Paint Analysis E-Book

Roger Dietrich

• Herausgeber: Vincentz Network
• Kategorie: Wissenschaft und neue Technologien
• Serie: European Coatings TECH FILES
• Sprache: Englisch

Seitenzahl: 494

Veröffentlichungsjahr: 2021

Beschreibung

The market demands modern, high-performance, flawless paints that possess specified properties. Where deviations from set points occur, the cause must be investigated and the error must be remedied. What "standard methods" don't disclose is why a particular coating either meets or fails to meet a requirement. Thus the author presents modern analytical techniques and their applications in the coatings industry that answer further complex questions. The information in this book can be used for performing failure analysis, production control and quality control, and also meet the requirements of modern high-level quality management. An excellent combination of theory and practice for formulators, paint engineers and applied technologists seeking a sound basic introduction to instrumental paint analysis and concrete answers to everyday problems.

Leseprobe

Roger Dietrich

Paint Analysis

The Handbook for Study and Practice

2nd Revised Edition

Cover: chokniti, Adobe Stock

Bibliographische Information der Deutschen Bibliothek

Die Deutsche Bibliothek verzeichnet diese Publikation in der Deutschen Nationalbibliographie; detaillierte bibliographische Daten sind im Internet über http://dnb.ddb.de abrufbar.

Roger Dietrich

Paint Analysis, 2nd Revised Edition

Hanover: Vincentz Network, 2021

European Coatings Library

ISBN 978-3-7486-0434-1

ISBN 37486-434-3

© 2021 Vincentz Network GmbH & Co. KG, Hanover

Vincentz Network GmbH & Co. KG, Plathnerstr. 4c, 30175 Hanover, Germany

This work is copyrighted, including the individual contributions and figures.

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European Coatings Library

Roger Dietrich

Paint Analysis

The Handbook for Study and Practice

2nd Revised Edition

Foreword

I am really glad that you found this book and opened it, curious to find out what to expect. This is the second edition of the book which was first published in 2009 and I can promise a lot of new interesting insights into field analysis and laboratory work with respect to coatings. Compared to the first edition you will find new techniques, additional advanced preparation and sampling methods and a lot of more practical examples. My intention was and is to share my experiences that I have made over the last 30 years in our laboratory and in the field with respect to the analysis of paints and coatings. I am a chemist and my approach to characterize a coating material, a raw material and coating failures is therefore primarily scientifically evidence based. I believe that is very important to have a profound insight into the basic, physical conditions and limitations of the analytical methods in order to comprehend their power and limitations. But I also have worked in the coating industry and know that is not always possible to investigate a problem extensively according to scientific rules. Therefore, I have tried in this second edition to shift the focus more to practical implications of analysing paints and coatings without neglecting the physical and methodical background. I added a section about sampling methods and representativeness because I have made the experience that the implications of proper sampling on the investigation result are too often disregarded.

Of course, you know the routine measuring and testing methods that tell something about colour, gloss and other physical conditions of paint films and coating material. These techniques provide the information how a certain material behaves. But they fail when it comes to the question why a tested material exhibits a particular property.

This is where this book comes in. I present a toolbox of analytical methods which exist for decades but have not yet found widespread use in the coating industry. However, I would like to demonstrate that the potential of the featured methods is incredibly big and far from being exploited.

The possible applications of the methods described here grow with the requirements of the samples to be examined. Almost every month a new question arises which can be answered with a process development based on the methods presented in this book.

Although I cannot deny my education as a chemist, the book is aimed at a broad group of users from the coatings industry. It is intended as an aid for the application engineer on site to start solving problems, but as well to provide the laboratory manager with suggestions for new ways of dealing with his tasks. I tried to build a bridge between the scientific view on the reality and the practical requirements on the site and in the applications laboratories.

I would like you to hold this book in your hands as a daily reference and tool, and to use it extensively to find out how you can

reveal the causes of coating defects,

investigate paint raw materials with respect to contaminations

or analyse lacquered products in terms of their properties

So, if after a short time this manual is lying on your desk or laboratory table with paint stains and marginal notes, I would be more satisfied. In the practical part of the manual I have, therefore, tried to structure and process the topics in such a way that you can draw immediate suggestions for action if, for example, paint craters occur when coating products or if you want to know whether and how two paint batches differ. Of course, this is backed up with theoretical principles in a separate chapter. But the problem solutions described in the practical part should ideally enable you, like a kind of “recipe”, to proceed directly to action with the book in your hand to tackle your daily challenges.

I do not see this book only as a handbook documenting the current state of the art, but I would like to use it to stimulate dialogues from which improvements of current procedures or perhaps even new developments can arise. I am therefore always open to suggestions and comments and hope to receive numerous feedbacks, which I will gladly take up and answer as soon as possible.

Münster, November 2020

Roger Dietrich

[email protected]

