Tool Condition ◽  
The Status ◽  
Emission Studies ◽  
Pcbn Tools ◽  
Cutting Speeds ◽  
Phenolic Composite

During manufacturing, the performance of a cutting tool is largely dependent on the conditions prevailing over the tool-work interface. This is mostly dependent on the status of the cutting tool and work material. Acoustic emission studies have been performed on carbon/phenolic composite using PCD and PCBN tools for tool condition monitoring. The studies have enabled to understand the tool behaviour at different cutting speeds.

Tool Wear Detection Using Time Series Analysis of Acoustic Emission

1989 ◽  
Vol 111 (3) ◽  
pp. 199-205 ◽  
Author(s):  
S. Y. Liang ◽  
D. A. Dornfeld
Keyword(s):  
Time Series ◽  
Acoustic Emission ◽  
Tool Wear ◽  
Time Series Analysis ◽  
Cutting Tool ◽  
Shear Zones ◽  
Parameter Vector ◽  
Model Parameter ◽  
Cutting Tool Wear ◽  
Series Analysis

This paper discusses the monitoring of cutting tool wear based on time series analysis of acoustic emission signals. In cutting operations, acoustic emission provides useful information concerning the tool wear condition because of the fundamental differences between its source mechanisms in the rubbing friction on the wear land and the dislocation action in the shear zones. In this study, a signal processing scheme is developed which uses an autoregressive time-series to model the acoustic emission generated during cutting. The modeling scheme is implemented with a stochastic gradient algorithm to update the model parameters adoptively and is thus a suitable candidate for in-process sensing applications. This technique encodes the acoustic emission signal features into a time varying model parameter vector. Experiments indicate that the parameter vector ignores the change of cutting parameters, but shows a strong sensitivity to the progress of cutting tool wear. This result suggests that tool wear detection can be achieved by monitoring the evolution of the model parameter vector during machining processes.

Audio-Based Condition Monitoring in Milling of the Workpiece Material With the Hardness Variation Using Support Vector Machines and Convolutional Neural Networks

2018 ◽  
Author(s):  
Achyuth Kothuru ◽  
Sai Prasad Nooka ◽  
Rui Liu
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Tool Wear ◽  
Support Vector ◽  
Workpiece Material ◽  
Artificial Intelligent ◽  
Tool Condition ◽  
Hardness Variation ◽  
Vector Machines ◽  
Audible Sound

Machining industry has been evolving towards implementation of automation into the process for higher productivity and efficiency. Although many studies have been conducted in the past to develop intelligent monitoring systems in various application scenarios of machining processes, most of them just focused on cutting tools without considering the influence due to the non-uniform hardness of workpiece material. This study develops a compact, reliable, and cost-effective intelligent Tool Condition Monitoring (TCM) model to detect the cutting tool wear in machining of the workpiece material with hardness variation. The generated audible sound signals during the machining process will be analyzed by state of the art artificial intelligent techniques, Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs), to predict the tool condition and the hardness variation of the workpiece. A four-level classification model is developed for the system to detect the tool wear condition based on the width of the flank wear land and hardness variation of the workpiece. The study also involves comparative analysis between two employed artificial intelligent techniques to evaluate the performance of models in predicting the tool wear level condition and workpiece hardness variation. The proposed intelligent models have shown a significant prediction accuracy in detecting the tool wear and from the audible sound into the proposed multi-classification wear class in the end-milling process of non-uniform hardened workpiece.

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