on Paint Appearance

216 Pages·2013·32.52 MB·English

Assessment of Paint Appearance Quality
in the Automotive Industry
A thesis submitted for the degree of Doctor of Philosophy
Hai-zhuang Kang (BSc, MSc)
BRUNEL
UNIVERSITY
Department of Systems Engineering, Brunel University
May2000
To my wife and daughter:
Judith Qunhun Nie
Connie Yue Kang
Hai-zhuang Kang © 2000
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Abstract
In the modem automotive industry, more and more manufacturers recognise that
vehicle paint appearance makes an important contribution to customer satisfaction.
Attractive appearance has become one of the important factors for customers in
making a decision to purchase a car. Objective measurement of the quality of
autobody paint appearance, as perceived by the customer, in a repeatable,
reproducible, continuous scale manner is an important requirement for improving the
paint appearance. It can provide car manufacturers a standard reference to evaluate
the qualityofthe paint appearance.
This thesis mainly deals with the measurement of paint appearance quality in the
automotive industryby investigating,identifyinganddevelopingmeasurementmethodsin
thisarea. Firstof all,the 'state of the art' inthe area ofpaint appearance measurement
was presented, which summarised the concept of appearance, models, attributes and
definitions.To further identifythe parameters and instruments used in the automotive
industry, a round robin test was launchedto perform visualassessment and instrument
measurements on a set of panels in some European car manufacturers. A summaryof
the correlation found between measurable parameters and visual assessment provided
the basis of the further work. Based on the literature survey and round robin test
results, the next work is mainlyconcentrated on the two most important parameters,
'orange peel' and 'metal texture effect', how to separate and evaluate them. Digital
signalprocessing technique, FFT and Filtering, have been employed to separate them
and a set of measures have been provided for evaluation. At the same time, The
technique for texture pattern recognition was introduced to evaluate the texture effect
when a finetexture comparison was needed. A set of computable textural parameters
based on grey-tone spatial-dependence matrices gives good correlation directly
corresponding to visualperception.
To resolve the overall appearance modelling problem, two novel and more powerful
modellingtools, artificialneuralnetworks and fuzzy logic,are introduced to model the
overall appearance. The test results showed that both of them are able to reflect the
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correlation between overall appearance and the major parameters measured from a
painted surface.
Finally,an integrated measurement system, 'Smart Appearance', was developed using
the image processing techniques and the artificial neural network model. The
implement results show that this system can measure the major attributes of paint
appearance and provide an overall appearance index corresponding to human visual
perception. This systemishelpfulto product qualitycontrol on car body paint. It also
couldbe used on the paintproduction linefor dynamicmeasurement.
IV
Acknowledgements
I am most indebted to my research supervisor, Prof. Clive Butler, Director of the
Postgraduate Programmes in Advanced Manufacturing Systems at the Department of
System Engineering of Brunel University, for his continuous guidance, support and
encouragement. His sincere personal concern and invaluableadvice have motivated
methroughout the research period. With hishelpthe research was completed on time.
I amvery gratefulto him fortaking the painto read, refineand polishthis thesis.
I would also liketo express mythanks and appreciation to my second supervisor, Dr.
Qingping Yang, Lecturer at the Department of System Engineering of Brunel
University, for his help, encouragement, and invaluable advice on this research. His
constant concern and encouragement helpedmethroughout myPhD studies.
The research work is founded by the EU project, BRITE EURAM Project nOBE-97-
4140 AUTOSURF (Surface Texture Optimisation for Automotive Industry). Our
industrialpartners provided their instruments, Round Robin Test results, painted steel
panels and invaluable experience. Their requirements inspired me to develop an
advanced measurement system for paint appearance quality control. Their advice and
helpmade thisresearch work more valuable. Here I would liketo express mygenuine
thanks to: Delpine Eyquem, Magali Touze (Renault), Katrien Meseure, Jan Scheers
(OCAS), Claudoi Prelini (IVECO), John Davies (PPG UK), Clive Chailinor,
Michelle Lewis (British Steel), Marie Louise (VOLVO), Stefano Gatti (Qualitech
Research Co.).
I would also like to thank the other members of the Autosurf Brunei team, Franco
Sacerdotti, Fabrizio Benati and Alessandro Porrino. We had a very good work and
collaboration environment. I benefited from our talks, discussions or even disputes.
Teamwork alsomade a valuablecontribution to the successofthe project 'Autosurf.
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Finally, I would like to thank to my wife, Qunhun Nie, and daughter, Connie for their
tolerance, continuous support and understanding.
My PhD study has been supported by the Overseas Research Students Awards Scheme
CORS)founded by the British Council.
