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Tool Wear Monitoring in Turning Using Fused Data Sets of Calibrated Acoustic Emission and Vibration

A thesis submitted for the degree of Doctor of Philosophy in the Faculty of Technology

By

Asa Prateepasen

Brunel Centre of Manufacturing Metrology, Brunel University, Cleveland Road, Uxbridge, Middlesex

January 2001

AcknowledgementsI would like to thank deeply my principal supervisor Dr. Y H J Au for his valuable comments, assistance and support to guide my work to a satisfactory conclusion. I am pleased to have had the opportunity to work with INTErSECT Faraday Partnership Flagship Project, "Acoustic Emission Traceable Sensing and Signature Diagnostics (AESAD)". I acknowledge the help and useful discussions from all my AESAD colleagues, especially Professor Barry E. Jones. I am especially grateful to Brian Shaw and his technical colleagues for their special kindness and professionalism, together with their expert advice on the experimental work. I would like to thank my friend K.Pakorn for his comments in the area of data classification and signal processing. I am also grateful to Professor C. Clark for his suggestions. I acknowledge the grant from Petroleum Authority of Thailand and King Mongkut's University of Technology Thonburi (1CMUTT) for the level-two training courses on five Nondestructive Testing (NDT) methods in Canada and ASNT level-two on AE in USA in 1993. That was my first exposure to NDT, and the experience convinced me the need to pursue Ph.D. research in acoustic emission. I am indebted to The Royal Thai Government for the scholarship to support my PhD research. I would also like to express my gratitude to KMUTT, the university that I have worked in, for allowing me to pursue this PhD work. Finally my greatest debts are due to my deceased parents who had given me their love and support when they were alive. My special thanks go to my wife who has been waiting patiently for me back in Thailand throughout this period. I am most indebted to her for her love, understanding and encouragement. Her support has motivated me to work hard with single-mindedness.

II

AbstractThe main aim of this research is to develop an on-line tool wear condition monitoring intelligent system for single-point turning operations. This is to provide accurate and reliable information on the different states of tool wear. Calibrated acoustic emission and vibration techniques were implemented to monitor the progress of wear on carbide tool tips.

Previous research has shown that acoustic emission (AE) is sensitive to tool wear. However, AE, as a monitoring technique, is still not widely adopted by industry. This is because it is as yet impossible to achieve repeatable measurements of AE. The variability is due to inconsistent coupling of the sensor with structures and the fact that the tool structure may have different geometry and material property. Calibration is therefore required so that the extent of variability becomes quantifiable, and hence accounted for or removed altogether. Proper calibration needs a well-defined and repeatable AE source.

In this research, various artificial sources were reviewed in order to assess their suitability as an AE calibration source for the single-point machining process. Two artificial sources were selected for studying in detail. These are an air jet and a pulsed laser; the former produces continuous-type AE and the latter burst type AE. Since the air jet source has a power spectrum resembling closely the AE produced from single-point machining and since it is readily available in a machine shop, not to mention its relative safety compared to laser, an air-jet source is a more appealing choice.

The calibration procedure involves setting up an air jet at a fixed stand-off distance from the top rake of the tool tip, applying in sequence a set of increasing pressures and measuring the corresponding AE. It was found that the root-mean-square value of the AE obtained is linearly proportional to the pressure applied. Thus, irrespective of the layout of the sensor and AE source in a tool structure, AE can be expressed in terms of the common currency of 'pressure' using the calibration curve produced for

that particular layout. Tool wear stages can then be defined in terms of the 'pressure' levels. .

In order to improve the robustness of the monitoring system, in addition to AE, vibration information is also used. In this case, the acceleration at the tool tip in the tangential and feed directions is measured. The coherence function between these two signals is then computed. The coherence is a function of the vibration frequency and has a value ranging from 0 to 1, corresponding to no correlation and full correlation respectively between the two acceleration signals. The coherence function method is an attempt to provide a solution, which is relatively insensitive to the dynamics and the process variables except tool wear.

Three features were identified to be sensitive to tool wear and they are; AErms, and the coherence function of the acceleration at natural frequency (2.5-5.5 kHz) of the tool holder and at high frequency end (18-25kHz) respectively. A belief network, based on Bayes' rule, was created providing fusion of data from AE and vibration for tool wear classification. The conditional probabilities required for the belief network to operate were established from examples. These examples were presented to the belief network as a file of cases. The file contains the three features mentioned earlier, together with cutting conditions and the tool wear states. Half of the data in this file was used for training while the other half was used for testing the network. The performance of the network gave an overall classification error rate of 1.6 % with the WD acoustic emission sensor and an error rate of 4.9 % with the R30 acoustic emission sensor.