Inhaltsverzeichnis

Foreword

Part I General information about paint analysis

1 The surface

2 Why paint analysis?

3 Relevance of modern analytical techniques to paint analysis

4 General considerations

5 Chemical mapping

5.1 Infrared microscopy mapping

5.2 TOF-SIMS imaging

5.3 SEM-EDS mapping

6 Depth profiling

7 Instrumentation

Part II Coating failure analysis

1 The bumpy road to knowledge

2 The analytical procedure

2.1 Inquiry

2.2 Inspection

2.2.1 Macroscopic inspection

2.2.2 Microscopic inspection

2.3 Informed guess

2.4 Instrument selection

2.5 Investigation

2.6 Interpretation

2.7 Iterance

2.8 Implementation

2.9 Incumbency

3 The power of sampling

3.1 The role of sampling in the analytical procedure

3.2 Random sampling and representativeness

3.3 Targeted sampling

3.4 Non-destructive sampling

3.4.1 Wipe sampling

3.4.2 Rinse sampling

3.4.3 Abrasive sampling

3.5 Typical sampling failures

3.5.1 Wrong sample collection

3.5.2 Non-representative sampling

3.5.3 Selecting the wrong amount of samples

3.5.4 Application of a wrong sampling procedure

3.5.5 Inappropriate sampling tools and containers

3.5.6 Insufficient storing and shipping of samples

3.6 Micro­sampling

4 Paint failures and their analytical approach

4.1 Some considerations on failure reasons

4.1.1 Insufficient workpiece preparation

4.1.2 Handling failures

4.1.3 Application conditions

4.1.4 Environment and climate

4.2 Investigation of adhesion failures

4.2.1 Delamination due to substrate contamination

4.2.2 Adhesion defects caused by migration processes

4.2.3 Delamination caused by moulding conditions

4.2.4 Delamination due to application faults

4.2.5 Delamination due to insufficient pretreatment

4.3 Paint cratering and “fisheyes”

4.3.1 Cratering caused by contamination of the paint material

4.3.2 Craters and pinholes caused by substrate contaminants

4.3.3 Craters caused by paint additive agglomeration

4.3.4 Cratering caused by the application conditions

4.4 Bubbles and blisters

4.5 Discolouration

4.6 Hazes and stains

4.7 Paint spots

4.8 Orange peel

4.9 References

Part III Quality control and process analysis

1 Quality control of raw material

1.1 Binders

1.1.1 Identity check

1.1.2 Detection of trace contaminants

1.2 Solvents

1.3 Pigmentsand fillers

2 Quality control of paint production

2.1 Analysis of filter residues

2.1.1 SEM/EDS analysis of filter residues

2.1.2 FT-IR analysis of filter residues

2.2 Analysis of fogging residues

2.3 Quality check of finished and semi-finished products

2.3.1 ATR-FT-IR screening

2.3.2 TOF-SIMS analysis

2.3.3 Headspace GC-MS analysis

2.4 Paint quality tests

3 Field analysis

3.1 Process analysis of paint shops

3.2 Aerosol analysis

3.3 Operating test

3.4 Sampling of the painting air

3.5 Monitoring of pretreatment steps

3.6 Investigation of the degree of crosslinking in 2-pack paints

3.7 Investigation of paint additive migration

3.8 Marine and aircraft coating inspection

3.9 Handhelds and portables

3.10 References

Part IV Methods of coating analysis

1 Optical light microscopy

1.1 Extended focus imaging (EFI)

1.2 Differential interference contrast (DIC)

2 Fluorescence microscopy

3 Infrared spectroscopy

3.1 Physical background

3.2 Characteristic absorptions

3.3 Instrumentation

3.4 Sample preparation

3.5 Spectrum representation

3.6 Quantification

3.7 Data analysis and evaluation

3.7.1 Data processing

3.7.2 Use of databases

4 Surface infrared spectroscopy

4.1 ATR-FT-IR spectroscopy

4.1.1 Physical background

4.1.2 Depth of penetration

4.1.3 Information depth

4.1.4 Effective path length

4.1.5 Quantification

4.1.6 Detection limit

4.1.7 Instrumentation

4.1.8 Sample preparation

4.2 Reflection infrared spectroscopy

4.2.1 Physical background

4.2.2 External reflection

4.2.3 Instrumentation

4.3 Diffuse reflection spectroscopy

4.3.1 Physical background

4.3.2 Penetration depth

4.3.3 Influences of variable parameters on the spectrum

4.3.4 Sample preparation

4.3.5 Instrumentation

4.3.6 Quantification

4.3.7 Optimization of the measuring parameters

4.3.8 Repeatability

5 Infrared microscopy

5.1 Instrumentation

5.2 Sample preparation

5.3 Infrared microscopy, infrared transmission mode

5.4 Infrared microscopy, reflection mode

5.5 Infrared microscopy, ATR mode

5.6 Line-scan und mapping analysis

6 Raman spectroscopy

7 Time-of-flight secondary ion mass spectrometry

7.1 Instrumentation

7.2 Calibration and mass resolution

7.3 Sample preparation

7.4 Spectral evaluation

7.5 Imaging mode

7.6 Quantification

7.7 Summary

7.8 Applications

8 Scanning electron microscopy

8.1 Physical background

8.2 Lateral resolution

8.3 Instrumentation

8.4 Sample condition

9 Electron microanalysis

9.1 Physical background

9.2 Quantification

9.3 Detection limits

9.4 EDS-Imaging

9.5 Applications

10 X-ray photoelectron spectroscopy

10.1 Physical background

10.2 Information depth

10.3 Lateral resolution

10.4 Information retrieval

10.5 Quantification

10.6 Instrumentation

10.7 Applications

10.8 Technical data

11 GC-MS

11.1 Physical background of GC

11.2 Headspace

11.3 Data evaluation

11.4 Application

12 Thin layer chromatography TLC-ATR-FT-IR

12.1 General principle

12.2 Separation procedure

12.3 Identification

12.4 Performance parameters of selected methods

13 References

Author

Acknowledgements

Index

Part IGeneral information about paint analysis

For the investigation of paints, semi-finished products, raw materials and finished coatings, a very extensive “toolbox” of the most diverse analytical methods is available today. This is sometimes a bit confusing for the non-analyst. For an effective use of the individual methods, it is necessary to have a very detailed knowledge of the possibilities and limitations of each method, the excitation modes and the information to be expected. The book that you just opened will try to shed some light on the thicket of methods. A great deal of attention is given to surface processes, as the majority of questions arising in practice are related to surface phenomena in one way or another. But also the so-called “bulk analysis” plays an important role in this book. Classical test methods such as climate-testing, solar simulation etc. are deliberately omitted because these are test methods for a certain parameter but have a completely different objective and are not analysis techniques.

