Design of Experiments for Coatings Hardcover

Design of Experiments for Coatings Hardcover

Design of Experiments for Coatings
In order to efficiently develop and improve coatings formulations, it is essential to analyse the several factors affecting their properties. For this purpose, Albert Rössler has compiled a comprehensive overview of the statistical approach of design of experiments (DoE), pointing out its effects and benefits for coatings development.

Based on real-world applications in coatings formulation, he shows that statistics don’ t have to be that dry and difficult mathematics. Essential for everyone who wants to dive into the topic quickly and start using DoE straight away.

Contents of Design of Experiments for Coatings

1 Design of experiments – systematic mania?
1.1 Design of experiments as part of the challenges and criteria of success in modern R&D
1.2 A typical experiment in coatings formulation
1.3 Factors, Levels, etc. – some vocabulary at the beginning
1.4 Classical design of experiments and the limitations
1.4.1 Conventional methods – more diversity is not possible
1.4.2 Limits in case of the classical approach Number of experiments in case of many factors Non-linear effects and domain dependence Universality of the statements. You desire, we play – multiple responses The gain of knowledge and new information is too slow
1.5 Design of experiments – what’s that?
1.5.1 Design, factors and effects.
1.5.2 Interactions
1.6 Where is the statistics?
1.7 Models – pictures of reality
1.8 Overview, possibilities, benefits and limits
1.9 A brief history of statistical design of experiments
1.10 References
2 Planning is essential – a lot helps a lot
2.1 General principles for setting up a DoE investigation
2.1.1 Strategy of experimentation and guidelines for pre-experimental planning
2.1.2 Overcome experimental errors – identical replication.
2.1.3 How to overcome trends – randomization and arrangement in blocks.
2.1.4 Normalization, centring, orthogonal design
2.1.5 Not realizable, irregular combinations – the experimental region
2.2 Factorial designs – the heart of DoE
2.2.1 Two levels, two factors – 22-design
2.2.2 Two levels, three factors – 23-design
2.2.3 The general design with two levels – 2k-design
2.2.4 Factorial designs with centre-points
2.2.5 Blocking with factorial designs
2.3 Fractional factorial designs – to separate the wheat from the chaff
2.3.1 Basic principle of the reduction – confounding
2.3.2 Blocking – perfect suited for the 24-1-design
2.3.3 Types of fractional factorial designs
2.3.4 Plackett-Burmann designs.
2.3.5 DoE in colour measurement of a red-metallic base coat –
2 6-1-fractional factorial design
2.4 Non-linear effect designs
2.4.1 Central composite designs.
2.4.2 Three- and higher level designs
2.4.3 Mixed designs
2.4.4 Box-Behnken designs
2.4.5 D-optimal designs – egg-laying wool-milk-sow
2.5 Mixture design – a huge area
2.6 Qualitative classification
2.7 References
3 Number-crunching – nothing ventured, nothing gained in data analysis
3.1 Evaluation of raw data
3.1.1 Transformation
3.1.2 Outliers
3.2 Confidence intervals – where are the limits?
3.3 Regression – the best model
3.3.1 Basic principles
3.3.2 Confidence intervals for the model parameters
3.3.3 Basic principles and standard assumptions for regression analysis
3.4 Residual diagnostic – what does the deviations mean?.
3.5 Analysis of variance – how certain we can feel?
3.5.1 Introduction
3.5.2 Example: Colour measurement of a base coat – ANOVA.
3.6 References
4 Parametric optimization and sensitivity analysis – finding a needle in the haystack
4.1 Strategies for optimization – how we can do it better
4.1.1 Method of the steepest ascent/descent
4.1.2 Box-Wilson's method
4.1.3 EVOP-Method (evolutionary operations)
4.1.4 Simplex-method
4.1.5 Further optimization methods
4.2 Multiple responses
Example: Multiple optimization of blocking and film formation in a clear coat
Example: Optimization of an indoor paint
4.3.1 Qualitative analysis of the response surface
Example: Disturbance in levelling of a pigmented base coat
4.3.2 Quantitative analysis of the regression model
4.3.3 Taguchi-method
Example: Micro foam in a thick-coat glaze finish
4.4 References
5 DoE-Software – do not develop the wheel once more
Autonomous commercial software-packages for DoE:
Statistic packages
EXCEL-based Software
Appendix 1 – Precision, trueness and accuracy
Appendix 2 – Location and spread parameters
Example: pH-value of a lime paint
Example: pH-value of lime paints
Appendix 3 – Normal distribution
Example: pH-value of a lime paint
Appendix 4 – Confidence intervals
Example: pH-value of lime paints – continuation
Appendix 5 – Hypothesis, tests and conclusions – statistical tests
Example: Picking mushrooms.
Example: Comparison of two standard deviations
Example: ANOVA – comparison of two square sums
Appendix 6 – Three-component diagrams
Appendix 7 – Linear regression
Example: Estimation of the glass transition temperature via DSC
Appendix 8 – Failure mode and effect analysis, FMEA
Appendix 9 – General references

Design of Experiments for Coatings Hardcover
by Albert Roessler (Author)

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