Intelligensys Formulating Knowledge - Melamine Formaldehyde Systems for automotive clearcoats

Developing better products faster

Automotive Clearcoats

Automotive clearcoats are generally the finishing coating on a car, and they need to be hard (to minimize scratching when the car is washed), glossy, and weather-resistant. Frequently they comprise a binder resin (such as a polyester or acrylic) with a melamine-formaldehyde crosslinker, although of course other ingredients are also added.

Here, we show how data mining, modeling, optimization and simulation can be used to understand this complex system.

In the first instance, we used neurofuzzy logic, the technology in the FormRules program, to find out the key relationships in three different data sets, taken from published data (1-3). Customers with a password can find all the data sets here, in an Excel 'book'. The full results of our study can be found here.

Of course, the objective of many formulators is to optimize a coating to obtain specific values for properties like hardness and gloss. So, we have also used INForm's modelling and optimization capabilities to look at the best combinations of ingredients, and the best processing conditions, to find hard coatings. These used the same data sets as the FormRules study. Our full results can be found here.

Simulation helps to understand how changing the relative amounts of materials can affect the crosslinking, and hence end-use properties. On this site, you will find two case studies. One looks specifically at systems where only melamine and formaldehyde are present, and shows both how the crosslinking resin is formed, and how the effect of changing the M:F ratio affects crosslinking. This uses DryAdd, and customers with a password can access the data set here. See the full results here.

It is simple (using DryAdd's simulation of polymerization and crosslinking) to add additional monomers. So, the second note shows what happens when hexamethylolmethyl melamine (HMMM) reacts with a polyester, in a model clearcoat. The DryAdd data set can be found here - you will need a 'customer password' to access it.

All these approaches gave useful information. The data mining approach showed the rules that govern the relationships between the measured properties and the ingredients and processing conditions. Modelling and optimizaton allowed good formulations to be suggested 'in silico'. Finally, simulation showed how varying the relative amounts of the key ingredients (binder resin and crosslinker) affected the crosslink density and, consequently, properties like hardness and Tg.

(1) K J H Kruithof and H J W van den Haak, A study of structure-properties relationships in automotive clearcoat binders by statistically designed experiments, J Coatings Technology 62 (1990) 47-52

(2) L Tusar, M Tusar and N Leskovsek, A comparative study of polynomial and neural network modelling for the optimization of clear coat formulations, Surface Coatings International (1995) 427-434

(3) R H Myers and D C Montgomery, Response Surface Methodology: Process and Product Optimization Using Designed Experiments, p 712, John Wiley & Sons, New York (2002)