1The surface

Figure I.1: Solvent droplets on a brushed, hydrophobic steel surface

This book deals with the application of modern techniques to paint analysis, with a special focus on surface analysis. If one thinks about the word “surface”, it quickly becomes clear what a relative and vague term it is. To a painter “surface” does not mean the same as it does to a surface chemist. To a painter, the surface represents that part of an object which is usually presented to the outside world and can be touched and observed directly, see Figure I.1. A painter considers a brushed visually clean steel surface. However, it can also be defined as the boundary layer between a solid or liquid material and a surrounding liquid or gaseous phase.

A surface physicist would probably refer to it as a phase interface. Alternatively, it could be defined as the area of a solid or liquid thing at which the bulk physical and chemical properties change instantly, a so-called property boundary.

A surface chemist, however, is talking about the uppermost molecular layers of a material when he uses the word surface. This is an area that cannot be observed without the help of analytical techniques. In fact, the uppermost layers of an object often determine the quality and behaviour of the material as far as (paint) adhesion is concerned. The uppermost layer of the steel surface is not directly visible without the help of machines. Probably the steel surface exhibits a chemical surface modification which explains the hydrophobic behaviour visible in Figure I.1. Or there is a thin layer of contaminations that produces a hydrophobic property!? – To define this is the task of surface analysis.

3.1.1.1Definition of the term surface

So, let’s first define how to use the word surface in this book. A surface is a boundary layer which separates a substrate from the surrounding environment (air, liquid). It is typically 1 nm to 1 μm thick. In contrast, a “thin layer” is defined as being 1 μm to 10 μm thick.

Figure I.2: AFM (atomic force microscope) image of a paint surface (60x60 μm)

The surface plays a significant role in the physical and chemical properties of a material. A contract painter, for example, who paints and coats coils and metal profiles has to rely on the surface quality of the goods he is going to coat. The surface of the raw material that he receives might well look clean. However, the material has a long history before it has been delivered to this company to be painted or coated. During production, storage and transport of a coil, for example, numerous substances may have been adsorbed onto the surface. This surface layer contaminants may not be visible, but it always exists! And sometimes even traces of contaminants can seriously impair the adhesion of a coating to a surface.

When it comes to processing of the coil, the chemical composition of the outermost molecular layer plays a significant role. If the coil has been coated with a protective layer of oils to prevent corrosion during transport and storage, the paint will exhibit poor adhesion or craters after application. Even a monomolecular layer of some of these oils can have deleterious effects on coating procedures.

As these ultra-thin layers are invisible, the unfortunate manufacturer is in fact “blind” as far as the surface quality of his coils is concerned. In most cases, therefore, he will decide to install a cleaning process before applying the coating. But he will do so without knowing if it is necessary and, even worse, without knowing what to remove from the surface. Unfortunately, there is no “magic” process for eliminating all the various kinds of contaminants. His efforts might well produce a surface quality worse than before, due to the presence of oil residues and traces of cleaning chemicals, such as surfactants.

Some goods require a pretreatment like e.g. phosphating before coated. The quality of the deposited anti-corrosion phosphate conversion layer is significantly depending on the parameters of the process. Only well crystallized zinc or iron phosphate guarantees a perfect adhesion of the subsequently applied coating. Without analytical methods like e.g. SEM or infrared external reflection spectroscopy, it is impossible to check the performance of the pretreatment process with respect to crystal morphology and percentage of coverage. The author has worked on a lot of adhesion failures issues during the last twenty years which have been caused by poor conversion layer application, although on impulse the painter has been blamed.

Another focus is on the coating material itself. As the paint and the painted substrate have to be a chemical match if good adhesion is to be obtained, a few questions need to be asked before the painting process is started.

What is the chemical composition of the substrate surface?

Which pretreatment can be used to improve paint adhesion and what effect will it have?

How do the paint ingredients influence the surface of the material that has to be painted?

What influence do the paint additives have on paint adhesion?

Unfortunately, these questions often can’t be answered by simple tests or classical chemical analysis, because they require an ability to analyse tiny amounts of substances that have high surface sensitivity. Only the surface analysis techniques described in this book can answer these questions

Figure I.3: SEM Images of different zinc phosphate conversion layer morphology

A growing field of application for modern surface analytical techniques is not only paint application but also paint production. Modern high-performance paints have to fulfil many requirements simultaneously that are sometimes hard to match. This does not only create a demand for characterisation of the raw materials and products. The chemical interaction of paint compounds and the reaction between each compound and the ingredients of the substrate (e.g. a polymer) are also key parameters.

If, for example, a moulded polymer part has to be coated, it is not just the polymer which is of interest. The manufacturer or supplier of the raw material compounds the polymer to customer demands. In accordance with the requirements imposed on the polymer material, he adds additives to improve flame, light, impact or heat resistance. One parameter the supplier is not concerned about is the paintability of the product made from the granules which he supplies. That is a process which the polymer supplier does not see.

However, it has been shown in the past that additives present in polymers “designed” to enhance moulding processes, e.g. offering for example good release from injection moulds, exhibit poor properties with respect to surface finishing by painting. Most of the additives incorporated into a polymer migrate to the surface, driven by temperature, humidity, time or solvents. This sometimes leads to unpredictable results, such as paint adhesion failure, chemical reactions, discolouration, and wetting failure. Many manufacturers of paint for automotive interior parts have therefore discovered, that it is essential not only to know their own paint manufacturing process, but also to learn something about the polymers which have to be painted. This is a task that can easily be fulfilled by the techniques which are described in this book.

2Why paint analysis?

A few years ago, the author was talking to the CEO of a paint manufacturer who had different issues with costumers complaining about the performance of several paint types on a constant level. I asked him if he ever thought about using the powerful toolbox of analytical methods to improve his paint production. He answered: “We sell lacquer, we are not an analytical company!”

For him “selling” was the key word. For the quality of the incoming raw material he relied on the factory test certificate of the suppliers, with respect to the quality of the produced paint he banked on his processes.