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Table of Contents
Chapter 1. Introduction
1.1. Research Problem Statement 1-1
1.2. Research Objective ·· · 1-5
1.3. Research Significance ··..·· 1-6
1.4. KnowledgeRequirement 1-7
1.5. Themeofthe Research 1-8
1.6. Organisationofthe Thesis 1-9
Chapter 2. An Overview of Paint Appearance Measurement in
Automotive Industry
2.1. Introduction 2-1
2.2. Conception ofAppearance 2-1
2.3. Appearance Measurement 2-6
2.3.1. Introduction 2-6
2.3.2. GlossMeasurement 2-9
2.3.3. Instrumentation 2-14
2.2.3.1. Wavescan(BYK) 2-14
2.2.3.2. QMS-BP 2-16
2.4. LinksBetween OverallAppearance and IndividualParameters 2-19
2.4.1. EmpiricalFormulas 2-19
2.4.2. Regression Analysis 2-19
2.5. Conclusion ·..·· 2-20
VII
Chapter 3. Visual Assessment on Paint Appearance
3.1. Introduction 3-1
3.2. Visual Assessment 3-2
3.3. Statistical Methods 3-3
3.3.1. Score Based 3-3
3.3.2. Rank Based 3-7
3.4. Improved Methods 3-9
3.4.1. HalfScore 3-9
3.4.2. Full Comparison 3-11
3.5. Conclusions 3-13
Chapter 4. Round Robin Test and Statistical Analysis
4.1. Introduction 4-1
4.2. Objective 4-1
4.3. Experiment Design 4-2
4.4. Results and Discussion ·· 4-5
4.4.1. Visual Assessment. ·········· 4-5
4.4.2. Surface Topography Parameters ···························4··-·10
4.4.3. Spectral Parameters ·············· 4-16
4.4.3.1. 010ss ········· 4-16
4.4.3.2. DOl 4-18
4.4.3.3. Haze 4-20
4.4.4. Overall Appearance 4-21
4.4.4.1. Overall Appearance with QMS (Brunel) 4-21
4.4.4.2. Appearance Index from OCAS 4-23
4.5. Conclusions 4-24
Chapter 5. Evaluation of Orange Peel on Paint Appearance
5.1. Introduction 5-1
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5.2. PaintingFilmStructure 5-3
5.3. Orange PeelClassification 5-6
5.4. Image SignalProcessing 5-7
5.4.1. Imageacquisition 5-7
5.4.2. ImageProfile 5-9
5.4.3. Separation of Orange PeelDominated Surface 5-11
5.4.3.1. Space-domain Signaland Time-domainSignal.. 5-11
5.4.3.2. Discrete Fourier Transform(DFT) 5-12
5.4.3.3. DigitalFiltering 5-16
5.4.4. EvaluationofMetal Texture and Orange Peel.. 5-20
5.4.4.1. Evaluation of MetalTexture 5-21
5.4.4.2. Evaluation of Orange Peel.. 5-24
5.4.5. Experiment Result ··········· 5-27
5.4.5.1. VisualAssessmentResult 5-27
5.4.5.2. ExperimentResult 5-29
5.4.5.3. Correlation 5-29
5.5. Conclusions 5-33
Chapter 6. Evaluation of Metal Texture Effect on Paint Appearance
6.1. Introduction 6-1
6.2. SurfaceTexture 6-1
6.2.1. What is SurfaceTexture? ······················ 6-2
6.2.2. SurfaceParameters ··········· 6-3
6.2.3. SteelTexture Type 6-4
6.2.4. Metal Texture Effect 6-5
6.3. Texture Evaluation 6-6
6.3.1. BasicTextural Properties ····················· 6-6
6.3.2. Grey-Tone Spatial-DependenceMatrices ···· 6-8
6.3.3. Textural Feature - Contrast. ····························6-10
6.4. ExperimentResult 6-13
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6.4.1. Test Panels 6-13
6.4.2. Correlation Between Contrast and VisualAssessment 6-14
6.4.3. Contrast Distance 6-16
6.4.4. Grey-LevelDifference 6-20
6.5. Conclusions 6-22
Chapter 7. Modelling of Overall Appearance using Artificial
Neural Network
7.1. Introduction ········· ·..·..7-1
7.2. ArtificialNeural Networks 7-2
7.2.1. What's Neural Networks? 7-2
7.2.2. Model ofa Neuron ······· 7-3
7.2.3. Architecture andAlgorithm 7-3
7.2.4. Benefitsand Limitation ·..············· 7-5
7.3. Modellingof OverallAppearance ················· 7-5
7.3.1. Definitionofthe problem ·············· 7-6
7.3.2. Architecture 7-6
7.3.3. Algorithm 7-7
7.3.4. Data Preparation 7-9
7.3.5. SupervisedTrainingand Testing ············ 7-12
7.3.6. Retraining 7-21
7.4. Comparison and Discussion 7-21
7.5. Conclusions 7-25
Chapter 8. Modelling of Overall Appearance Using Fuzzy Logic
8.1. Introduction 8-1
8.2. Fuzzy-LogicModelled System ····················· 8-2
8.2.1. TheNature of FuzzyLogic 8-2
8.2.2. Reasons for UsingFuzzyLogic ···························8·-·3···
8.2.3. ModellingOverallAppearance UsingFuzzyLogic 8-3
8.2.4. MembershipFunction 8-9
8.2.5. Rulesand Process ·········· 8-13
8.2.6. FuzzyLogic Model Interface 8-14
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