On-Line Monitoring the Hard Turning Using Distribution Parameters of Acoustic Emission Signal

2015 ◽  
Vol 787 ◽  
pp. 907-911
Author(s):  
J. Bhaskaran
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Hard Turning ◽  
Metal Cutting ◽  
Cubic Boron Nitride ◽  
Emission Signal ◽  
Distribution Parameters ◽  
On Line ◽  
Ae Signal ◽  
Wear Monitoring

In hard turning, tool wear of cutting tool crossing the limit is highly undesirable because it adversely affects the surface finish. Hence continuous, online tool wear monitoring during the process is essential. The analysis of Acoustic Emission (AE) signal generated during conventional machining has been studied by many investigators for understanding the process of metal cutting and tool wear phenomena. In this experimental study on hard turning, the skew and kurtosis parameters of root mean square values of AE signal (AERMS) have been used for online monitoring of a Cubic Boron Nitride (CBN) tool wear.

Application of CBN Cutting Tools in Hard Turning and Tool Wear

2013 ◽  
Vol 690-693 ◽  
pp. 2022-2025
Author(s):  
Hai Dong Zhao ◽  
Li Bao An ◽  
Pei Qing Yang ◽  
Ye Geng
Keyword(s):  
Tool Wear ◽  
Hard Turning ◽  
Manufacturing Industry ◽  
Metal Cutting ◽  
Elevated Temperatures ◽  
Cubic Boron Nitride ◽  
Cutting Tools ◽  
Tool Material ◽  
Specific Strength ◽  
Strength And Stiffness

Considerable research has been directed towards discovering new engineering materials for various applications. As a superhard material, Cubic Boron Nitride (CBN) has been developed and applied to engineering for several tens of years. Due to its high specific strength and stiffness as well as good creep, fatigue and wear resistance at elevated temperatures, CBN has been widely used as cutting tool material in manufacturing industry. In this paper, the preparation and characteristics of CBN are introduced. As hard turning has been more and more employed in recent years as an advanced metal cutting technique, the application of CBN cutting tools in hard turning is presented based on the literature, and in particular, the main wear mechanisms of CBN tools in hard turning are summarized, owing to the significant influence of tool wear on the tool life and product quality.

Integrated System for Monitoring the Tool State Using Temperature Measuring by Natural Thermocouple Method

2014 ◽  
Vol 1036 ◽  
pp. 274-279 ◽  
Author(s):  
Marinela Inţă ◽  
Achim Muntean
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Manufacturing Systems ◽  
Metal Cutting ◽  
Block Diagram ◽  
Cutting Temperature ◽  
Integrated System ◽  
Work Piece ◽  
Tool Temperature ◽  
On Line

The intensive developments of intelligent manufacturing systems in the last decades open the large possibilities of more accurate monitoring of the metal cutting process. One of the most important factors of the process is the tool state given by the rate of the tool wear, which is the result of a lot of influences of almost all cutting parameters. The modern tool monitoring systems relieved that the accuracy of the results increases when using a combination of surveyed signals such as: vibrations, power consumption, acoustic emission, forces or tool temperature. Combining the output signals in a monitoring function using the neural network method gives the best results when using on-line monitoring. Considering the tool temperature as an important factor in the tool wear process and adding it to the acoustic emission and force measuring the accuracy of the results seems to improve significantly. The present paper describes an integrated monitoring system with integration of the cutting temperature, the calibration device for work piece-tool thermocouple, and the block diagram for on-line survey measuring using LabView platform.

Study on accurate tool wear monitoring based on acoustic emission signal

2010 ◽  
Author(s):  
Yinhu Cui ◽  
Guofeng Wang ◽  
Dongbiao Peng ◽  
Xiaoliang Feng ◽  
Lu Zhang ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Acoustic Emission Signal ◽  
Tool Wear Monitoring ◽  
Emission Signal ◽  
Wear Monitoring

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.

Study of AE Signal for Tool Wear Monitoring in Micro Milling Based on LVQ Algorithm

2010 ◽  
Vol 126-128 ◽  
pp. 719-725 ◽  
Author(s):  
Chia Liang Yen ◽  
Ming Chyuan Lu ◽  
Jau Liang Chen
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Frequency Domain ◽  
Micro Milling ◽  
Tool Wear Monitoring ◽  
Characteristic Analysis ◽  
Classification Rate ◽  
Ae Signal ◽  
Wear Monitoring ◽  
Wear Condition

The Acoustic Emission signal was studied in this report for tool wear monitoring in micro milling. An experiment was conducted first to collect the AE signal generated from the workpiece during cutting process for characteristic analysis, training the system model and finally testing the system performance. In the system development, Acoustic Emission (AE) signals were first transformed to the frequency domain with different feature bandwidth, and then the Learning Vector Quantization (LVQ) algorithms was adopted for classifying the tool wear condition based on the generated AE spectral features. The results show that the frequency domain signal provides the better characteristics for monitoring tool wear condition than the time domain signal. In considering the capability of the AE signal combined with LVQ algorithms, the sharp tool condition can be detected successfully. At the same time, 80% to 95% of the classification rate can be obtained in this study for the worn tool test. Moreover, the increase of the feature bandwidth improved the classification rate for the worn tool case and 95% of classification rate for the case with 10 kHz feature bandwidth.