But certificates provided by the supplier do not tell everything, what is essential to know to ensure a safe and high quality production. The best process documentation and compliance cannot guarantee a flawless production. In addition to that, the experience has shown that a lot of customer complaints in the field are not substantiated, because they based on application faults in the coating process. Without knowledge about the details of raw materials, the products and the field application a manufacturer is on a constant “blind flight”. Some paint producers have understood this and profit from a well-equipped laboratory and excellently educated lab staff. But for the commercial department a laboratory and analytical operations are very often simply “lost” costs without profit. On the long run however it pays off to understand the details of a production line. It is a competitive advantage to understand the details of a costumer`s paint shop to avoid unsubstantiated complaints and help him to improve his process. And it is good to know the raw materials in detail to save costs with respect to production failures caused by deficient raw material.

Often, when assigning an investigation task to an internal or external laboratory, high expectations are placed on the laboratory:

It should be done quickly (“Best by the day before yesterday”)

There must be no charge

One analysis should, if possible, comprehensively solve the entire possibly complex production problem

It should not be surprising, that these wishes will not come true. Solving a production problem normally requires complex procedures, with analytical methods providing the necessary facts. But this process avoids expensive “try and error” actions and aimless activities. The prerequisite for a reliable solution based on measured data are:

A qualified problem analysis

caution during sampling

the selection of appropriate testing methods

specialist knowledge of the laboratory

experience in the evaluation of measurement data

accuracy in the interpretation and formulation of results

sound translation of the results into the process

This requires a very intensive exchange with the laboratory in order to effectively combine in-house knowledge and process know-how with the knowledge generated by the analysis. The dividing line between the internal knowledge of the commissioning company and the external knowledge of the laboratory depends on the personnel structure of the company. A contract coater with a more artisan orientation will usually not know and need many details about paint chemistry, while an automotive supplier specializing in industrial series coating often has its own competence teams on site for all questions of paint chemistry. Both they can also benefit from instrumental analysis methods if these are used in the service and, if necessary, the knowledge of the laboratory service provider is taken into account. Roughly speaking, the less knowledge about the processes and materials in the commissioning company, the more the external knowledge of the service provider can and should be consulted. If this interaction works well and smoothly, in some cases even one analysis costing a few hundred Euro, can save five times as many costs for complaints or faulty batches.

3Relevance of modern analytical techniques to paint analysis

There are hundreds of techniques for analysing paints and coatings. They yield information about viscosity, gloss, haze, hardness, acid value, etc. In other words, they describe the product and its properties. They ensure that the desired level of quality is achieved. On the other hand, standard analytical tools often fall short when failures and production problems arise. The standard techniques are perfect for checking the quality of a product. However, if a product is defective and the root cause has to be investigated, the standard techniques are not very helpful. For example, a monomolecular layer of a release agent on the surface can easily cause severe adhesion failure if the material is to be painted. The quantity of substance may be too low to be detected by standard techniques. Poor cleaning procedures in a paint shop can also cause paint defects of a few microns in size. Before this problem can be solved, it is necessary to know what has caused the paint defect. The substance or inclusion particle causing this failure is too small to be characterised by standard techniques.

This is an analytical gap that can be closed by the surface analytical techniques described in this book. They will help to answer the question:

Why does a product have unexpected properties and why do failures happen?

Typical topics in paint analysis are:

What do contaminants in paint layers or wet paint samples consist of?

What is the chemical composition of paint layers at a certain depth from the surface?

How are chemical bonds formed between paint components?

Why does a paint layer peel off a substrate and where does the delamination take place?

What is the reason for paint spots?

It should be mentioned that there is no all-embracing technique that can answer these questions. In fact, there are many parameters which influence the decision which technique to employ for the analysis, including:

additional information about the appearance of the defect

preliminary sample investigation by optical light microscopy

chemical and physical properties of the coating

desired detection limit

In other words, it takes an experienced user to find the best tool that can answer the questions raised about the sample. These considerations will be discussed later in this book.

4General considerations

Before presenting the techniques discussed in this book, it would be helpful to find a common basic principle to describe them. No matter whether we are talking about infrared spectroscopy or TOF-SIMS or SEM, the main principle consists in probing a sample with radiation.

Figure I.4: General concept of probing the surface of a sample by primary radiation excitation

The sample is essentially analysed by radiation that probes for specific properties and characteristics of the material. This radiation, which is called the primary radiation, can consist of electrons, ions, neutral particles and photons, such as infrared waves and X-rays. The primary radiation triggers a reaction specific to the sample that may take the form of the emission of electrons, ions or X-rays. This “reaction” by the sample is detected by an electronic system composed of an analyser and a detector. The result can be displayed as a spectrum on a computer or be printed on paper. The last step of the process is data evaluation by an experienced analyst. The evaluation must include

plausibility check

comparison with databases

interpretation with respect to the analytical problem

The nature of the interaction which occurs between the probing beam and the sample depends on the type, energy and angle of incidence of the probing radiation and, of course, the sample material.

The primary radiation interacts with the sample in a specific way. Each type of sample reaction can be detected separately and analysed to reveal the chemical and physical composition of the sample and its surface. The radiation emitted by the sample is called secondary radiation. Each type of primary radiation can produce a different type of secondary radiation. Probing with an electron beam, for example, may lead to the formation of:

secondary electrons

X-rays

back-scattered electrons

fluorescence

Figure I.5: Surface analysis techniques sorted by the excitation radiation type: photons, electrons and ions

Each type of radiation conveys different information about the sample that all adds up to a comprehensive understanding of the sample’s properties. Not only the primary radiation, but the secondary radiation emitted by the sample, can consist of electrons, ions, neutral particles and photons that result from sample excitation or reflection of the primary radiation.

