A better understanding of your coating stacks and their behavior in the desired application is a crucial point for the optimization of complex systems. To improve the understanding of the system usually a lot of tests are performed, which are designed to be as close as possible to the application. A better performance in the tests correlates to a better performance in the application.

With the help of our models and software packages it’s possible to improve this process. The idea is to transform as much gathered knowledge as possible to a digital model. With such a model you will not only know how good the system performed in a specific test. You can use the data and compare the test results to find the initial reason for a certain failure mode.

What is needed for such a digital model, which we call digital twin

You need to know the material parameters for each coating and the substrate. The neccessary material parameters are the Young’s modulus, the Poisson’s ratio and layer thickness. In addition the critical values like yield and tensile strength are needed.

How to get the material data

A good way to get the Young’s modulus values is to perform indentation measurements. These are relatively simple experiments in which a normal load is applied and the indentation depth is measured. These information allow to evaluate the Young’s modulus by using the Oliver & Pharr method. This method is built into the measurement device software, but it has limitations. It only gives one result for the complete system not taking the real structure into account. If the indentation depth is very low compared to the layer thickness (<10%), these results can be used as the results for coating in the most cases. With our extension called Oliver & Pharr for Coatings the analyzis is possible for every coated system. It takes the material structure into account and so it’s possible to get the Young’s modulus and Yield strength for each part of the system.

How to use this data

Knowing the material parameters helps to analyze more complex tests like the scratch test. In such tests a lateral load is added and so a great variety of contact conditions can be created to reflect the desired application much better. With the Scratch Stress Analyzer module such tests can be analyzed and the stress strain field or each position during can be evaluated. This helps to correlate the observed failure mechanism to a certain field value, e.g. the von Mises stress exceeded the yield strength which indicates the start of plastic flow. So it’s possible to detect the initial reason for a system failure and add the critical values to the digital twin.

Why digital

Having the digital twin allows to perform simulations of real tests in the computer without performing them. This can save time and money and helps to narrow down the search path during the optimization process. Also you can easily transform your system to a new application, by performing calculations with test parameters which represent another application, it’s possible to check how good the coating system performs. And you can play around with some material combinations or material parameters to find out how the system needs to be changed to perform better in a new application. So you can speed up the optimization time a lot by choosing the most promissing system for a different application using simulations and the digital twins of your coated samples.

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