Optimization of Fluid Coating
- Published: October 17, 2014, By Mark Miller
If you'd like to hear from Mark Miller's own lips rather than read his blog post titled, "Mark's Coating Matters | Optimization of Fluid Coating," click on his podcast below:
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Developing a product in the fluid coating industries requires determination of raw materials to provide for the differentiated features of the product, but equally important is the process development associated with turning these raw materials into an effectively and efficiently produced item. Whether you are looking to develop an improved tape, label, battery, or optical film, understanding and optimizing the coating process is important. The larger this coating window the more latitude there is for success.
But what should you concentrate on in the development of the process? Higher line speed? Decreased coat weight variation crossweb? Reduced production costs? These are all reasonable goals, but they need to be tempered with a balanced approach to developing the coating window for process efficiency, product quality, and production costs. The question is how.
One approach is to look more at reducing variation than to optimize one variable. The reason for this approach is that optimization of one variable may lead to an improvement in one area, but a reduction of quality in another. An example would be to increase line speed until the coating is unstable crossweb leading to more product rejects.
Coating processes have many functions that play on process control and product quality. These include: Pump rate, line speed, temperature variation, coating head to web gap, and coating thickness. To reduce the variation in a multifactorial system, statistics can be your friend. In the variable set presented, we have 5 variables that can be studied with a simplified experimental design that can take 16 trial points. This provides a full understanding of the entire set of variables, including interactions between the variables, without having to take multiple days and many hours on the production line.
When you study the effects of these variables, the result is a series of two-dimensional graphs of what works and what doesn’t. These two-dimensional graphs of the interactions between the 5 variables, when combined, provide the process engineer what is referred to as the coating window. The graphs provide the cutoffs for success or failure for a given product, with specified raw materials, running on specific equipment. The success/failure boundary lines are typically associated with coating defects recognized at the given process condition. The goal is to place the process parameters (or operating recipe) in the center of the coating window. This allows for the most freedom of operation, in case one of the variables shifts during production. Remember that the variables of interest are moving in the experimental design, while everything else is held constant. If raw materials or equipment change, the coating window can open up or narrow.
As an example, if you ran the designed experiment and looked at the resulting graph of pump rate versus line speed, you would have success in a region of reasonable line speed and reasonable pump rate. Failure of air entrainment would occur as line speed increased and/or pump rate decreased. Failure of adequate fluid flow would appear at a critical pump rate, and overcoating phenomena would occur at an increased pump rate at low line speeds. This window of coatability provides you with a two-dimensional picture that can be combined with the other 2 factor interaction graphs to provide you with an optimized coating process that stabilizes the product within the current equipment parameters.
What do you do if the current process coating window is not adequate for either the quality or manufacturability of the product? Equipment improvements would be necessary. This data set, run in 16 trial points, provides the starting point for data collection in an acquisition for equipment process. So, whether the goal is to determine the optimization area of an existing product, or understand the limitations of the existing equipment, applying statistical analysis can quickly provide a window on the situation.
In the end, we should be shooting for a stable coating process that considers multiple performance factors and makes the product manufacturable, effective, efficient, and of the highest quality. This balanced approach to process design will provide the best return on investment.