The latter is a consequence of diffraction and dispersion that change the energy, angle and intensity of the primary radiation in accordance with the topography, structure and chemical composition of the sample. The secondary radiation emanating from the sample is detected, analysed and displayed in the form of an angle-, energy- or mass-resolved spectrum, which contains information about the sample and its surface.

The various types of probing primary radiation and detected secondary radiation have spawned more than 50 different analytical techniques over the decades. Some of them are useful for solving practical problems and have made their way into routine work. Many of them, however, never passed the experimental stage and have very limited application to technical samples outside of academia. Figure I.5 sums up the abbreviations of a few techniques sorted by the type of primary radiation. A sample excitation by photons (left part) can be performed e.g. by infrared radiation resulting in absorption and the “answer” of the sample can be analysed by reflection absorption spectroscopy (IRRAS), internal reflection spectroscopy (MIR or ATR) or diffuse reflection spectroscopy (DRIFT). A photon excitation can also be done using a laser resulting in the release of ions (LAMMA) or by X-rays that produce photoelectrons analysed by X-ray photoelectron spectroscopy (XPS).

Table I.1: List of analytical techniques

Primaryradiation

Secondary radiation

Technique

Abbreviation

Analysed area

Electrons

electrons

auger electron spectroscopy

AES

uppermost molecular layer

scanning electron microscopy

SEM

sample surface down to a depth of a few microns

X-rays

electron microanalysis

ESMA

EDS

WDX

sample surface down to a depth of a few microns

Infrared radiation

infrared radiation

surface infrared spectroscopy

FT-IR

ATR

IRRAS

sample surface down to a depth of a few microns

infrared microscopy

IRM

X-rays

electrons

X-ray photoelectron

XPS

ESCA

sample surface down to a depth of a few nanometres

ions

ions

secondary ion mass spectroscopy

SIMS

TOF-SIMS

uppermost molecular layer

In this book, the author covers those techniques which have proven to be very useful for routine work and can deliver data in a reasonable time and at reasonable cost.

The techniques mentioned in Table I.1 yield different data about the sample. Each has its particular strengths and weaknesses. It is very important to appreciate this when trying to find the right combination for the given analytical problem. It is commonly said that one technique on its own is no use and so a combination is the best way of achieving the right results. The important parameters of a technique are its

information depth

detection limits

information content

suitability for technical problems

Some techniques, for example, allow only very limited sample sizes, which sometimes renders the technique useless for “real world samples”. Others require vacuum conditions, and that excludes liquid or volatile samples. Only a handful of techniques have proven useful for routine work. The limiting features are:

measuring time per sample

suitability for technical samples

comprehensive databases of reference materials

sample preparation

Another important question is the sample area to be analysed. If, for example, a paint crater a few microns in diameter has to be analysed for possible surface contaminants capable of causing cratering, the technique to use must allow for spot analysis. This means the investigation of a very small spot with a lateral resolution of a few microns.

Figure I.6: Measuring modes in surface analysis

If, on the other hand, it is the general surface quality of the sample which is of interest, a larger area measurement must be performed in order that a representative image of the surface composition may be obtained.

Analysis of the distribution of a specific substance over a certain area calls for a scanning technique that generates a chemical map of the analysed area. On the assumption that not only the chemical composition of a surface area has to be analysed but also the depth distribution, a depth-profilingmode needs to be chosen. That entails sputtering the sample layer by layer and analysing the surfaces as they become exposed.

5Chemical mapping

The analysis of a sample like, e.g. a coating on a polymer surface for the chemical composition answering the question “which substances can be identified in the coating” sometimes leads to the additional question: “where are these substances located?. This means the desired information is the variation of the chemical composition at different points of the surface or inside the bulk material. Imagine a black coating that seems to be homogeneous on the first sight. But there are some strange features in the surface topography like Figure I.7 shows. This might lead to the question if the visible inhomogeneities are correlated with chemical properties!? The investigation of this question asks for a two-dimensional chemical analysis resolving the chemical composition with a high lateral resolution.

Two different approaches have to be distinguished with this kind of analysis: mapping and imaging.

Figure I.7: a) Optical microscope image (DIC) of a 2K high gloss polyurethane coating surface with unusual topographic features, b) infrared microscopy mapping analysis of the coating surface (marked area); false colour image displaying the peak intensity of the 2238 cm-1 peak of free (unreacted) isocyanate groups of the hardener

The so-called mapping can be done by scanning and probing the surface in two dimensions point by point or line by line acquiring a full spectrum for each point and resolving the chemical composition by extracting key features of the spectra. There are a few methods available to perform this kind of analysis which deliver different information: infrared microscopy imaging, Raman microscopy mapping, TOF-SIMS imaging and SEM mapping. Mapping means that a region of interest (ROI) is visually selected and (rather than taking a simple intensity spectrum of the whole area) rastered by the exciting beam point by point and line by line. At each point a whole spectrum is collected and stored. This can be achieved either by moving the sample on a computerized stage (like with an infrared microscope) and keeping the exciting beam fixed. Or the beam of the primary radiation is rastered while the sample is fixed on a stage like scanning electron microscopy (SEM) does. The whole set of spectra of the specified area of interest is the database of this measurement. In a second data analysis process after the measurement special features can be selected and extracted to produce images.

Imaging in contrast means taking all spectral intensities simultaneously from the whole ROI and filtering the information according to one selected part of the spectrum.

5.1Infrared microscopy mapping

The imaging mode of infrared microscopy analysis, which will be described in detail in Part IV Chapter 5, delivers molecular information with an information depth of 0.5 to 1 μm into the surface.

Figure I.7b shows a false colour 2D image of the coating surface of Figure I.7a. The colour codes the intensity of a specific signal of the NCO group of the isocyanate hardener between dark blue (no free NCO group) and white (high NCO content). This coloured image is the translation of a complex physic-chemical procedure (the excited vibration of the NCO group) into an easy-to-understand picture. The image demonstrates that the content of free isocyanate is not homogeneously distributed in the uppermost layer of the coating. This leads to the conclusion that there must have been something wrong with the mixing of the polyurethane components. Whereas the “back end” (ATR-FT-IR microscopy) is something you cannot understand without knowing vibrational theory, this “front end” picture can transport the key information in a glance.