Quantitative Relationships for Acoustic Emission from Orthogonal Metal Cutting

1981 ◽  
Vol 103 (3) ◽  
pp. 330-340 ◽  
Author(s):  
Elijah Kannatey-Asibu ◽  
David A. Dornfeld
Keyword(s):  
Acoustic Emission ◽  
Metal Cutting ◽  
Energy Content ◽  
Energy Levels ◽  
Cutting Parameters ◽  
Signal Energy ◽  
Emission Signal ◽  
Orthogonal Metal Cutting ◽  
Ae Signal ◽  
Work Of Deformation

Theoretical relationships have been drawn between acoustic emission (AE) and the metal cutting process parameters by relating the energy content of the AE signal to the plastic work of deformation which generates the emission signals. The RMS value of the emission signal is expressed in terms of the basic cutting parameters. Results are presented for 6061-T6 aluminum and SAE 1018 steel over the range of speeds 25.2 to 372 sfm (0.128 to 1.9 m/s) and rake angles 10 to 40 deg. Good correlation has been found between predicted and experimental signal energy levels. In addition, AE generation from chip contact along the tool face is studied and the AE energy level reflects the existence of chip sticking and sliding on the tool face, and indicates the feasibility of utilizing AE in tool wear sensing.

The Online Monitoring of Surface Quality Based on Time-Frequency Spectrum Analysis of Acoustic Emission

2011 ◽  
Vol 141 ◽  
pp. 564-568
Author(s):  
Chang Liu ◽  
Guo Feng Wang ◽  
Xu Da Qin ◽  
Lu Zhang
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Surface Quality ◽  
Machining Process ◽  
Work Piece ◽  
Time Frequency ◽  
Quality Of Work ◽  
On Line ◽  
Ae Signal

There is a high requirement on the surface quality of work-pieces made of Ni-based super-alloys due to the important application in aviation and aerospace fields, so it is particularly important to implement the on-line monitoring to the surface quality of work-piece in the machining process. The acoustic emission (AE) signal has the relatively superior signal/noise ratio and sensitivity during the process of nickel alloy. Through the analysis of AE signal’s characteristic which comes from the different condition of tool wear, it is an effective mean to evaluate the tool wear condition and monitor the surface quality of work-piece due to the usage of AE during the machining process. This paper indicate that it is simple and intuitive to achieve the on-line monitoring of surface quality which based on spectrum analysis of AE signal and proposed the method of on-line monitoring of the nickel alloy surface quality under different condition of tool wear based on AE time-frequency spectrum.

Investigation and utilization of the acoustic emission signal for monitoring the dressing process

2002 ◽  
Vol 216 (4) ◽  
pp. 543-553 ◽  
Author(s):  
M Wehmeier ◽  
I Inasaki
Keyword(s):  
Acoustic Emission ◽  
Cubic Boron Nitride ◽  
Low Frequency ◽  
Frequency Component ◽  
Grinding Wheel ◽  
Emission Signal ◽  
Continuous Scanning ◽  
Conventional Grinding ◽  
Ae Signal ◽  
User Friendly

Improvement of monitoring techniques is essential to make the complex dressing process more reliable, economical and user friendly. A system utilizing data from acoustic emission (AE) sensors and a suitable algorithm combined with a graphical user interface has been applied with this aim. The influence of different grinding wheel types and dressing parameters on the AE signal has been investigated and a dressing monitoring system is proposed. The root mean square (r.m.s.) signal, or the low-frequency component of the AE signal, provides information that can be utilized to monitor the process. A reliable touch detection and cycle termination can be established. The spectrum of the raw signal has been investigated for cubic boron nitride (CBN) and conventional grinding wheels. Reliable data acquisition techniques, which make a continuous scanning of such wide bandwidth signals possible, have been applied.

Research on Gate Valve Internal Leakage Detection Based on Acoustic Emission Signal Processing

2014 ◽  
Vol 1037 ◽  
pp. 169-173 ◽  
Author(s):  
Liang Zhu ◽  
Bin Zou ◽  
Shao Hua Gao ◽  
Ming Jiang ◽  
Zhi Ping Li
Keyword(s):  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Gate Valve ◽  
Signal Acquisition ◽  
Production Environment ◽  
Emission Signal ◽  
Safety Production ◽  
On Line ◽  
Ae Signal ◽  
Adverse Influence

Gate valve is common equipment in the petrochemical industry. It often brings out internal leakage because of quality, operation, corrosion and other reasons, and has adverse influence on safety production, environment protection and energy conservation. This paper put forward the on-line detection method about the gate valve internal leakage detect problem on the basis of AE signal analysis. Firstly, introduced the structure of gate valve and main reasons of internal leakage, discovered the acoustic emission signal parameters of gate valve internal leakage, and derived the relation model between internal leakage and AE signal theoretically. Then, constructed internal leakage signal acquisition platform based on DN80 gate valve internal leakage simulation system. Finally, put forward gate valve gas internal leakage test, set the different pressure at different points on the conditions of different pressure valve of 0.2MPa, 0.4MPa and 0.6MPa, The experiment showed that gate valve internal leakage status can be displayed by the signal RMS value when the gate valve internal leakage AE signal is between 30kHz and 80kHz. The AE signal next to the gate valve downstream position is the strongest. Internal leakage of the valve exists if downstream signal was significantly greater than the upstream signal or the background noise. The AE signal strengthened while the pressure difference between upstream and downstream is increased.

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