IV

Forum Attended and Papers PublishedA. Prateepasen, Acoustic Emission Traceable Sensing, "International Forum for 1999, Frontiers of Science and Measurement", (NPL, 21-25 June 1999), UK.

A. Prateepasen, Y. H. J. Au and B.E. Jones, Comparison of Artificial Acoustic Emission Sources as Calibration Sources for Tool Wear Monitoring in Single-Point Machining, "Proceedings of the 24 th European Conference on Acoustic Emission Testing" (Senlis, 24-26. May 2000), CETIM, France, 2000. P.253-260 (Published in Journal of Acoustic Emission Vol 18, 2000. P 196-204)

A. Prateepasen, Y. H. J. Au, Acoustic Emission and Vibration for Tool Wear Monitoring in Single-Point Machining Using Belief network, "Doctoral Research Conference 2000", (Brunel, 14-15 September 2000), UK, 2000 (Accepted by "IEEE Instrumentation and Measurement Technology Conference" (Budapest 21-23 May 2001), Hungary, 2001.

A. Prateepasen, Y. H. J. Au and B.E. Jones, Calibration of Acoustic Emission for Tool Wear Monitoring, "XVI IMEKO World Congress" (Vienna 25-28. September 2000), Austria, 2000, Volume VI, P. 255-260.

A. Prateepasen, Y. H. J. Au and B.E. Jones, Transferability Validation of AE for Tool Wear Monitoring, "Eurosensor XV, 11 th International Conference on SolidState Sensors and Actuators" (Munich 10-14 June 2001), Germany, 2001. (Submitted)

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ContentsChapter 1: Introduction 1.1 General Introduction 1.2 Acoustic emission and vibration for tool wear detection 1.3 Aims of the project 1.4 Objectives of the project Chapter 2: Literature Review 2.1 Wear in metal cutting 2.1.1 Types of cutting tool wear mechanism 2.1.2 Types of tool failure 2-1 2-1 2-2

1-1 1-3 1-4 1-4

2.1.3 Types of tool wear and tool failure in carbide cutting tools 2-2 2.1.4 Tool life 2.2 Review of AE and its signal processing 2.2.1 AE waveform parameters 2.2.2 AE wave propagation 2.2.3 Sources of AE in metal cutting 2.2.4 Models of AE for orthogonal machining 2.2.5 Review of various techniques for tool wear detection 2.2.6 Advantages and disadvantages of various methods 2.2.7 Review of AE technique for tool wear detection 2.2.8 Advantages of AE for tool wear and failure detection 2.2.9 Limitation of AE for tool wear monitoring 2.2.10 AE transducer calibration versus system calibration. 2.2.11 Artificial sources for AE transducer calibration and AE system calibration 2.2.12 Comparison of artificial AE sources 2.3 Vibration 2.3.1 Machine tool vibration 2.3.2 Measures of vibration signal 2.3.3 Correlation techniques 2.3.4 Vibration techniques for tool wear monitoring 2-32 2-35 2-36 2-37 2-38 2-39 2-43 2-6 2-8 2-10 2-12 2-13 2-14 2-17 2-21 2-21 2-28 2-28 2-30

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2.4 Classification techniques 2.4.1 2.4.2 Neural networks Classification using Bayes' rule

2-44 2-44 2-47

Chapter 3:Tool Wear Measures and Preliminary Study of Artificial AE Sources 3.1 Objectives of preliminary test 3.2 Set up of preliminary test 3.3 Experimental equipment and specification of the tool tip and the tool holder 3.3.1 3.3.2 3.3.3 3.3.4 3.3.5 3.3.6 3.3.7 Detail of the tool tip and the tool holder AE equipment model 5500 AE Transducer AE filter and pre-amplifier Accelerometer SI 1220 spectrum analyser Hewlett Packard HP 89410A Vector Signal analyser 3-2 3-3 3-3 3-4 3-4 3-4 3-5 3-5 3-8 3-10 3-15 3-15 3-16 3-16 3-1 3-2

3.4 Microset Replica method 3.5 Preliminary test procedure and results 3.6 Preliminary test of Artificial AE sources 3.6.1 3.6.2 Pencil-lead breakage source Air jet source

3.7 Conclusions

Chapter 4: Comparison of Artificial Acoustic Emission Sources as Calibration Sources 4.1 Introduction 4.2 Artificial AE sources for tool wear monitoring 4.3 Similarity Coefficient 4. 4 AE comparison of air jet, laser and machining 4.4.1 4.4.2 4.4.3 Machining tests Air Jet Tests Pulsed Laser Test 4-1 4-2 4-3 4-3 4-4 4-4 4-6 4-7VII

4.5 Similarity of artificial and machining AE sources