5.2TOF-SIMS imaging

In contrast to the infrared microscopy mapping (IRMM) the TOF-SIMS imaging allows for a higher spatial resolution which is shown in Figure I.8. The cross section of a 25 μm three-layer primer system consisting of a phthalate primer in between two chlorine primer layers has been analysed by a primary ion beam of Ar+ ions in a TOF-SIMS V mass spectrometer.

Figure I.8: Optical microscopy image and TOF-SIMS image of a cross section through a three layer primer system displaying the secondary ion Cl - of chlorine compounds and (C6H5)COO- of phthalate anions

The false colour images of the Cl- ion and the (C6H5)COO- ion display the intensity of the two characteristic secondary ions in the spectrum of the negative secondary ions of the cross section and thus is an extract from the whole data set of the two dimensional measurement. Whereas IRMM detects vibrational transitions of characteristic bonds and displays the intensity of the absorptions, the peak intensity of a characteristic fragment like C6H5COO- is derived from the integration of the peak area. This is not molecular information because this fragment can originate from a lot of molecules that contain this specific group. However, knowing that the fragment C6H5COO- can be attributed to phthalate compounds and analysing further fragments associated with this secondary ion like e.g. 191 u for PET or 219 u for PBT, the molecular information can be deduced from the mass spectrometry data. The image shows, that there is a phthalate ester-based primer in between a “sandwich” of thin layers of chlorine containing primers.

5.3SEM-EDS mapping

Figure I.9: SEM-EDS mapping result of a paint defect (top left) sowing the false colour intensity images for calcium, silicon and iron (clockwise)

Another method for the visualization of lateral chemical differences which will be described in this book is the EDX (= energy dispersive X-ray analysis) or EDS (= energy dispersive spectroscopy). The sample is scanned point by point and line by line and excited by a beam of electrons between 1 keV and 30 keV. This results in the emission of characteristic X-rays for each element present in the target area. The intensity is detected and gathered by a detector and the result can be extracted from this so-called hypermap as 2d false colour image for each detected element. In contrast to the TOF-SIMS and the infrared microscopy mapping this technique (only) delivers elemental information. If, for example, calcium has been detected (=> Figure I.9) and the lateral distribution is displayed, it cannot be distinguished between calcium carbonate, calcium hydroxide or a calcium soap.

6Depth profiling

The imaging techniques enable the two-dimensional characterisation of a certain sample area in the uppermost plane. But some issues ask for analyses into the depth of the sample. Depending on the so-called information depth or penetrating depth of the applied method this allows for chemical information of the uppermost surface down 1 to 2 μm into the bulk.

Figure I.10: Information depth for selected methods

The information depth is the distance (measured down from the very top of the surface into the bulk) to the area inside the sample, where the information is generated that can be measured by the detector. Everything that lies deeper is not available from the top without sample preparation. But sometimes you might want to go deeper to understand what is happening for example 5 to 10 μm underneath the visible surface. The question is: how to achieve this goal with the minimum influence on the sample. SEM-EDS, for example, offers a limited variation of the information depth by adjusting the acceleration of the primary electrons that excite the sample surface. But the most attractive approach would be sputtering (“slicing”) the sample layer by layer from the top and analysing the surfaces as they become exposed. The TOF-SIMS method offers this mode. But the disadvantage of this process is that each sputtering process changes the surface in a way that most of the organic compounds are cracked or even destroyed. So, the target area is irreversibly modified before the information can be collected.

Figure I.11: Axial profiling of a coating layer from the surface down to the substrate by confocal design (focussing the exciting beam along the optical axis) (left) and lateral profiling of a cross section (right)

A non-destructive method is offered by the Confocal Raman Microscopy (see Chapter IV). With this method the sample is excited by a laser beam which is focussed to a very narrow so-called focal volume. By focussing the laser this vocal volume (about 1 μm3) is incrementally moved down from the surface into the bulk. This so-called axial profiling scans the chemical composition along the optical axis. So e.g. for a 5 μm layer of a primer on polymer substrate this is the only non-destructive way of characterizing the primer layer and the interface between primer and polymer substrate on a molecular basis.

The most commonly used method of gathering information about deeper layers is lateral profiling of a polished cross section. The sample is embedded into a resin, sectioned perpendicular to the surface, grinded and polished achieving a cross section surface that can be analysed by different techniques subsequently.

7Instrumentation

Some of the analytical techniques described in this book require vacuum conditions (SEM, TOF-SIMS, XPS). Other systems work in ambient air condition (ATR-FT-IR, Raman). Although they differ significantly in detail and in their chemical background, the instrumentation follows a general concept.

The instrumentation setup for the majority of techniques consists of an excitation system (the primary system) that generates photons, electrons or ions. The primary beam is directed by a focusing system onto the sample surface and into the desired area. Some techniques have an additional sputtering system (e.g. an ion gun) that allows for subsequent sputtering of layers and thus for depth profiling. After interaction of the primary beam with the sample (surface), the excited secondary radiation is collected by a ray optics system which directs the secondary beam towards the analyser. The analyser separates the secondary radiation spectroscopically by energy, direction or mass. The detectorrecords the separated or resolved signals and measures their intensity. The signals generated by it are displayed as a spectrum, which is a chart of intensity versus wavelength, mass or energy.

Figure I.12: General instrument setup for surface analysis systems

Part IICoating failure analysis

1The bumpy road to knowledge

The main application of surface analytical methods is without doubt that of failure analysis. Types of paint failure are spots, adhesion, wetting and flow problems, craters and stains. Very often, this issue demands the detection and identification of very low quantities of paint components and contaminants in a small sample area (microanalysis). For these tasks, surface analysis provides an extensive set of instruments. But that is not the only prerequisite for a successful problem troubleshooting. Therefore, the author wants to point out which steps are also important before diving deep into the analysis of different failure types: If a production fails and one does not know why, the urgency of beginning a journey of knowledge that makes the mystery familiar is obvious.

