Rubber compounds are produced in a discontinuously working internal mixer with the addition of components such as rubber, fillers, plasticizers, and chemicals. In order to ensure a high quality, mixing instructions must be developed for each recipe. Optimal process settings are currently determined iteratively based on expert knowledge. As the scale‐up from lab to production machines is not possible today, these process developments take place on production machines, which is limited in terms of time, costs, and material. Therefore, the aim is to predict and subsequently optimize the mixing process depending on the recipe and process parameters. In order to develop initial approaches for describing the entire mixing process, mathematical models are being developed using symbolic regression to describe the ram path and torque as well as the mixing quality based on incorporation, dispersion, and distribution. Overall, sufficient accuracy can be achieved for predicting the ram path and torque. The following optimization using evolutionary algorithms enables a reduction in energy input up to 40%.
Bardehle et al. (Thu,) studied this question.