Mohammed, Arif (2012) Applying laser irradiation and intelligent concepts to identify grinding ...

189 Pages·2017·2.58 MB·English

by  Christine Middleton

Applying laser irradiation and intelligent concepts to
identify grinding phenomena

By
Arif Mohammed

Aerospace Manufacturing in the Faculty of Engineering
The University of Nottingham

Thesis submitted to the University of Nottingham for the degree of Doctor of
Philosophy
September 2010
Abstract

The research discussed in this thesis explores a new method for the detection of
grinding burn temperature using a laser irradiation acoustic emission (AE)
sensing technique. This method is applicable for the grinding process
monitoring system, providing an early warning for burn detection on metal alloy
based materials (specifically nickel alloy based materials: Inconel718 and
MarM002). The novelty in this research is the laser irradiation induced thermal
AE signal that represents the grinding thermal behaviour and can be used for
grinding burn detection.
A set of laser irradiation experiments were conducted to identify key process
characteristics. By controlling the laser power, the required grinding
temperatures were simulated on alloy test materials. The thermal features of the
extracted AE signal were used to identify the high, medium and low
temperature signatures in relation to the off-focal laser distances. Grinding
experiments were also conducted to investigate burn conditions. The extracted
AE data was used to identify grinding burn and no burn signatures in relation to
the depth of cuts. A new approach using an artificial neural network (ANN) was
chosen as the pattern recognition tool for classifying grinding burn detection
and was used to classify grinding temperatures by extracting the mechanical-
thermal grinding AE signal. The results demonstrated that the classification
accuracy achieved was 66 % for Inconel718 and 63 % for MarM002 materials.
The research established that the wheel wear has a large influence on the
creation of burn within the workpiece surface. The results demonstrated that the
AE signals in each grinding trial presents different levels of high, medium and
low temperature scales. This type of information provides a foundation for a
new method for monitoring of grinding burn and wheel wear.

[ii]
Acknowledgement

I am humbly grateful to Dr Janet Folkes from the University of Nottingham and
to Professor Xun Chen from the Liverpool John Moores University for their
supervision and meticulous assistance in the successful completion of my
doctoral studies at the University of Nottingham.
First and foremost I would like to express my deepest gratitude to my
supervisor Dr Janet Folkes at the University of Nottingham who after a long
battle with cancer suddenly passed away just before my final thesis submission.
Dr Folkes worked tirelessly during her illness and was extremely helpful during
my research period. She was an extremely kind, generous, thoughtful and
friendly academic with all her students. She never allowed her terminal illness
to impact on her duties towards her students. She kept to her regular routine and
would come to the office and enquire about the progress of her students. I had
regular meetings with Dr Folkes during the last four years of my research at the
University of Nottingham. Her inspirational guidance, endless support, advice
and encouragement made this thesis a practical reality as opposed to just an idea
and I am deeply saddened that she will not be present at my graduation
ceremony.
I would forever like to remember her as the wonderful Teacher and Engineer
she was and also as my doctoral supervisor throughout.
I am also extremely grateful to a number of people who assisted me in the
completion of this research. I would like to thank various colleagues for their
assistance and encouragement in the dark times specifically Dr James Griffin
from the University of Nottingham and Dr Jamie Mcgourley from Rolls Royce
plc.
I am also very grateful for the technical support provided by Rolls-Royce plc
and the assistance provided by the Nottingham University laboratory
technicians specifically Mr Stuart Branston and Mr Mark Daine; for their
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expertise, support and assistance during the completion of the experimental
phase(s) of this research.
In particular, I most humbly acknowledge the unyielding support of my parents
and close siblings; without their support over the years I would not have been
presented with this opportunity.
I am indebted to all my friends and colleagues; who in various ways helped and
assisted me during my study period both morally and intellectually.
I would like to thank EPSRC and my sponsoring company Rolls Royce plc for
their financial support during my PhD research tenure.
Finally, I would like to thank God for giving me the physical strength and
faculties of my mind to complete this research to a successful conclusion.