In order to understand

how a coating product works

why it fails

how it is composed

what can be done if an unexpected coating result occurs

how the quality of raw material can be controlled

how my products can be tested…

facts and data are necessary. And in mind: „Furious activity is no substitute for understanding!”

Figure II.1: Powder coating booth

Especially, when it comes to paint failure analysis people tend to rely on conjectures more and more, rather than focussing on facts. But only facts are reliable.

This book presents various examples of “real world” challenges that have occurred in this or a similar way in the author's professional experience over the last twenty-five years, happening somewhere every day, and shows the ways to solve certain problems. Attention is focused on the analysis of paint defects, as well as on raw material control, process control and production monitoring. It is important to notice that the methods go beyond standard paint quality tests and can be adjusted to the actual situation which makes them flexible for any kind of upcoming problem.

If a solution for a problem is based on science it works, if it is based on assumptions it often leads into dead ends, costs a fortune and wastes resources. To achieve facts and data you need machines, analytical equipment and manpower, but it pays off. Facts and data do not solve problems. They must be combined with knowledge and expertise. But if these requirements are fulfilled, the right path is under your shoes.

The methods described here, serve as a tool to elucidate the root cause of the failure. They should enable the optimization and improvement of production processes which would otherwise afford a lot of money and time for try and error without having these techniques.

On the other hand, the facts generated by measurements and analyses are sometimes used to “share” the costs of production failures with the suppliers.

The scientific methods are an important and versatile tool to understand and solve problems, but they are not the universal remedy. If a coating failure happens, it is a good choice to think about the procedure how to solve it before starting into action.

Failure analysis includes the search for the cause of a malfunction during production of coating materials as well as the investigation into coating application failures. A paint crater observed at the end of the production line of a product, for example, can be caused by:

coating material production failures (wrong choice of paint components, mixing errors)

storage failures

application failures

insufficient cleaning and pretreatment of the raw product

Therefore, a comprehensive search must cover all aspects from raw materials to the finished product. The challenge is the total amount of substances that have to be detected and identified. For example, one droplet of a fluorocarbon lubricant of 10 μm diameter can be the cause for a paint crater. This is a total of a few nanogram of material which has to be found and identified. It is obvious that classical low-cost analytical techniques of routine laboratory processes do not have the power to achieve this. In fact, this task asks for “heavy machinery” like e.g. TOF-SIMS and highly educated manpower. But it is worth it, because a problem solution is nothing without understanding the problem.

The efficient failure analysis requires:

the appropriate design of experiment

the appropriate sampling

the appropriate instrumentation

a qualified and educated evaluation of the data and

last but not least the translation and transfer of the achieved knowledge into the process

If one of these steps fails, the whole analytical process is worth for nothing. These issues will be described in detail in the following chapters.

2The analytical procedure

It is a common misbelief that the process of investigation of coating failures starts when the samples arrive in the laboratory. In fact, the process sequence starts earlier on the site of production. The whole process flow of failure analysis comprises:

the investigation goal

the design of experiment

the sampling procedure

the storage of samples

the transport to the laboratory

the first sample inspection

sample preparation

selection of the appropriate measurement technique

sample measurement

data evaluation

data interpretation

report

perception of the results

Figure II.2: The risk to fail in the analytical process (arbitrary units)

Each step has its challenges and can ruin the whole outcome with respect to the final goal of removing the root cause of the fault. But what is the best path to a sustained solution of production failures? A close look at each step one realizes the obstacles that can appear. It will be demonstrated how internal production knowledge, low cost technical aids, laboratory analyses, expert knowledge and last but not least common sense generate a base on which a reliable solution can grow

Figure II.3: Coating failure analysis proceeding after sampling

Normally the trouble starts with suddenly and unexpectedly appearing failure parts. Of course, this requires immediate action in order to find the cause and define measures to eliminate the issue. But how?

Quite often the author has experienced a procedure like this when called to help with these issues:

let us have a meeting

let us look for someone guilty

let us have a meeting again

put pressure on the employees

let us have a meeting again

ask for different plans to solve the problem

then panic

All these “actions” are driven by the understandable desire to get rid of the issue as soon as possible to make sure that the costumer can be served without any delay. The call for a systematic investigation of the root causes is often unheard facing pressure from costumers and financial accountants. Under the pressure of doing “something” plans and measures often suffer from a severe lack of reliable facts but are based on assumptions, rumours or feelings. Nevertheless, sometimes by chance the problems disappear, but nobody can say why. Moreover, nobody can guarantee that it will not pop up again and there is no solution for the future.

But what is the value of a removal of the issue instead of a solid solution? For sure, the systematic approach is slower and sometimes does not deliver “results” within a short period of time, but at the end it is more reliable and thus cheaper.

Of course, a good plan of what to do is a good start:

Figure II.3 shows the favourable path through the whole investigation process. The necessary tool are analytic methods to gather facts and place the latter in opposition to assumptions. But the analytical option is “only” delivering reliable data which are necessary to circle the source of the failure and the area of the production line, where it can be located. From that end to the real solution there is more to do.

2.1Inquiry

Let us assume a paint crater issue appearing in an automotive supplier plant. Someone sampled a failed bezel and presented it. To solve the issue, you would want to know as much as possible about the circumstances of this sudden production failure.

Figure II.4: Visible light microscopy picture of a paint crater

A good method is an interview of all the people involved in the project. The questions to be answered with respect to this case are:

Where does the sampled part come from? (directly from your process, somewhere from the supply chain, from the customer)

What is the history of this sample? (which does not mean which way parts of this kind typically go, but what the sample in your hand has “experienced”)

How many parts are affected? (percentage of the lot/production)

When did the issue appear?

