Paint and Coating EquipmentTesting, Monitoring &Tools

Why Tech Matters for R&D in the Coatings Industry

Life is set to change for companies in the coatings industry, especially in their R&D departments. It’s such a hot topic that Merit van de Lee of RheoCube and Ulf Stalmach of ORONTEC took a deeper look at some of the more pertinent issues. They looked at the tsunami of changes on the horizon for coatings companies and how technology can help them rise to the challenges ahead. Read below for some great insights that came out of that conversation.

Where do you see R&D in the next decade or so?

If I was to look into the future, I see transparent labs, working off shared, open data, which is used to drive deep analysis and powerful simulations. I picture the model paint factory, focused on using data to supply the end user as best they can. This is more of a necessity than a dream actually. Up to now, only two metrics have been key in defining a product  quality increases and price decreases. That, however, is set to change dramatically. For one, sustainability scores are being entered into legislation, which will affect how chemical supplies are purchased. No matter where materials are sourced from, the EU, Asia, or elsewhere, there will be a regulatory impact on any choices made.

To meet the needs of tomorrow’s market, the use of raw materials is going to change, but production methods will too. And that’s before we get to new product applications, which must be delivered with minimal environmental impact. A quick drying paint, for example, will need to have the right additives, but not consume too much energy to give it that property. At the end of all of this, we still need to maintain the right price and quality.

So, R&D has gone from considering two factors  price and quality  to an entire matrix of variables. With most lab technicians struggling to keep up with current research requirements, it’s hard to see how they can take on this extra work. Trial-and-error over six dimensions or more just won’t work. A proactive approach and more planning is needed to handle this increased complexity.

Can tools like simulations help?

Coatings companies can get a competitive edge by looking beyond their own product, company or even community. They can learn a lot from looking at other industries, or seeking points of improvement along the value chain. Simulations support this new level of exploration. It becomes easier to predict the results of trying new things, be imaginative, and think outside the box.

Innovations aside, simulations also make it easier to get the product right in the first place. Without a deep understanding of their materials, coatings manufacturers are often working blind. They know, for example, that a binder is generally used in formulations. To counteract certain phenomena in the mixture, additives are needed, then defoamer, and perhaps a thickener for viscosity. With no real visualization of how the basic ingredients interact, a producer could add 10 to 15 ingredients to get the right property and applications for a product.

But what if we simulated the interactions between the initial set of ingredients? Rather than adding new substances to compensate for effects like foaming, it’s easier to tweak a smaller amount of ingredients to get the mix right. Instead of fixing problems, we could better understand what’s going on in the original batch of ingredients. This will undoubtedly save on costs, and drastically reduce the number of trial-and-error experiments.

Shared data powers the simulations that provide these insights. This means, as I have mentioned before, freeing data from the various silos it's currently trapped in, both company-wide and industry-wide. There is so much to be learned from cross-pollinating different sources of information. Once we have gotten a handle on sharing all of that data, simulations help by showing us what’s happening in our formulations.

What other digital tools do you see in the lab of the future?

Smart production is very interesting, especially things like voice or gesture-controlled data entry. For example, being able to test paints without having to constantly remove a large pair of gloves to input results. It’s also much faster to scan ingredient barcodes than input data on substances. There’s less room for human error too, so experiments benefit from more complete information. Simulations in particular take on a new meaning when they are powered with more accurate data input. At some point we will make the move to predictive analysis, using AI. Of course, we are not quite there yet, but we can at least start.

Clean, correct information makes quality control so much easier, and there’s more room to tweak formulations and try new things. The end result will be smarter research cycles and better results. Scientists win too with new, exciting insights, and much less tedious, frustrating trial-and-error work. These tools make their job more interesting, even more fun. VR is another interesting concept, imagine being able to experience what’s going on in your formula, by being virtually immersed in it!

Can these technologies really add that much value?

The problem with the modus operandi is that it does not put the end customer at the center of what’s going on in R&D. When information is needed, manufacturers generally turn to raw material suppliers. A supplier’s objective, however, is not really aligned with creating the best possible scenario for the end product. They need to sell more of their own product, whether or not a competing raw material makes more sense. Even if that were not the case, their knowledge of other products would be too limited to offer sound advice.

Shared, objective data is the solution and new technologies are crucial in using it. If we take steps to implement those technologies now, they will complement the current R&D setup. Rather than trying to force the current model to fit a new market, we can get comfortable with the tools needed to future-proof R&D and indeed our businesses.

In a rapidly changing market, it all comes down to efficiency and risk management. Change is not always easy but digital transformations don’t have to happen overnight. A simulation solution will strengthen current R&D practices, while paving the way for an increasingly digital future.

What’s crucial is for companies to understand the relevant tech, become familiar with it and see where it can bring them going forward. The first step, and indeed the most important one, is to get started.