The optimization of plant parameters in powder coating is still done traditionally - this means that elaborate tests as well as experienced employees are necessary to achieve good results in a manageable time. But even today, experienced employees with the appropriate experience and skills for optimizing plant parameters are rare. In addition, the human brain cannot analytically consider and evaluate the many parameters that affect a coating process and their mutual interactions. However, there are, of course, people who can intuitively arrive at the right diagnosis and steer the process back onto the right track, even in situations where the process has become muddled, based on their many years of experience. But such powder whisperers are exceedingly rare.
Two years ago, the startup coatingAI stepped up to develop a software solution that uses intelligent algorithms to perform comprehensive parameter optimization in powder coating. The AI experts have identified around 15 parameters that interact with each other and have a significant impact on coating quality and homogeneity of layer thicknesses. "It is possible for an experienced coater to achieve an acceptable result in many cases, but in most cases this has little to do with an actual optimum," Marlon Bondrini, one of the managing directors and founders of coatingAI, describes his experience. "That is why the optimization of a powder coating is an ideal field of application for artificial intelligence. This is able to clearly identify the nonlinear relationships between all the important parameters and then derive the parameters of the system coating."
An initial tool for optimizing conveyor speed and gun stroke is already available on the market for coaters. The test results so far suggest that quite a few difficulties in the homogeneity of the coating thicknesses of automatic systems can be eliminated by such optimization alone. However, the complex optimization of all plant parameters relevant to powder coating is also already well advanced and has already passed its acid test on flat parts in the pilot plants and laboratories of well-known application manufacturers.
Coding AI has thus shown that its goal of developing comprehensive and practical parameter optimization using intelligent algorithms is no fantasy. On the contrary, the practically conducted tests make it clear that the technical/scientific approach is viable.
Learn more about this AI solution for powder coating in the January-February issue of Surface Technology magazine or as a premium subscriber right here online.