When has it been realized?

Are there still good parts which have been produced in the same process?

Are there any changes of production parameters that can be correlated to the failure?

Are there any unusual circumstances around the production line that might have influenced the issue? (e.g. construction works, repair, cleaning procedures)

Are there any (proven) correlations to certain lots, production shifts, material lots, production tools and so on?

Please note: You can never enquire too much but always too little. Be curious and vigilant during the whole data acquisition process. Each piece of information can be very precious, when it comes to the data evaluation of the analyses. The history of a sample also includes the exact circumstances of the sampling. Interviews of the people on the site that deal with that kind of product every day and know the machines very well are a versatile tool.

2.2Inspection

2.2.1Macroscopic inspection

The next step of the analytical process is a personal, visual inspection of the samples. The human visual sense is very sensitive and sometimes realizes details that are hard to measure by machines. It is sometimes quite frustrating to admit that e.g. discolourations are clearly visible but often cannot be detected even by the most sensitive instruments. On the other hand, there are people who call themselves experts and believe that they can classify a failure type just by macroscopic visual inspection. This must be doubted because e.g. a paint bubble of 20 μm diameter in a primer layer of a multicoat system may appear as a crater or a speck in the topcoat upon visual inspection and this is impossible to distinguish without instrumental assistance.

But the watchful eye can find out very important details about the samples which can be precious, when it comes to the evaluation of the analytical data:

Do the samples comply with the targeted goal of the analyses and the design of experiment?

How does the failure appear?

Are the failure spots (e.g. paint specks) randomly distributed or located in specific areas?

Are there any optical differences between failed parts and sound parts?

Does the sample exhibit further unusual features which might hint at application failures?

How many failure spots are noticeable?

Are all visible failure spots similar or are there different failure types on the same part?

Of course, this list is not comprehensive. Each individual issue requires a customized set of questions. It is good practice that the person who plans the final analytical approach (see Part II Chapter 2.3 “Design of experiment”) should have seen and thoroughly inspected the samples himself before. Please note that touching, wiping, rinsing or other influences on the samples must be avoided during this first inspection, because it can ruin the target area for the analysis.

2.2.2Microscopic inspection

Figure II.5: Microscopic inspection by a computer microscope

For the first inspection it is very helpful to use a simple mobile microscope (see Figure II.5). These low-cost computer microscopes are easy to transport, easy to use and often deliver surprisingly good pictures that help to improve the sample documentation and support the design of experiment for the next more sophisticated analytical steps. Especially, it is essential when it comes to field investigations at a costumer site or in a production plant. The main task for this inspection is to distinguish easy-to-solve issues from more complex problems. If (for example) a paint chip is sampled from a steel object (machine, steel structure) and the backside of this paint chip exhibits microscopic traces of steel dust or if there are suspicious features of the surface underneath the detached paint like grinding or scratching marks that might hint at a sample pretreatment or drying residues that can point at insufficient cleaning. This information can narrow the scope of the analyses which have to be planned. A paint failure in a multilayer coating system can appear as a bubble of the clear coat upon first microscopic inspection but, in fact, is caused by voids or cracks of the base-material.

This first tool can help to gather more information about the failure but should not be overestimated on the other hand. If e.g. a paint chip disbonds from a clear primer layer because the primer has not been cured sufficiently, the backside of the paint under first inspection with a simple microscope, might exhibit only a very thin layer of the defective primer which is not visible on microscopic inspection. So, this might lead to the wrong decision concerning the path the analysis has to go. Only a surface infrared spectrum of the backside of the paint chip can reveal the true cause of the failure for this specific example.

The first microscopic inspection does not replace a thorough laboratory investigation but can help to avoid wrong approaches to the issue.

2.3Informed guess

Once the initial data of the first inspection and the process parameters have been collected, there should be an informed guess about possible root causes. An informed (or educated guess) must be distinguished from wild speculations which are the result of missing knowledge. The informed guess together with information from interviews directly lead to the design of experiment (DOE) and “prior planning prevents poor performance”.

In fact, the design of experiment (DOE) is a key feature of a successful problem solution. If the DOE is poor, the results lead to the wrong direction.

The DOE exactly defines the type and amount of analyses to be performed and the expected results. So, at the beginning of a DOE there is the educated guess or let us say the theory what could have happened. To evaluate this question, it is good to have background knowledge about paint technology, paint chemistry and an overview about the analytical methods and their limitations. This limits the group of people that are meant to do the DOE due to their education. Planning an error analysis starts with the question: How do I start? Unfortunately, there is no standard procedure in the sense of:

Compare it to the hiking trip onto a mountain. You would want to take the shortest path with less obstacles and hopefully no deviations. At the beginning of the trail a few paths lie in front of you. But how to decide which path is the most promising? When it comes to failure analysis, it is good to keep in mind that coatings fail because something went wrong. You may want to start with an Ishikawa diagram [1] or a FMEA [2] to sort the influences on the coating quality to visualize the possible weak spots in the process. The author himself is not convinced that this approach is the best way to deal with the issue, because these methods operate with probabilities and it has been shown very often that the most improbable root cause has been the key issue of a failure event. Therefore, it is not good advice to rule out one cause just because there is low probability for it. The experience shows that one should beware of the mental mistake of believing exclusively in monocausal connections according to the pattern “if A, then B”. In reality, it is often the coincidence of several factors that influence each other and only in a certain combination lead to a coating defect.

For example: The author was called to help in a coating crater issue that appeared erratically with a certain type of polymer parts whereas other parts coated on the same line have not been affected. Following the most probable assumption based on these observations the polymer quality, and the quality of the paint very soon came into the focus. It was argued that there are parts which can be coated without or with a minimum of failures on the same line which seemed to rule out the paint shop as a possible source.