[iv]
Abbreviations

AD Analog-to-digital
AE Acoustic Emission
Al O Aluminium Oxide
2 3
ANN Artificial Neural Network
BPNN Backpropagation Neural Network
CBN C u b i c B o ron Nitride
CNC Computer Numerical Control
DSP Digital Signal Processing
DAC Data Acquisition Card
DMA Direct Memory Access
EDX Energy Dispersive X ray Analysis
FT Fourier Transform
FFT Fast Fourier Transform
Fuzzy-c Fuzzy-c clustering algorithm
GA Genetic Algorithm
GP Genetic Programming
JFTA Joint Time Frequency Analysis
LMS Least Mean Square
LS-SVM Least Square Support Vector Machine
LSM Liquid state machine
ME Maximum Entropy
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ML Machine Learning
MLP Multi layer-perceptron
MRR Material Removal Rate
NN Neural Network
PSO Particle Swarm Optimisation
PAC Physical Acoustic Corporation
PCA Principal Component Analysis
PCI Peripheral Component Interconnect
PXI PCI-extensions for instrumentation
RMS Root Mean Squares
RMSE Root Mean Squared Error
SLT Statistical learning theory
SEM Scanning Electron Microscope
SNR Signal to noise ratio
STFT Short Time Fourier Transform
SVM Support Vector Machine
TCM Tool condition monitoring
VIPER Vitreous Improved Performance
Extreme
WT W avelet Transform
WPT Wavelet Packet Transform

[vi]
Nomenclature

Symbol Units Quantity
α Integer Learning rate
mm Depth of cut
ap
mm Skin thickness (or
δ penetration depth of
laser)
Wavelength of the laser
λ0 𝜇𝑚
Η Absorption coefficient
The permeability of
µo 𝐻�𝑚 vacuum
The initial permeability
µr 𝐻�𝑚 of metal
Ν Poisson’s ratio
Ρ kg/m3 Material density
Electrical conductivity

7
σ ∗10 �
Ω.m
Initial radian of laser
ω1 mm beam
Laser spot radius
ω2 mm
Special heat capacity
cp
J⁄kg.K
Laser total energy
E0 J
mm Laser focal length
f1
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mm Laser off-focal length
f2
Fourier transform
F(ω) function
G mm3/mm3 G-ratio
Laser energy flux
I0 W� 2
mm
Thermal conductivity
K
W⁄m.K
Thermal diffusivity
2
α m ⁄s
Kurtosis
Kr
Watts Laser total power
P0
mm3/min Material removal rate
Qw
µm Roughness
Ra
Skewness
Sk
Time
t s
Temperature of the
T(0,t) ∘C material surface after
time t (Laser)
Root-mean-square
URMS (RMS)
Feed Rate
Vf 𝑚/𝑚𝑖𝑛
Spindle speed (wheel
Vs 𝑚/𝑠 speed)
Workpiece speed
Vw 𝑚𝑚/𝑚𝑖𝑛

[viii]
Contents

Chapter 1: Introduction ............................................................................... 16
1.1 Introduction ........................................................................................ 16
1.2 Aim and Objectives ............................................................................ 20
1.2.1 Methodology ............................................................................... 21
1.3 Main Contributions ............................................................................. 23
1.4 Outlines of the thesis .......................................................................... 25
Chapter 2: Literature Review...................................................................... 27
2.1 Introduction ........................................................................................ 27
2.2 Grinding behaviour ............................................................................. 28
2.2.1 Thermal effects in grinding ......................................................... 29
2.2.2 Grinding wheel wear ................................................................... 32
2.2.3 The performance of grinding fluid .............................................. 36
2.3 Acoustic emission technique for grinding monitoring ....................... 37
2.4 Pattern recognition and machine learning .......................................... 46
2.4.1 Feature extraction and classification of AE signals in grinding . 50
2.5 Application of laser technique in grinding ......................................... 53
2.6 The research gaps ............................................................................... 54
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Chapter 3: AE sensing systems for the laser irradiation and grinding ... 56
3.1 Introduction ........................................................................................ 56
3.2 Grinding process ................................................................................. 57
3.2.1 Nickel and Nickel based alloys ................................................... 57
3.2.2 Grindability of nickel based alloys ............................................. 59
3.2.3 Properties of grinding wheels...................................................... 61
3.3 AE sensing system development for the laser irradiation .................. 62
3.3.1 Experiment set up for laser irradiation ........................................ 62
3.4 The sensing system development for the grinding experiment .......... 67
3.4.1 Makino A55 machine centre ....................................................... 67
3.4.2 Experiment set up for grinding ................................................... 68
3.4.3 Force, power and vibration measurement ................................... 73
3.5 Wheel wear measurement .................................................................. 76
3.6 Summary ............................................................................................ 76
Chapter 4: Digital signal processing technique ......................................... 78
4.1 Introduction to signal processing........................................................ 78
4.1.1 Signal properties and representation ........................................... 79
4.2 Features in time, frequency or time-frequency domains .................... 84
4.2.1 Features in time domain .............................................................. 84
4.2.2 Feature in the frequency domain ................................................. 85
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